Are Seasoned Equity Offerings bad news?

Aarhus School of Business and Social Sciences Aarhus University May 2015 Are Seasoned Equity Offerings bad news? A research on European Seasoned Equity Offerings and the disclosed use of issue proceeds from 2000 to 2010 MSc Finance & International Business Master thesis Authors: Jesper Dissing (403148) Kristian Rasmussen (522165) Academic advisor: Jan Bartholdy Characters: 196.900 (excl. blanks)

Abstract This thesis examines the effect of seasoned equity offerings, specifically the abnormal stock returns around the announcement, change in operating performance subsequent to the issue and actual use of issue proceeds. The thesis contributes to the existing literature by combining the short- and long-term view in a European setting and taking into account the announced use of issue proceeds. We use a sample of 342 seasoned equity offerings conducted between 2000 and 2010 from 15 European countries, divided into three subsamples based on their announced intended use of issue proceeds as well as two comparative benchmark samples. We use the event study methodology based on abnormal stock returns in a three, seven and 21-day event-window for the short-term analysis, Difference-In-Difference (DID) regression in a three years ex-ante and ex-post event-window and OLS regressions on our study on actual use of issue proceeds. We find that, in general, firms that issue seasoned equity experience significant abnormal negative stock returns around the announcement and experience a significant decline in operating performance relative to peers. We do not find any abnormal stock returns or any significant change in operating performance relative to peers for the 81 firms that issue equity with the purpose of repaying debt. For the 107 firms who intend to use the issue proceeds for general corporate purpose we find significant abnormal negative stock returns around the announcement, but do not find a significant decline in operating performance relative to peers. Lastly, we find significant negative abnormal stock returns around the announcement and a significant decline in operating performance relative to peers, for the 154 firms who announce specific investments as the intended purpose. Our analysis on use of issue proceeds indicates that the firms in fact use the issue proceeds as announced. Most noticeable are the strong results for debt repayment, by firms who announce this as their primary purpose.

Table of contents 1 INTRODUCTION... 1 1.1 RESEARCH QUESTION... 2 1.2 THESIS APPROACH... 2 1.3 DELIMITATION... 3 1.4 DATA SOURCES... 4 1.5 THESIS STRUCTURE... 4 2 THEORY OVERVIEW AND EMPIRICAL FINDINGS... 5 2.1 SEASONED EQUITY OFFERINGS... 5 2.2 THEORY OVERVIEW... 6 2.2.1 Capital structure... 6 2.2.2 Adverse selection... 8 2.2.3 Signaling... 10 2.2.4 Agency theory... 12 2.2.5 Summary... 13 2.3 LITERATURE REVIEW... 14 2.3.1 Studies on market reaction... 14 2.3.2 Studies on market reaction with use of issue proceeds... 16 2.3.3 Studies on operational performance... 18 2.3.4 Studies on operational performance with use of issue proceeds... 21 2.3.5 How are the issue proceeds used... 22 2.3.6 Conclusion based on previous research... 23 3 HYPOTHESES... 24 4 RESEARCH METHODOLOGY... 25 4.1 SHORT-TERM EVENT STUDY... 26 4.1.1 Efficient market hypothesis... 27 4.1.2 Event window and estimation period... 27 4.1.3 Choice of model... 29 4.1.4 Abnormal returns... 29 4.1.5 Statistical test... 31 4.1.6 Mean comparison test... 33 4.1.7 Cross-sectional regression... 34 4.1.8 Challenges of the short-term event study... 34 4.2 PERFORMANCE STUDY... 35 4.2.1 Difference-In-Difference method... 35 4.2.2 Autocorrelation... 37 4.2.3 Parallel trend assumption... 38 4.2.4 Selection of operating performance... 38 4.2.5 Event window... 41 4.2.6 Choice of benchmark... 42 4.2.7 Mean comparison test... 42 4.2.8 Challenges of long-term performance study... 42 4.3 USE OF PROCEEDS STUDY... 43 5 DATA... 45 5.1 SAMPLE COLLECTION... 45 5.2 USE OF ISSUE PROCEEDS GROUPING... 47 5.3 MATCHING PROCEDURE... 48 5.3.1 Matching algorithm... 48 5.3.2 Winsorizing... 50 5.4 COMMENTS ON FINAL DATA SET... 50

6 EMPIRICAL RESULTS... 51 6.1 SAMPLE CHARACTERISTICS... 51 6.2 SHORT-TERM RESULTS... 54 6.2.1 Means comparison of motives... 59 6.2.2 Cross-sectional analysis of abnormal returns... 60 6.2.3 Summary of short-term findings... 64 6.3 LONG-TERM OPERATIONAL PERFORMANCE RESULTS... 64 6.3.1 Pre and post-seo performance descriptive... 65 6.3.2 Operational performance of SEO firms... 71 6.3.3 Operational performance of the subsamples... 74 6.3.4 Quantile regression results... 78 6.3.5 Pairwise comparison of motives... 78 6.3.6 Summary of long-term findings... 79 6.4 ACTUAL USE OF ISSUE PROCEEDS... 80 6.4.1 Actual use of issue proceeds summary... 86 7 CONCLUSION... 87 8 CRITICAL DISCUSSION... 89 BIBLIOGRAPHY... 91

Table of appendices APPENDIX A - OVERVIEW OF PREVIOUS FINDINGS... 96 APPENDIX B - SAMPLE COLLECTION PROCEDURE... 97 APPENDIX C - MATCH COMPARISON PLOT... 98 APPENDIX D - DESCRIPTION OF NACE CODES... 99 APPENDIX E - SHORT TERM: SHAPIRO-WILK TEST FOR NORMALITY... 99 APPENDIX F - LONG TERM: SHAPIRO-WILK TEST FOR NORMALITY... 99 APPENDIX G - QUANTILE REGRESSIONS... 100 Table of figures FIGURE 1 - ESTIMATION PERIOD AND EVENT WINDOW (THREE-DAY EVENT WINDOW)... 28 FIGURE 2 - SAMPLE OVERVIEW... 51 FIGURE 3 - CUMULATIVE ABNORMAL RETURNS [-10;+10]... 55 Table of tables TABLE 1 - DIFFERENCE-IN-DIFFERENCE TABLE... 36 TABLE 2 - OFFER SIZE & OFFER SIZE SCALED BY ASSETS... 52 TABLE 3 - SEO S DIVIDED BY COUNTRY AND INDUSTRY... 53 TABLE 4 - DESCRIPTIVE STATISTICS... 54 TABLE 5 - SHORT-TERM TEST STATISTICS: FULL SAMPLE... 56 TABLE 6 - SHORT-TERM TEST STATISTICS: DEBT REPAYMENT... 57 TABLE 7 - SHORT-TERM TEST STATISTICS: GENERAL CORPORATE PURPOSE... 58 TABLE 8 - SHORT-TERM TEST STATISTICS: INVESTMENT... 58 TABLE 9 - DIFFERENCE BETWEEN SUBSAMPLES... 59 TABLE 10 - PAIRWISE DIFFERENCE BETWEEN SUBSAMPLES... 60 TABLE 11 - SHORT-TERM CROSS-SECTIONAL REGRESSION... 62 TABLE 12 - DESCRIPTIVE STATISTICS... 65 TABLE 13 - DID TABLE: PRE-SEO CHARACTERISTICS... 66 TABLE 14 - DID TABLE: POST-SEO CHARACTERISTICS AND DID ESTIMATOR... 69 TABLE 15 - LONG-TERM DID REGRESSION: FULL SAMPLE... 72 TABLE 16 - LONG-TERM DID REGRESSION: DEBT REPAYMENT... 74 TABLE 17 - LONG-TERM DID REGRESSION: GENERAL CORPORATE PURPOSE... 76 TABLE 18 - LONG-TERM DID REGRESSION: INVESTMENT... 77 TABLE 19 - PAIRWISE COMPARISON BETWEEN SUBSAMPLES... 79 TABLE 20 - ACTUAL USE OF ISSUE PROCEEDS: GENERAL CORPORATE PURPOSE... 83 TABLE 21 - ACTUAL USE OF ISSUE PROCEEDS: DEBT REPAYMENT... 84 TABLE 22 - ACTUAL USE OF ISSUE PROCEEDS: INVESTMENT... 85

List of abbreviations ABT - Arbitrage Pricing Theory AIM - Alternative Investment Market C&STI - Cash & Short-term investments CA - Current Assets CAPM - Capital Asset Pricing Model CAR - Cumulative abnormal return CFIA - Cash flow from investing activities CL - Current Liabilities D - Debt DEB - A firm that states that the issue proceeds will be used for repaying debt DID - Difference-In-Difference EBIT - Operating income before interest and tax EBITDA - Operating income before interest, taxes, depreciation and amortization Eq - Equity EUR - Euro FCF - Free cash flow GEN - A firm that does not precisely state what the issue proceeds will be used for INV - A firm that states that the issue proceeds will be used for investment purposes LN - Natural log M&A - Mergers & Acquisitions NPV - Net Present Value OLS - Ordinary Least Square OROA - Operating return on assets OROS - Operating return on sales PP&E - Property, plant & equipment R&D - Research & Development SEO - Seasoned equity offering TA - Total Assets Note: We use comma (,) as decimal separator, and punctuation (.) as thousands separator

1 Introduction A so-called investment expert on a worldwide TV-station recently made a bold statement that, this logic of seeing a seasoned equity offering as bad news, is an old fashioned way of viewing the market (Website 1: CNBC, 2015). Could this non-academic investment expert really be right? Empirically, everything speaks against this statement, as the extensive research by academics seems to agree that a seasoned equity offering is in fact bad news. The general consensus on the subject is that the offering entails a negative market reaction, in the shape of a significant decline in stock prices, and a subsequent decline in operating performance in the years following. However, the investment expert might still have a point, as he suggests that the ultimate use of the issue proceeds might actually be beneficial for the shareholders. Thus, it could potentially depend on the use of issue proceeds whether a seasoned equity offering is bad news. The academics have already turned their attention to this subject, however all research seems to heavily focus on the market reaction while almost completely ignoring subsequent operating performance. Especially in terms of operating performance, the findings seem to be inconclusive, which leaves the question very open. Previous studies have primarily focused their researched on either the market response or operating performance, but there seems to be a lack of research linking the market reaction to the subsequent operating performance. Furthermore, most studies are quite outdated and/or based on US firms. Thus, there is a gap in the literature both in terms of EU firms and whether the market reaction actually corresponds to the future operating performance of firms issuing seasoned equity. The question then remains whether a seasoned equity offering is always bad news or whether this depends on the intended and actual use of issue proceeds. This thesis therefore examines this subject, which leads to the following research question. Page 1 of 101

1.1 Research question The aim of this study is to investigate the market response, any change in operational performance and the use of issue proceeds of firms issuing seasoned equity. This leads to the following main research question: - What are the implications of a seasoned equity offering on firm performance and how is this moderated by intended use of issue proceeds? To answer the main research question, the following three sub-questions are put forth: - What are the implications of a seasoned equity offering on stock price and how is this moderated by intended use of issue proceeds? - What are the implications of a seasoned equity offering on operating performance and how is this moderated by intended use of issue proceeds? - Are the issue proceeds used in accordance with the proclaimed purpose? 1.2 Thesis approach This thesis seeks to further explore the literature of seasoned equity offerings. This will be done through three methodological approaches. First, a short-term event study will be conducted to measure the immediate effect the SEO 1 announcement has on the issuers stock price. Secondly a long-term performance study on the basis of accounting measures will be conducted, to examine any changes in operational performance. At last we seek to explore if the firms actually use the issue proceeds in alignment with the SEO prospectus. These three methodological approaches will all be assessed on the basis of the intended use of issue proceeds, stated by the firms in their SEO prospectus. By combining these three approaches we enhance the robustness of our conclusions compared to previous literature, which primarily focus on the short- or long-term stock return or long-term operational performance. Moreover this paper will take its origin in European data, thereby contributing to the modest evidence on seasoned equity offerings in Europe, since most of the previous literature is based on US data. 1 For the remainder of the thesis SEO will be used as an abbreviation of Seasoned Equity Offering. Page 2 of 101

This paper is built up upon previous research and empirical evidence. The theoretical framework of Walker & Yost (2008) and Autore, Bray & Peterson (2009) has been used to define the motive behind the SEO, by the intended use of issue proceeds. Also the framework of MacKinlay (1997) and Barber & Lyon (1996) have contributed to the methodology for the research design as well as the execution of the short-term event study and the long-term performance study. Furthermore the relatively new model developed by Kim & Weisbach (2008) has been applied to assess the use of issue proceeds. In order to answer the above problem statement the approach to this thesis will be based on an analytical approach, which means the thesis will be approached on the basis of a positivistic paradigm. The assumption behind this paradigm is that we try to see the reality as objective as possible, where we only focus on the facts and causality. Therefore this paper should be independent from its writer, which means that another writer should draw about the same conclusions as we do in this paper. 1.3 Delimitation Most of the delimitations are outlined throughout the thesis. However a few fundamental delimitations are laid down in this section. The geographical boundary of this thesis is limited to the following European countries: Austria, Belgium, Denmark, Finland, France, Germany, Great Britain, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain, Sweden and Switzerland. 2 This thesis will apply a variety of theories as described in section 2.2, in order to explain the market reaction and subsequent firm performance after a SEO. The applied theories will not be tested empirically, nor will the previous research be applied to test if the theories hold in practice. Instead the theories are assumed to be well specified and tested, which is why they will only be applied in order to describe the market reaction and operating performance after a SEO. 2 These countries also comprise the Morgan Stanley Capital International Europe Index. Page 3 of 101

This thesis is mainly limited by availability of data. Due to requirements of sufficient ex-ante and ex-post data, focus is therefore exclusively on seasoned equity offerings conducted in the period from 2000 to 2010, both years included. We exclude companies in the financial and utility sector in our analysis. These companies operate under strict regulations that are fundamental different from the remainder of companies. This is common practice in researches on seasoned equity offerings. 1.4 Data sources The literature in this thesis will be based on primary sources, which are well known published articles. These articles have been published in respected international journals, which include a mixture of previous research on seasoned equity offerings as well as more theoretical models. The primary reason for choosing peer-reviewed articles is the validity. Besides articles from well-respected journals, this thesis relies heavily on stock as well as accounting data. As a primary source for the stock data, Datastream has been used. For the accounting data and additional firm information Bloomberg has been used, combined with robustness checks of the data through Orbis, as well as the annual reports of the firms. The data sources are considered reliable, and furthermore due to the robustness check of the accounting data, our data is suggested to be of good quality. Nevertheless the data should still be observed from a critical point of view. All data can be found on the enclosed CD. 1.5 Thesis structure The remainder of the thesis is structured as follows. Section 2 provides an overview of the relevant theory of seasoned equity offering. Following, an overview of previous empirical findings regarding the market reaction as well as operational performance of issuing firms is presented. On the basis of the theoretical foundation and empirical findings the thesis hypotheses are presented in section 3. In section 4 we present the research methodology for the short-term study, operational performance study and study on actual use of issue proceeds. Section 5 presents the data as well as any considerations taken in the process of collecting the data. On the basis of the theory, previous empirical findings, research methodology and data, section 6 presents the empirical results divided into short-term, longterm and actual use of issue proceeds. We conclude the thesis with a conclusion in section 7 Page 4 of 101

and a critical discussion in section 8. A bibliography of applied literature and appendices are available at the back of the thesis. 2 Theory overview and empirical findings In the following section a brief introduction to SEO s in general and the possible reasons for conducting a SEO will be provided. The section also provides an overview of relevant theory in relation to market reaction and operational performance as well as previous empirical findings on the subject. The presented theory and empirical findings will form the basis of the thesis hypothesis and serve as reference in the analysis. 2.1 Seasoned equity offerings A seasoned equity offering is a way for a firm to gain outside capital through the financial markets. A SEO is defined as a new stock/equity issue, by a firm that currently is traded on a stock exchange. This means that the firm is publicly listed by previous having gone through an equity issuance through an Initial Public Offering (IPO), where the original stock was offered for the first time. When firms conduct a SEO, they are able to do so by offering two types of shares. First off they are able to offer primary shares, which are newly issued shares that are offered to public investors for the first time. The other way is through the use of secondary shares, which refer to existing shares that are being sold from existing shareholders to new shareholders, so basically the already traded shares shift hands. The main differences between these two types of offerings is that primary share offering raises new capital to the firm, whereas secondary shares do not raise new capital. Furthermore a secondary share offering is non-dilutive 3, since no new shares are issued. In a primary share offering current ownership will be diluted, because a number of new shares are issued. This thesis will only be looking at SEO s where the offering contains an amount of primary shares 4. There are several reasons why a firm might offer new equity. Generally this is described in the prospectus following the offering and is known as the Use of proceeds. This could include specific investment in R&D, funding of continuous operations, merger and acquisitions, 3 Stock dilution refers to the reduction in the ownership percentage, due to a SEO 4 Most offerings contain a mixture of primary- and secondary shares, however often with an clear overload of primary shares. Page 5 of 101

recapitalizing, repaying debt and so on. The work of Walker & Yost (2008) and Autore, Bray & Peterson (2009) have grouped the many reasons into three different categories, based on the stated use of issue proceeds. The first group is the firms that offer new equity in order to recapitalize, by repaying some of its current outstanding debt, which would result in a change of leverage 5 (DEB). The second group is the firms that issue new equity in order to use it for general corporate purposes. This group does seldom reveal any specifics about the use of proceeds, which basically means that the issue proceeds will either be added to the firms working capital, and serve as a liquidity buffer for continuous operations or that it is too costly for the firm to reveal any specifics in terms of competitive advantages (GEN). The third group is the firms that issue equity with a clear intention to fund investments through the issue proceeds, be it for M&A activities or R&D (INV). We will in this thesis follow the same classifications as Walker & Yost (2008) and Autore, Bray & Peterson (2009), in order to detect if the stated intended use of issue proceeds have an effect on the short- and long-term performance of the SEO firms. The different motives in relation to this thesis will be described further in section 5.2. 2.2 Theory overview As a theoretical framework to answer our research question a number of theories have been included. The main subjects of the theories are capital structure, adverse selection, signaling and principal-agents problems. The theories do in many ways overlap and are therefore not mutually exclusive. However, they each take its roots in different aspects of the corporate finance theories. 2.2.1 Capital structure The first to define a relation between the optimal capital structure and firm value where Modigliani & Miller (1958), who argued that a company does not increase or lower its value by changing its capital structure, assumed that there are no taxes, bankruptcy costs, agency costs or information asymmetry. The reason for that argument is that when a company changes its leverage by increasing its level of debt, the equity holders will require a higher return due to the increase in risk, thereby offsetting the effect the capital structure has on company value and vice versa (Modigliani & Miller, 1958). Since all firms in practice face 5 Debt to Equity Page 6 of 101

taxes, this thesis finds it more relevant to look at the corrected model by Modigliani & Miller (1963), where they include corporate taxes. In the corrected model they argue that there is an advantage of holding debt over equity, because debt entails a tax shield, due to the tax deductibility on the interest payments. Thus the market value of a levered firm will always be higher than an unlevered firm (Modigliani & Miller, 1963). All things equal, the market will perceive an equity issue negative since an increase in equity per se decreases leverage. If the issue proceeds will be used to repay some of the firm s outstanding debt, leverage decreases even more, which would result in a larger drop in stock price for this SEO motive. The reason is that when firms lower their leverage, they do not exploit their tax shield favorably, which leads to declining firm performance and firm value. Brennan & Schwartz (1978) have elaborated further on the model developed by Modigliani & Miller (1963), by including bankruptcy costs. They argue that with a rising amount of debt, a firm faces an increased probability of bankruptcy, which increases the cost of financial distress. Basically this means that the marginal benefit of the increase in firm value declines with rising debt levels, whereas the marginal bankruptcy cost increase. Therefore an optimal capital structure exists, which result in a trade-off between the amount of debt and equity a firm should hold in order to maximize firm value. A firm should therefore increase debt until the advantage of the higher tax shield is offset by the increase in bankruptcy costs, as higher leverage beyond this point would reduce firm value (Brennan & Schwartz, 1978). This theory is further supported by DeAngelo & Masulis (1980) who propose a similar optimal capital structure model (DeAngelo & Masulis, 1980). Assuming that an optimal capital structure exists, a SEO would always be perceived as something positive by the market, regardless of what the issue proceeds will be used for. The reason is that a SEO per se changes firm leverage, and assumed that managers act in the best interest of shareholders, the change in leverage is to the better i.e. towards the optimal capital structure. However should a firm issue new debt in combination with equity, thereby having no effect on the leverage ratio, the market value of the firm should be unaffected. The market should therefore be neutral to this action, which should not lead to any change in stock prices. This type of combination is mostly seen when firms need cash to finance investments. In the long-term, the improvements in capital structure should be reflected positively in earnings and performance in general. Page 7 of 101

2.2.2 Adverse selection It is said that the financial markets work provided that buyers and sellers have the same full information at the same time. However, in practice this is rarely the case, as asymmetric information does exist within the financial markets. On the basis of asymmetric information Akerlof (1970) specified the term adverse selection, which defines a process in which buyers and sellers do not have the same level of information. In his paper Market for lemons, he exemplifies the adverse selection problem with the used car market, where the adverse selection results in owners of good cars not wanting to put their cars on the market (Akerlof, 1970). The idea behind the theory is that only the sellers know the true quality of the used cars and buyers are not able to distinguish the good ones from the bad ones. This drives prices down, as buyers are only willing to pay for the average quality car, which results in good-quality cars being undervalued. As good-quality car owners are not willing to sell at undervalued prices, they take their cars out of the market, resulting in adverse selection. This theory is quite fundamental for many financing theories. The pecking order model of Myers & Majluf (1984) elaborates further on the adverse selection dilemma in a financing context. The basic idea behind the pecking order theory is that firms prioritize their way of financing investment projects, through a clear hierarchy. First, they will use internal financing such as internal generated cash, then they will use debt and as a last resort the firm will issue equity as a financing source. Myers & Majluf assumes that firm managers act in the interest of current shareholders, that managers know the true value of the firm s assets and that only positive NPV-projects exist. When firms finance internally there are no costs of information asymmetry, as outside financing is not needed. However when outside financing is needed firms pass some information to the public, which leads the market to revise the value of the firm and its shares. As with the Lemon problem outsiders do not know the true value of the firm, and their best guess of the firm is that it is of average quality. Since this is the public s best guess, managers of a good quality firms, refuse to issue equity, because the true value of the assets in place are higher than the market perceives. This causes the investors to revise their best guess about the quality of the firm downwards, entailing information asymmetry costs, because undervalued firms with good projects will not issue equity. This would in general mean that when the equity is underpriced, managers would rather foresee NPV-positive projects, rather than having to Page 8 of 101

issue equity. On the other hand managers would issue equity if their securities were overpriced i.e. if managers expectations of future performance were lower than the markets expectations. Overall the pecking order model predicts that a SEO would lead the market to revise their expectations of the firm value and future performance downwards. Cooney & Kalay (1993) have taken a slightly different view on the pecking order. Opposite Myers & Majluf (1984), they state that not all projects are NPV positive; arguing that the market has the expectation that the investments a firm can undertake both can be good or bad. They therefore argue that if firms do not undertake an investment, it may simply be because the project has negative NPV. Furthermore Cooney & Kalay assume that the market expects the future projects to be of average quality. This means that the announcement of an equity financing for a project could entail a positive market reaction if the project is better than the market expected and thus predicts better performance (Cooney, Jr. & Kalay, 1993). One of the ways to mitigate this adverse selection dilemma is by minimizing the asymmetric information between managers and investors. Managers are able to do so by being as informative about the use of the issue proceeds through the SEO prospects. In general firms that do not state what the issue proceeds will be used for, will experience a significant drop in stock price, since they do not minimize the information asymmetry between managers and investors. Since investors do not know what the issue proceeds will be used for, they are not able to distinguish between good or bad projects; hence they still face the adverse selection dilemma. Thus if firms do have good projects they would be interested in differentiating themselves, in order to avoid themselves from being pooled with the average firm. This means that firms with good projects would be as specific to the public as possible in the SEO prospectus. This would lead the stock market reaction to be less negative, because of the ability to positively distinguish themselves from the average firm. However not all firms with good projects may be able to be specific, for example if the costs of letting competitors in on project details are too big. These firms are therefore forced to be pooled with the average projects when issuing new equity. On the other hand firms with bad projects will never be specific about the use of issue proceeds, as informing the public about a bad project will result in a more negative reaction than if they were pooled with the average project. Firms with bad projects would therefore rather choose to be unspecific about the use of issue proceeds. Page 9 of 101

Firms issuing equity in order to repay current outstanding debt do not face the adverse selection dilemma. As they merely adjust their capital structure they should be free for the signal of bad projects, hence the market response for these firms should be less negative and performance should be unaffected (Myers & Majluf, 1984). Also Krasker (1986) has made an extension to Myers & Majluf (1984). He argues that there is a relationship between the issue size and stock price reaction. Opposite Myers & Majluf this model suggests that managers are able to decide the amount of equity raised through an issue. This means that more overvalued firms tend to issue more equity and keep the excess cash as financial slack. Therefore there is a negative correlation between stock price and issue size, which is why investors use issue size as a proxy for overvaluation and insider asymmetric information (Krasker, 1986). There are some shortcomings to the pecking order hypothesis, because in order for firms to follow the pecking order, they must have access to all financing sources. Relatively small and young companies might not have access to sufficient internal funds, nor external funds in terms of debt, since lenders might not have gained confidence in the firm. Young and small firms are therefore often forced to finance through equity issues. One might therefore suggests that the pecking order hypothesis only holds for older and larger firms, because smaller and younger companies are more likely to be financed with equity ( (Frank & Goyal, 2003) & (Diamond, 1989)). Furthermore the pecking order theory disregards the effect of taxes, agency costs, financial distress and the issuance cost of securities. 2.2.3 Signaling The pecking order hypothesis above assumes that financial slack is fixed and always inadequate for undertaking the project, thus requiring external financing. However, other theories adopt another point of view and assume that external financing only is needed when internal cash flows are insufficient. The basic idea is therefore that managers have inside information on future performance and profitability and that a financing decision therefore is a signal to the market of the firm s future cash flows. Page 10 of 101

One signaling model developed by Miller & Rock (1985), suggests that the net financing policy can be used as a signal of firm quality. They argue that when a firm pays out dividends to its investors it sends a strong positive signal of future cash flows. This model therefore suggests, the higher the pay-out ratio the stronger the signal. The opposite of giving its investors money, is asking them for money through an equity issue, the effect is therefore merely the effect of paying dividends, though with opposite sign. A SEO is therefore a negative signal of future earnings. Even though the firm state that the issue proceeds will be used for specific investments, the market should react negatively, as they argue that the firm is not able to generate enough internal funds to finance the optimal level of investment. As also argued by Krasker (1986), this statement would mean that a larger issue size indicate a larger need of cash. Again this indicates a negative association between issue size and stock price. If firms issue equity in order to repay debt, the firms should be free from the signaling effect of lack of liquidity and future declining performance as this just changes capital structure (Miller & Rock, 1985). Ross (1977) has developed another signaling theory, where he suggests that debt can be used as a signal of firm quality. He argues that the higher the amount of debt, the higher the quality of the firm. He assumes that high quality firms earn high profits, which means they easier can bear a larger amount of debt and the entailed interest payments, compared to firms of lower quality. Therefore high leverage firms can signal the level of firm quality to investors (Ross, 1977). Basically this means that if a firm issues equity and uses the proceeds to repay some of its debt, this has a negative signaling effect, which according to Ross (1977), should indicate declining profits. Firms that intend to use the issue proceeds for investments or do not state the intended use of issue proceeds, should not see a negative market reaction to the SEO announcement, or at least only a slight negative reaction since they should have used debt over equity as a financing source. Leland & Pyle s (1977) signaling theory is based on management s willingness to own and invest in the firm and any new projects. The argument is that managers are able to minimize the information asymmetry by increasing their own ownership in the firm. They base their theory on the adverse selection problem, and thus a higher level of management ownership signals good firm quality. In general this theory states that a SEO is seen as a negative signal of Page 11 of 101

future performance, since a SEO dilutes manager ownership in the firm. If managers therefore instead would take up larger equity positions when issuing equity, some of the asymmetric information between managers and investors would diminish, which would send a strong signal of the quality of the firm. In turn management can include secondary shares in the offering, which would indicate lack of confidence to future performance. Thus an inclusion of secondary shares would send a negative signal to the market in terms of future performance (Leland & Pyle, 1977). 2.2.4 Agency theory Within corporate finance, agency problems are a widespread phenomenon and in relation to equity financing the theories are well applied. In short, the agency theories explain the conflicts between principals 6 and agents 7, where problems arise due to the fact that the agents not always act in the best interest of the principals. The problem herein lies that it is difficult for the principals to control if the agents spend excessive money on perquisites or engage in excessive risky investment etc.. Therefore the agency costs rise, due to this information gap (Brealey, et al., 2009). Jensen & Meckling (1976) combined the agency theories with financing theories and developed a theory on the ownership structure of the firm. Their theory explains how the cost associated with the conflicts between managers and shareholders reduce the market value of the firm. The costs are defined as the costs associated with monitoring the manager as well as loss in firm. These costs originate from the assumption that when management ownership level decreases, the manager tends to be more inclined to fulfill his or her own needs in form of perquisites and opportunistic behavior instead of maximizing firm value, which basically leads to a lower firm value. In order to compensate for this, shareholders monitor the manager in various ways, which are associated with costs. Therefore from the shareholders perspective an equity issue will always be seen as something negative since it increases the resources available to the managers, which leads to higher cost associated with monitoring and open for more investments in value decreasing activities. 6 Investors, Shareholders, lenders etc. 7 Managers, firm executives etc. Page 12 of 101

Jensen (1986) has extended the version of Jensen & Meckling (1976), to include the availability of free cash flow. He states that if a firm has sufficient free cash flow it should use these funds as a source of financing, instead of external financing. His definition of free cash flow is cash flows in excess of that required to fund all positive NPV projects of a firm. If there is free cash flow available this should be paid out to the shareholders in order to maximize value for the shareholders. Available free cash flow entails an increase in agency costs of equity and reduces firm value. He therefore suggests that if excess free cash flow is available and a firm announces a SEO, the market would react negatively since a SEO further increases the cash availability for managers and lead to value decreasing investments (Jensen, 1986). However if a firm has no excess free cash flow to invest in a project the firms has to approach the capital markets. In this case it is up to the investors and analysts to assess the project and to value if the issue proceeds are spent in their best interest, which might lead to a nonnegative market reaction around announcement. Jensen (1986) argues that one way of reducing the agency conflict between managers and investors is through the use of debt. By using debt over equity managers are forced to payout future cash flows, which reduces resources available to the managers, which in turn reduces the consumption of perquisites and opportunistic behavior of the managers. Shareholders on the other hand cannot force managers to pay out dividends or repurchase stocks 8. Therefore if a firm issues equity with the purpose of paying back outstanding debt, this would opposite Myers & Majluf (1984) be perceived as more negative by the market, as managers are no longer obliged to pay out future cash flows, which enhances the resources available to managers and thereby increases the agency conflict. 2.2.5 Summary Basically all the theories suggest that a SEO in general entails a negative market reaction and a subsequent decline in performance. If equity issues are split into the SEO motives the theories are a bit ambiguous, though they all agree that when a firm states general corporate purpose the stock price will be revised downwards due to expected lower performance expost. If we look at firms stating investment as their intended use of issue proceeds, the theories argue that the market reaction could be both negative as well as positive. 8 Repurchase of stocks is in the finance literature seen as another way of paying dividends Page 13 of 101

Nevertheless most of the theories point in the direction of a negative market reaction and subsequent decrease in operating performance. When the issue proceeds are used for debt repayment, the theories comes with contradictory arguments on how the market reacts as well has how performance develops ex-post. The preponderance of theories though suggests that a SEO should not impact the stock price or operating performance ex-post. To give a better indication of the effect of a SEO or lack thereof, we review the previous research and report the findings in the next section. 2.3 Literature review A reasonable amount of research has been conducted to investigate the implications of SEO s, i.e. the short-term market reaction to a SEO and the operating performance in the years after. As will be described in this section, the main findings of these researches are most often an immediate negative reaction in the stock price around the SEO announcement. Most of the studies on the stock price reaction have been carried out on the US market, whereas only a few studies have focused on European countries. A number of researchers have extended the stock performance research to focus on the long-term, which basically measures the stock price development over a longer period than the short-term market reaction. The long-term stock performance is however out of the scope of this thesis. Furthermore, researchers have put some attention to the long-term operational performance of seasoned equity issuers. The long-term operational performance studies primarily focus on the changes in the income statement, balance sheet items and performance measures thereof. Only a few, though, have coupled the announced use of issue proceeds to the short-term market reaction and longterm operational performance, as we will in this thesis. 2.3.1 Studies on market reaction One of the earliest studies on the stock price reaction to the announcement of a SEO is by Asquith & Mullins, done in 1986 (Asquith & Mullins Jr., 1986). In their study on 266 US listed industrial firms issuing equity, they find that, on average, the announcement is associated with a significant abnormal negative return of 2,7%. This negative reaction is observed in 80% of their sample. The reduction in price and thereby total drop in firm value actually showed to equal about 78% of the proceeds from the SEO. They also found that issuing firms stock on average outperformed the market two years prior to the SEO and underperform the market two years ex-post, with 33% and -6% respectively. Asquith and Mullins (1986) Page 14 of 101

findings are consistent with the hypothesis stating that a SEO in general is a negative signal about a firm s current performance and future potential. Furthermore, their pre-issue stock performance findings support Myers & Majluf s (1984) pecking order theory, indicating an overvalued firm at the time of the SEO. Another study on the US market, conducted by Kim & Purnanandam (2006), also finds negative market reactions to SEO announcements. The magnitude of the reaction they find is a bit lower, -1,34%, but still significant. By looking into the combination of primary and secondary shares they also find support for the signaling theory by Leland and Pyle (1977). Thus, when a SEO contains an amount of secondary shares, meaning that an insider or a large shareholder is selling its shares through the issue, the market reacts more negative than is the case in a pure primary share offering (Kim & Purnanandam, 2006). Korajcyk et al. (1990) also detect an abnormal drop in stock returns of 2,9% around the announcement. Their sample is composed of 1.480 SEO s in the US between 1974 and 1983. However, contrary to Kim & Purnanandam (2006), the reaction to offerings that include secondary shares is roughly the same as pure primary offerings (Korajczyk, et al., 1990). Cox and Aryal, who studied 77 non-financial US firms between 1997 and 2004, found that both rights and underwritten offerings experienced negative returns upon the announcement (Cox & Aryal, 2007). These above researches are, however, conducted on the US market, a market that has been studied extensively. The few available studies of the European market are not as conclusive. A study on the Norwegian stock market on 188 SEO s between 1980 and 1993 by Bøhren et al. (1997), shows a significant positive reaction of 0,47%. Compared to Asquith & Mullins (1986) 80%, only 51% of the announcements are followed by a negative market reaction (Bøhren, et al., 1997). On the Finnish market, Do (2009) finds a significant drop in stock price of 3,6% for the 93 announcements during 1996 and 2003. Similar to Asquith & Mullins (1986) findings, around 80% of Do s observations experienced negative returns around the announcement (Do, 2009). Page 15 of 101

2.3.2 Studies on market reaction with use of issue proceeds The above-mentioned studies all take a general perspective to the announcement effect of SEO s, while the focus of this thesis is to assess the effect the announced use of issue proceeds have on the market reaction and firm performance. One of the first studies to examine this topic was carried out by Masulis & Korwar in 1986. They examined 1.406 offerings from 1963 to 1980, of which 690 was by industrial firms. Masulis & Korwar divided their sample in three categories of intended use of issue proceeds based on the information available in the prospectuses; Debt reduction, capital expenditures and mixed uses. The debt reduction firms intended to repay outstanding debt, the capital expenditure firms intended to invest in specific projects and mixed uses firms were those who listed multiple purposes. For the combined sample of SEO s they found a significant negative two-day return of 3,25%. In the divided samples, they did not find any significant differences between the stated uses of issue proceeds on the announcement effect. With two-day returns of -3,84% for debt reduction, - 3,75% for capital expenditures and -2,52% for mixed uses, all announcements were received negatively by the market (Masulis & Korwar, 1986). Hull & Moellenberndt (1994) specifically studied US equity offerings intended for reducing debt in the years 1970 to 1988. Their research is based on the premise that an increase in debt is a positive signal, and thus a decrease in debt would be a negative signal. Within a three-day event window the 496 sample firms that issued equity with the intention of paying off debt, on average experienced a significant drop in stock price of 2,82%. In an extended window of 11-days, the reduction was 3,05% and still significant (Hull & Moellenberndt, 1994). Their findings are therefore in line with the agency theories, Ross signaling theory and M&M s (1963) tax shield model. Johnson et al. (1996) took another view and studied the market reaction for 141 SEO s in the US who only stated investment as the use of issue proceeds. They used a sample of SEO s that stated anything else than investment as a benchmark, and found that the market did not react significantly different to SEO s announcing specific investment purposes. The announcement day reaction was -2,07% and -2,17% for the investment sample and non-investment sample respectively. A separation of the full investment sample into general capital expenditures and acquisitions did not result in any significant differences either (Johnson, et al., 1996). Page 16 of 101

Gajewski & Ginglinger (2002) study on the French market during 1986-1996 showed a slightly different result. They found that when issuers state that proceeds were intended for either M&A or other investments, the market reaction was still negative, but around 1% more positive than when stating debt repayment or general corporate purpose (Gajewski & Ginglinger, 2002). Gajewski & Ginglinger (2002) assigned the difference to be due to reduced information asymmetry and reduced moral hazard. This is consistent with the agency theories, where debt repayment and general corporate purpose increases free cash flow at disposal to managers. Hull et al. (2009) combined the previous studies and investigated SEO s where the stated use of issue proceeds was either investment related purposes or debt reduction. They removed SEO s where none of this was the case, thus excluding all observations where the intended use of issue proceeds were more general. Like most studies, they also studied US listed stocks, but within a newer period, from 1999 to 2005. For their sample of investments purposes and debt reduction purposes, SEO s the mean three-day market reaction was negative for both groups with 2,73% and 1,72% respectively. Furthermore, they found evidence that the difference in market reaction was significantly different between the two samples (Hull, et al., 2009). This supports the adverse selection theory of Myers & Majluf (1984) and the signaling model of Miller & Rock (1985), that the market receives debt reduction purposes less negative. However, the different reactions to investment and debt reduction purpose SEO s is contradictory to the findings of Gajewski & Ginglinger (2002), A more in depth study of the market reaction of announced intended use of issue proceeds is by Walker & Yost (2008). They divided their sample of 328 US SEO s into investment, general corporate purpose and debt repayment based on the intended use of issue proceeds described in the prospectuses. Walker & Yost s results are in line with the general effect of SEO announcements, as they observed an overall two-day abnormal return of -2,76%. For the divided sample, the observed abnormal returns were -2,18%, -3,2% and -3,26% for the INV, GEN and DEB sample respectively. The smaller reaction for the investment sample were however not significantly different from the other two samples, and thus does not support the findings of Hull et al. (2009). In general, they concluded that being specific about the use of Page 17 of 101

issue proceeds matters. Walker & Yost s categorization of the SEO s is very similar to the one of this thesis and thus their results are highly comparable to ours. Furthermore, Walker & Yost also covered long-term operational performance, so we will return to their study later (Walker & Yost, 2008). 2.3.3 Studies on operational performance The focus of this thesis is not only the announcement effect of SEO s, but also any implications the equity issue may have on the long-term operational performance. This focus area is however not so extensively studied as the short-term announcement effect, but not completely overlooked either. Whereas the short-term studies predominantly use abnormal stock returns, the long-term studies have employed a number of different methods and performance measures. This makes the results of the previous long-term findings more difficult to compare. Some of the first to focus on the implications of SEO s on the operational performance were Healy & Palepu (1990) in their study on 93 industrial firms during 1966 to 1981 in the US. They measured the earnings per share ex-ante and ex-post and found no decline in earnings per share ex-post for issuers compared to industry averages. However, they found increased volatility in earnings in the ex-post years (Healy & Palepu, 1990). A concurrent study of SEO s between 1975 and 1982 by Hansen & Crutchley (1990) however got dissimilar results. Hansen & Crutchely argue that earnings per share may be diluted following a SEO, and that ROA (return on assets) therefore is a better measure for operational performance. Compared to a weighted market average, the 109 issuers in their sample experienced a significant downturn in ROA in the three years ex-post (Hansen & Crutchley, 1990). Both studies ascribe their findings to signaling theories of management s knowledge of future earnings and ROA. These fairly early studies where quite basic in terms of how to illustrate the effect of a SEO, by merely looking at one performance measure. Later on Loughran & Ritter (1997) applied a number of ratios in their study to expose any operational performance implications. Their sample consisted of 1.338 SEO s between 1979 and 1989, excluding firms in the financial and utility industries. Besides ROA, they measured changes in profit margin, EBITDA scaled by sales and scaled by assets as well as investments and R&D scaled by assets. Methodological Page 18 of 101

they matched their sample to non-issuers of same industry and size with the closest EBITDA/Assets and measured performance differences between SEO firms and non-issuing firms from four years ex-ante to four years ex-post. In the four years past the SEO, Loughran & Ritter documented a significant deterioration on all ratios for issuers compared to nonissuers. Their result holds for both median and mean ratios, and after having winsorized their data. Contradictory to the pecking order theory Loughran & Ritter did not find any evidence for increases in debt ratios prior to the SEO, implying that the sample firms were not constrained by their debt capacity (Loughran & Ritter, 1997). In a contemporaneous study, McLaughlin et al. (1996) used the free cash flow as a performance measure for their sample of 1.296 SEO s in the US from 1980 to 1991. Similar to Loughran & Ritter (1997), they found a significant decline of 1,7% in the industry-adjusted FCF from one year prior to three years ex-post. However, McLaughlin et al. s results showed that the sample firms in general performed better, by having higher industry-adjusted FCF ratios than their industry peers throughout the seven years of their analysis. Furthermore, regression results of the determinants of the performance change showed that the ratios of FCF to assets in t-1 were significantly negative related to the change in post-seo performance. Thus their results follow Jensen s FCF theory, that larger ex-ante FCF s is followed by greater performance deterioration. Their regression results also indicated that the investments take time to become productive, as the coefficients for investments in fixed assets (change in PP&E) increased and became more significant throughout the years. This also indicated that SEO firms that invested, performed better than non-investing SEO firms. McLaughlin et al. (1996) extended their research and found two variables that stood out as being significant determinants for the SEO decision. They found that firms with high growth opportunities, as proxied by Tobin s Q 9, and high leverage had a higher tendency to perform a SEO (McLaughlin, et al., 1996). This indicates that firms with high leverage seek to reduce or avoid higher bankruptcy costs and adjust towards a target leverage ratio, as the trade-off theories suggest. 9 McLaughlin et al. (1996) proxy Tobin s q as market value of equity plus book value of debt to book value of asset. Page 19 of 101

Heron and Lie (2004) also observed performance declines in their sample of 2.038 primary share offerings in the US from 1980 to 1998. The decline was measured with operating income scaled by sales and assets as well as FCF scaled by assets. Like McLaughlin et al. (1996), Heron & Lie also found evidence for the significant negative effect of pre-seo performance to the change in performance. With regards to firm characteristics they found, that issuers in general did not have low cash holdings, i.e. were not in need of equity/liquidity, or had high leverage in comparison to industry medians (Heron & Lie, 2004). Cox and Aryal (2007) on the other hand found no significant changes in ROA, ROE or operating income scaled by sales (Cox & Aryal, 2007). Having said that, Cox & Aryal only looked at a three-year period from one-year prior to one-year post and they did not compare their sample to any benchmark samples. Thus their results should be looked upon with caution. As was the case with studies on the announcement effect of SEO s, a large proportion of the studies on long-term operational performance study the US listed firms. The study by Andrikopoulos (2009) on UK SEO s is one of the few non-us studies. He examined 1.542 issues on the London Stock Exchange, AIM market excluded, in the period from 1988 to 1998. He used a benchmark sample of non-issuers matched by industry and size and found a significant decline in both ROA and net profit margin compared to both benchmarks, for up to three years after the SEO. This is while issuers on average experienced a growth in turnover above benchmark firms in the same three years. Furthermore, he observed significant larger investments in assets for the sample firms in comparison to the benchmarks. Andrikopoulos linked his findings with the agency theories and free cash flow theories that excess funds/cash flows are invested in negative NPV projects, and thus that the firms are subject to empire building managers (Andrikopoulos, 2009). Bayless et al. (2005) studied 1.752 SEO s by industrial firms in the US during 1974 to 1999 and found similar declining operational performance for issuing firms ex-post. Overall they reported a significant decline in sales growth, operating income growth, asset growth, growth in cash to total assets as well as capital expenditures. On the other hand they found that growth in debt and growth in free cash flow increased significantly (Bayless, et al., 2005). Their findings were however not compared to any industry benchmarks, and thus it is Page 20 of 101

difficult to assess whether their findings can be assessed to the SEO offerings or other macroeconomic effects. 2.3.4 Studies on operational performance with use of issue proceeds A common feature of the previous studies are the generalization of the SEO s, where the researchers have disregarded the announced intended use of issue proceeds. As mentioned by Cox and Aryal (2007), the proceeds may have been used to pay back costly debt and therefore may or may not have had a positive or negative effect on ROA (Cox & Aryal, 2007). Segregation between the intended uses of issue proceeds might therefore show another picture or at least a differentiated picture in regards to operational performance of SEO firms. Only a few studies though are available with this segregation. As mentioned earlier the study by Walker & Yost (2008) also covers the long-term operational performance, split into three subsamples based on the announced use of issue proceeds. Recall they divided their US sample into firms who use the funds for investment (INV), debt repayment (DEB) and general corporate purpose (GEN). The operational performance of the sample firms were studied from the year prior the SEO to two years after. To determine the use of SEO proceeds they measured changes in total assets, capital expenditures plus R&D cost, the level of long-term debt and working capital in relation to the year prior to the SEO. They found that firms that intended to invest in fact increased their total assets more than the GEN and DEB subsamples. However, both INV and GEN firms increased their assets significantly more than DEB firms did. In comparison to this, they found that the working capital ratio for all three groups only proved to be significant for the GEN and DEB samples. An absolute increase in capital expenditures plus R&D were also found for all groups, with INV and GEN firms showing a significant higher increase compared to DEB firms. With regards to the level of long-term debt, they found that firms that intended to repay debt generally had larger amounts of debt prior to the SEO compared to the industry median. Even though DEB firms had a higher level of long-term debt, Walker & Yost found that these firms only decreased their leverage significantly in the SEO year, before increasing it to the same pre-seo level in the following two years. Neither the INV, DEB nor GEN subsample had a significant different leverage in the second year after a SEO, which indicated that the debt Page 21 of 101

firms rather are replacing its debt instead of repaying it. With regards to performance, Walker & Yost found that all three groups saw a significant decline in operating income to total assets, with GEN firms showing a significant decline. However, when they adjusted for industry, both the INV and DEB subsample actually saw an increase in comparison to industry peers. As with the short-term findings, they concluded that firms that announce general corporate purpose, as intended use of issue proceeds appeared to invest in less profitable projects compared to INV and DEB firms. A similar study by Autore et al. (2009) also examined the post operational performance taking into account the announced use of issue proceeds, with same categorization as Walker & Yost (2008). Autore et al. s sample consists of 880 SEO firms listed in the US during 1997 to 2003, excluding firms in the financial and utility industries. Autore et al. (2009) focused on the change in operating income scaled by sales and scaled by total assets. Both performance measures were industry and performance adjusted, and in both cases, they used OLS and quantile regressions to validate their results. For the entire sample, they found a significant decline in both performance measures when adjusting for industry and performance. However, when separating the sample into the three subsamples the decline was only significant for the DEB and GEN sample, while the performance for the INV subsample remains unchanged. Their regression results validated these results and furthermore indicated that the size of the SEO in terms of issue proceeds relative to firm size, does not have a significantly effect on the change in operational performance. These findings are partly consistent with the findings of Walker & Yost (2008). Autore et al. (2009) linked their findings to the fact that the INV subsample most often has a specific investment opportunity and thus are less likely to take advantage of a possible overvaluation. 2.3.5 How are the issue proceeds used While the before mentioned studies focus on the overall performance changes of SEO firms, the following study tries to link the direct effect of SEO proceeds to changes in key ratios. Kim & Weisbach (2008) studied as much as 13.142 SEO s from 38 different countries across continents between 1990 and 2003, among these 1.820 from continental Europe and 1.651 from the UK (Kim & Weisbach, 2008). They employed quite a different method than previous studies, and looked into the effect of SEO proceeds on seven accounting variables; change in Page 22 of 101

total assets, inventory, cash holdings, capital expenditures, R&D, acquisitions and the reduction in long-term debt. The variables were looked upon from the year-end of the SEO year to three years ex-post. More precisely they ran a regression on these variables with the proceeds from the SEO and other sources of funds as explanatory variables. Other sources of funds covered funds from divestitures of fixed assets, long-term debt issuances and funds from operations. By doing so, they argued that the model were able to explain which funds were used for which purpose, and thus give insight into how SEO proceeds are being used in comparison to internal generated funds. On all dependent variables, besides long-term debt reduction, they found that SEO proceeds had a significant positive effect. Furthermore, they found that the issue proceeds mainly were held as cash and invested in R&D, while internal generated funds mainly were used for reductions in long-term debt. Kim & Weisbach s (2008) model also gave the possibility of examining the dollar change in the accounting variables by a dollar change in either SEO proceeds or funds from other sources. The results showed that for a dollar increase in issue proceeds, approximately 50 cent were held as cash the first year after and then decreasing to 30 cent in the third year after the SEO. In terms of R&D they found that a dollar increase in issue proceeds raised R&D expenses by about 19 cent in the first year and increased to about 65 cents in the second year after. This indicated that the issue proceeds initially were held as cash and then spend on R&D investments over time rather than invested all at once. Kim & Weisbach (2008) thus concluded that SEO proceeds were used to finance investments and that the SEO had been conducted to take advantage of a high market valuation. 2.3.6 Conclusion based on previous research Based on the previous literature, it seems that the SEO announcement generally entails an immediate negative reaction, with only the Norwegian serving as an exception. Furthermore, the studies show that a SEO announcement is received negatively by the market regardless of the intended use of issue proceeds. However the previous findings are inconclusive to whether the market reaction is differentiated by the announced use of issue proceeds. Based on the previous proposed theory one would expect debt repayment SEO s not to experience the decline that the studies observe. Page 23 of 101

The operational performance studies all indicate the same overall change in performance subsequent to a SEO. Aside from one study, all others observe negative ex-post performance compared to ex-ante performance. The few studies that divide their sample based on the intended use of issue proceeds do not show any conclusive results. Only the results for firms stating general corporate purposes are consistently indicating a negative performance change ex-post. The previous findings on firms who are stating specific investments or debt repayment indicate that it is still uncertain if and how a SEO affect these firms performance. For an overview of previous findings in respect to the market reaction, as well as subsequent performance see Appendix A. 3 Hypotheses The previous sections have outlined the relevant theories regarding seasoned equity offerings, as well as previous empirical findings related to the topic. Based on this and in continuation of the research question the specific hypotheses of the thesis are presented below. The hypotheses are formulated as our predictions given the theory and empirical findings. The hypotheses are arranged in the same context as the analysis. The following hypotheses are related to the short-term implications: H1: Firms offering seasoned equity experience significant negative abnormal stock returns around the announcement. H1A: H1B: H1C: Firms offering seasoned equity intended for debt repayment do not experience significant abnormal stock returns around the announcement. Firms offering seasoned equity intended for general corporate purpose experience significant negative abnormal stock returns around the announcement. Firms offering seasoned equity intended for investment experience significant negative abnormal stock returns around the announcement. H1D: H1E: Significant difference in the abnormal stock returns around the announcement between the use of issue proceeds subsamples. Offer size, Inclusion of secondary shares, Firm age, Pre-SEO Free cash flow and Leverage have a significant impact on abnormal stock returns. Page 24 of 101

The following hypotheses are related to the long-term implications. H2: Firms offering seasoned equity experience a significant decline in operating performance after a seasoned equity offering compared to non-issuers. H2A: H2B: H2C: Firms offering seasoned equity intended for debt repayment do not experience a significant change in operating performance after a seasoned equity offering compared to non-issuers. Firms offering seasoned equity intended for general corporate purpose experience a significant decline in operating performance after a seasoned equity offering compared to non-issuers. Firms offering seasoned equity intended for investment experience a significant decline in operating performance after a seasoned equity offering compared to nonissuers. H2D: Significant difference in the operational performance change after a seasoned equity offering between the use of issue proceeds subsamples. The following hypotheses are related to the use of issue proceeds: Since we theoretically cannot determine how firms actually use the issue proceeds, for each subsample we therefore state and test the following alternative hypotheses to determine the use of issue proceeds. Finding support for these hypotheses therefore means that the issue proceeds are used for this purpose. H3A: H3B: H3C: H3D: H3E: SEO proceeds have a significant impact on Cash & short-term investments. SEO proceeds have a significant impact on Capital expenditures. SEO proceeds have a significant impact on R&D investments. SEO proceeds have a significant impact on Acquisitions. SEO proceeds have a significant impact on Long-term debt reductions. 4 Research Methodology The aim of this section is to give a description of the methodology applied in order to answer the stated hypotheses in section 3. More specifically the quantitative research methodology for the short- and long-term study and the use of issue proceeds analysis are discussed, along with the relevant choices made in the process of conducting the event- and performance Page 25 of 101

study. We make use of three methods, which all take its origin in acknowledged methodologies. First, a short-term event study will be conducted, where the structure will take its origin in MacKinlays market model approach from the article Event studies in economics and finance (MacKinlay, 1997). The purpose of this method is to tests whether any abnormal stock returns can be detected around the announcement of a seasoned equity offering. Roughly, this is done by estimating what the normal stock return would be around the event, given the event had not taken place, and deducting this from the actual return, thereby detecting the abnormal return. Thus, the short-term study will focus solely on the stock returns around the announcement of a SEO. The method will be described further in section 4.1. Second, a long-term performance study will be conducted based upon the Difference-in- Differences method using accounting data to detect any positive or negative change in performance. The change in performance will be measured on the basis of operating performance measures, where the ex-ante performance will be compared to the ex-post performance, relative to an appropriate benchmark as defined by Barber & Lyon (1996). Further details regarding the long-term performance study will be described in section 4.2. Third, we employ a relatively new regression specified by Kim & Weisbach (2008) to examine the actual use of issue proceeds for SEO firms. To our knowledge Kim & Weisbach (2008) are the first to employ this model, with which they are able to estimate the use of funds from equity offerings or debt issuances relative to internal generated funds. We use this model to test whether the issue proceeds are used in accordance with the announced intended use. This study will be described further in section 4.3. 4.1 Short-term event study As mentioned, the aim of the short-term event study is to detect how the market reacts to an announcement of a SEO. Specifically we focus on the immediate effect on the stock price of a firm to such an announcement. We will be doing this by following MacKinlay s (1997) eventstudy methodology using the market model approach on daily stock returns. This section will therefore go through the assumptions underlying this method as well as relevant choices Page 26 of 101

regarding choice of estimation period, event window, calculation of abnormal returns and choice of parametric and non-parametric statistical tests. 4.1.1 Efficient market hypothesis One of the basic assumptions for doing a short-term event study is that the efficient market hypothesis (Fama, 1970) holds. The efficient market hypothesis states that all new public available information, in this case the announcement of a seasoned equity offering, should be incorporated immediately in a firm s stock price. This means that the stock price of a firm should reflect all current public information regarding the specific firm, thus implying that it is not possible to earn an abnormal profit (arbitrage opportunities) around the announcement of an event. For this to hold the announcement of the seasoned equity offering should be unexpected, which is assumed to be the case in our sample. Fama (1970) distinguishes between three types of market efficiency; weak, semi-strong and strong market efficiency. The weak form states that all historical prices are reflected in the stock price however no other information can predict the future stock performance, whereas the semi-strong adds all public available information to the weak form assessment. The strong form takes information that is not public into consideration, suggesting that there is private and insider information, and that the stock price reflects all this information. Since we are not able to measure the effect of private and insider information, it is assumed that the semistrong market efficiency holds, which states that the announcement of an unexpected seasoned equity offering should have an immediate effect on the stock price of a firm. 4.1.2 Event window and estimation period The announcement day or event day is the day of the official announcement according to Bloomberg (t = 0). In accordance with the efficient market hypothesis, the announcement should be incorporated in the stock price immediately. Thus one should ideally use a one-day event window, but due to announcements reaching the public after market has closed or information slips just prior to the announcement, this is rarely possible. To accommodate for this, a three-day event window is often used, which in practice should capture any effect of the announcement. This is in line with the semi-strong market hypothesis. However, markets may not always be efficient and MacKinlay (1997) & Peterson (1989) therefore suggest the use of several tests with different event windows. A longer event window are also appropriate Page 27 of 101

in order to catch information leakage or any insider trading, meaning that effect of the announcement is gradually incorporated into the stock price. Choosing a longer event window on the other hand means that controlling for confounding effects are often a problem (McWilliams & Siegel, 1997). A longer event window also severely reduces the power of the test statistics, which leads to false inferences about the significance of an event (Brown & Warner, 1985). Furthermore, it has been empirically demonstrated that the short event window usually will capture the effect of an event ( (Ryngaert & Netter, 1990), (Dann, et al., 1977) & (Mitchell & Netter, 1989)). Figure 1 - Estimation period and event window (three-day event window) Source: Own contribution We choose a primary event window of three days, consisting of one day before the announcement ( 1 = -1), the announcement day (0) and the day after the announcement ( 2 = +1). As will be further discussed in section 5.1, the announcement dates provided by Bloomberg do not always seem to be correct, meaning the dates sometimes deviate with a couple of days. By using a longer event window, we are able to take into account the possible wrong announcement dates provided by Bloomberg. We therefore also employ a seven-day window ( 1 = -3 to 2 = +3) and a 21-day window ( 1 = -10 to 2 = +10). To estimate the normal performance of the stock in comparison to the market, we need to choose an estimation period. The estimation period should be long enough to reliable estimate abnormal returns. As recommended by MacKinlay (1997) & Brown & Warner (1985), we choose to use an estimation period of 200 trading days. The estimation period for this study is therefore from 201 days prior to the announcement to two-days prior ( 0 = -201 to 1-1 = -2) 10. 10 For the seven-day event window the estimation period is ( 0 = -203 to 1-1 = -4), and for the 21-day event window the estimation period is ( 0 = -210 to 1-1 = -11). Page 28 of 101

4.1.3 Choice of model In order to measure the abnormal return of a stock, several models can be applied. The most common ones are the statistical models, such as the constant mean return model and the market model. Alternative to these models are the Capital Asset Pricing Model (CAPM) and the Arbitrage Pricing Theory (APT), which both are economic models. The economic models are generally restricted statistical models, which potentially could increase the explanatory power of the models. However the results using these models are suggested to be very sensitive to these restrictions. This sensitivity is easily avoided by using the market model or constant mean return model, which is why the use of CAPM and APT is almost ceased. It is therefore the statistical models such as the constant mean return model and the market model that dominate when performing event studies (MacKinlay, 1997). In this paper we will be applying the market model to estimate the abnormal return since, according to Brown & Warner (1985), it is suggested to be the one providing the best results. This is due to the fact that the market model, compared to the constant-mean model, delivers lower variances in the abnormal returns. This increases the explanatory power of the model, and any abnormal returns are therefore easier detected (MacKinlay, 1997). 4.1.4 Abnormal returns The market model is a linear specification that relates the return of a given stock, to the return of the market portfolio. By relating the stock return to a market index we are able estimate the part of the stock movement that is related to the market return, thereby reducing the variance of the abnormal return (MacKinlay, 1997). The linear relationship for the return of a given stock i and the market return is as follows: : ( 1 ) ( ) ( ) where is the expected normal return for a stock, which is measured on the basis of the estimation period specified in section 4.1.2. The normal return is calculated on the basis of log Page 29 of 101

returns, which has the advantage that it increases the normality in the data, as suggested by Campbell et al.(1996). is the return on the market portfolio during the same estimation period as the stock. is the zero mean disturbance term and, and are the parameters of the model. To measure the return of the market portfolio MacKinlay (1997) and Bartholdy et al.(2007) suggest the use of a broad based stock index, that reflects the represented stocks in the sample. We will be using the local MSCI indexes, thereby taking into account that there could be national differences between the firms in the sample. If a local MSCI index is not available the MSCI Europe index will be applied. 11 On the basis of the market model the abnormal returns can be calculated by measuring the difference between the actual return and the estimated return based on the estimation period. The formula used to measure the abnormal return is given by: ( 2 ) where is the abnormal return on stock i at time t. is the actual observed return for stock i at time t, are the parameters of the market model and is the return on the market portfolio at time t. Before any conclusions regarding the event, in our case the SEO announcement, can be drawn, the abnormal returns must be aggregated through time and stocks. First abnormal returns will be aggregated through time for each individual stock and thereafter the abnormal returns will be aggregated across stocks (MacKinlay, 1997). By doing this we are able to measure the cumulative abnormal return in the event windows. The cumulative abnormal return for a specific stock is given by: ( ) ( 3 ) where and represent the event window, is the number of days prior to the event and is the number of days after the event. As mentioned, the event windows in this study are [- 11 In our sample only Luxembourg does not have a local MSCI index, and thus MSCI Europe is used as benchmark. Page 30 of 101

1;+1], [-3;+3] and [-10;+10], and CAR will therefore represent three, seven and 21 days. Furthermore the aggregated abnormal return across stocks and time is given by: ( ) ( ) ( 4 ) With this aggregation it is assumed that there is no overlap in the event window of the different stocks, so that the abnormal and cumulative returns will be independent across stocks, suggesting no correlation (MacKinlay, 1997). 4.1.5 Statistical test In order to test our main hypothesis regarding the announcement effect of a SEO, there are several statistical tests to choose between. We will be using a battery of tests based on the study Conducting Event Studies on a Small Stock Exchange by Bartholdy et al. (2007). Specifically, we employ three parametric and three non-parametric tests. The parametric and non-parametric tests rely on different assumptions, and therefore should provide us with high validity in our test results. In the following section the assumptions behind the tests will be described thoroughly. The test results will be presented in section 6. 4.1.5.1 Parametric test: The three parametric tests chosen for this study are the most commonly applied tests to test for significant abnormal return. These tests are basically t-test, that tests the differences between two means and scale this by some measure of estimated variance. The t-statistic below represents the basis of the tests: ( ) ( ( )) ( 5 ) where ( ) is the mean CAR 12, and ( ( )) is the estimate of the standard deviation of ( ) by using an appropriate estimator. In order to use these test, certain assumptions have to be made about the sample data. The tests rely upon the assumption that the data is normally distributed, with an expected value of the abnormal return to be zero 12 See equation ( 4 ) Page 31 of 101

with a constant variance. If these assumptions hold the parametric test are said to have more statistical power, than the non-parametric test, because they are more accurate. However if the assumptions are violated, the results of the parametric tests can be misleading. Empirically the assumptions do often not hold, which is why the parametric test are often not considered robust. This is further supported by Fama (1976), who suggests that the log of price changes are fat-tailed, relative to a normal distribution (Fama, 1976). To test whether our sample data is normally distributed, the Shapiro-Wilk test will be applied (Shapiro & Wilk, 1965). This test is known to be the most powerful when testing whether a sample is normally distributed ( (Razali & Wah, 2011) & (Saculinggan & Balase, 2013)). The three parametric tests applied to test the null hypothesis, each differ from another in the way they deal with the sample data ( (Brown & Warner, 1985), (Patell, 1979) & (Bartholdy, et al., 2007)). The tests are presented below: T1: Adjusted cross-sectional independence T2: Standardized abnormal return T3: Adjusted standardized abnormal return The main difference between the above-applied parametric tests is the choice of method used to estimate the variance. The abnormal return on the event day for the T1 test statistic is assumed to be independent across stocks. The variance of the T1 test statistic is the sum of the variances of abnormal returns of the individual stocks. For T2 the abnormal return for each stock is scaled by the standard deviation of each individual stock and afterwards they are all added together to produce the test statistic. T3 is an adjusted version of T2, where the test statistics is calculated by using the standard deviation of the forecast errors to scale abnormal returns, instead of using the actual standard deviations (Bartholdy, et al., 2007). Moreover we use the test with Patell s adjustment to improve the performance of the test (Patell, 1979). 4.1.5.2 Non-parametric test: Unlike the parametric test, the non-parametric tests do not rely on the strict assumptions of a normal distribution. This makes the non-parametric test more robust and reliable if the assumption of normality is violated, as stated by Bartholdy et al. (2007). Even though the nonparametric tests provide more robust test results they are not as statistical powerful as the Page 32 of 101

parametric test. Therefore the combination of the non-parametric and parametric test will provide a good combination of different types of tests that increase the validity of our conclusions. The following three non-parametric tests will be applied: T4: Rank Test T5: Sign Test T6: Generalized sign test The T4 rank test is based on a transformation of each stocks time series excess return into a rank. This transforms the distribution into a uniform distribution, which means we are able to check for the abnormal performance in the event window, through a comparison of ranks in the event window versus the estimation period. This method of ranking generally performs better than other non-parametric test (Corrado, 1989). T5 is a sign test, which assigns a sign for each day in the estimation period as well as event window. It will assign +1 if the excess return is positive, -1 if the excess return is negative and 0 if there is no positive or negative return, meaning it disregards any asymmetry in the original distribution. The test relies on the assumption that there is an even probability to observe a positive or a negative abnormal return (Corrado & Zivney, 1992). For T6 the probability of observing a positive or negative abnormal return is estimated on the basis of the actual returns from the estimation period (Cowan, 1992). All tests are performed in Microsoft Excel and the workbooks are provided on the enclosed CD. 4.1.6 Mean comparison test To determine if the abnormal returns divided by SEO motive are significantly different from each other, we employ the parametric one-way ANOVA and the non-parametric Kruskal- Wallis test. The one-way ANOVA test whether the means of the subsamples are different from each other, by comparing the variances of the subsamples. The non-parametric Kruskal-Wallis does not assume normality in the data, and thus performs better with non-normal data. Both the ANOVA and Kruskal-Wallis test does not provide information to which subsample is different, but only that one sample is different to the other. To test which subsample is Page 33 of 101

different from each other, we therefore employ both Bonferroni and Least Significant Difference (LSD) multiple comparison tests. 13 4.1.7 Cross-sectional regression We employ cross-sectional regressions to examine whether any other factors besides the SEO motive influence the abnormal returns around the announcemnt, and in which direction. Performing cross-sectional regressions by Ordinary Least Squares, on the abnormal returns is a commonly used method in event study literature (MacKinlay, 1997). The model is the based on the econometric model in equation ( 6 ) below. ( 6 ) where y is the dependent variable, here CAR, x are the explanatory variables being examined and ε is the error term. Whether the OLS estimators are good approximated and thus are the best linear unbiased estimator (BLUE), highly depends on the assumptions made. The assumptions are known as the Gauss-Markov conditions, which consist of the four following assumptions. (A1) The expected value of the error terms is zero, (A2) x and ε are independent, (A3) homoscedasticity (A4) zero correlation between the error terms. The conditions above are assumed to be satisfied. 4.1.8 Challenges of the short-term event study Since the short-term study relies on the efficient market hypothesis, the power of the study is influenced by the degree of efficiency in the market. The market may not be able to incorporate the effect of a SEO fully in the stock price as some details regarding the intended use of issue proceeds may not reach the market until later. Thus the announcement may be followed by a lot of unknowns that influence the stock price over time. Furthermore, a SEO may not be completely unanticipated as concession to perform a SEO at some point, often is granted by the board of directors at the ordinary board meetings. However this grant does not constitute an official SEO announcement. A challenge for all researchers in short-term event studies is the impact of thinly traded stocks. As Bartholdy et al. (2007) show, thinly traded stocks make the tests less powerful and 13 Tukey s multiple comparison method often performs better than Bonferroni and LSD, however Tukey s MCD assumes equal sized sample which is not the case for our sub-samples. Page 34 of 101

thus less reliable. This is caused by less movement in the prices, which leads to unstable parameter estimates of the market model. We therefore, as advised, remove stocks that are traded less than 40% of the days in the estimation period and event window combined (Bartholdy, et al., 2007). For our sample only a small amount of stocks were thinly traded and thus removed, more on this in section 5.1. 4.2 Performance study Whereas the short-term event study seeks to explore the immediate announcement effect of a SEO, the performance study will investigate the long-term operating performance of issuing firms. Instead of focusing on the stock price reaction, as we did in the short-term event study, the performance study will look at accounting data, to measure if firms perform better/worse after a SEO than they did prior to it. The methodology used to test our hypothesis will be based on the research from Barber & Lyon (1996). Furthermore this section will describe and evaluate the relevant choices made when designing the performance study. This includes the development of a model for expected performance, the selection of operating performance measure, choice of benchmark and the statistical test. 4.2.1 Difference-In-Difference method In order to test whether the performance of SEO firms changes after a SEO, the Difference-In- Difference (DID) method is applied. This method is generally used in event studies that try to measure the effect of an event or treatment on variables in the time following the event. It does so by comparing the difference of two groups before and after an event or treatment, and thus captures the difference within the difference. For our study this means comparing our sample of SEO firms to a control group in the years prior and post to the SEO. One of the reasons for choosing the DID method is due to its relatively simplistic approach, as well as its ability to bypass some of the endogeneity problems that arise when comparing two heterogeneous groups (Meyer, 1995). The DID model will be used for evaluating the impact of a SEO on the performance of our sample firms during 2000 to 2010. The specific performance variables will be discussed in section 4.2.4 When applying the DID model, the estimators and standard errors are typically derived by using Ordinary Least Squares in repeated cross sections of data on the individuals within the Page 35 of 101

treatment- and control group (Bertrand, et al., 2004). The following regression 14 is used to derive the DID estimator: ( 7 ) where denotes the outcome of interest, for firm i, in group s (sample or benchmark), at time t (pre or post SEO). and define the fixed effects for state and time respectively, are relevant control variables, is defined as a dummy variable that reflects whether or not the event has affected group s at time t. is the treatment effect or the so called DID estimator and is the error term (Bertrand, et al., 2004). Thus the DID estimator are equivalent to the estimated impact effect the SEO has on the variables of interest, in our case the performance measures. An alternative way of deriving an appropriate DID estimator is through the use of the DID table. The main difference between the DID table and the above equation is that the DID table is quite easy to employ and ultimately displays a simple DID estimator. However including any control variables that could enhance the specification of the model is not possible using the table. The DID table is illustrated below: Table 1 - Difference-In-Difference table Yst t=1 (pre SEO) t=2 (post SEO) Difference s=1 (SEO group) Y11 Y12 Y12 - Y11 s=2 (Control group) Y21 Y22 Y22 - Y21 Difference Y21 - Y11 Y22 - Y12 (Y21 - Y11) - (Y22 - Y12) Source: Bertrand et al. (2004), Own contribution Y11 denotes the outcome of interest within the treatment group before the event, whereas Y21 denotes the same for the control group. Y12 and Y22 denote the outcome of interest of the treatment- and control group respectively after the SEO. For each state and group the difference is calculated, in the end resulting in the DID estimator (Y21 - Y11) - (Y22 - Y12), which is the same as in equation ( 7 ) if no control variables are included. We choose to collapse 14 This regression will in the rest of the thesis be referred to as DID regression Page 36 of 101

our data into pre-seo observations and post-seo observations by averaging the outcome of interest for all observations. This method is widely used by researches and has the advantage of overcoming problems regarding serial correlation, which we will discuss further in the next section (Roberts & Whited, 2013). In section 6.3 we will at first present results from the DID table, as this also presents a descriptive overview of the sample group and control groups. After presenting the DID table, we do a more in depth analysis of the impact of a SEO on financial performance, which is why we employ the DID regression method. For this purpose we use first differencing and take the difference between the pre-seo and post-seo characteristics, which is then used as our observation. We do this to simplify the DID regression equation a bit as the time aspect now has been completely removed from the equation. For the general specification, in equation ( 8 ), the model now has a dummy variable equal to one if the firm performs a SEO and zero otherwise. We employ this regression for the full sample, and individually for each subsample and their respective control firms. Furthermore, we add a number of control variables that might have an impact on the variation in the performance measures. ( 8 ) 4.2.1.1 Quantile regression Besides applying OLS regressions we also employ quantile regressions. The purpose of the quantile regression is to check the robustness of our OLS regression. The main difference between the OLS regression and the quantile regression is that the quantile regression estimates on a pre-specified percentile instead of the mean. We use the 50 th percentile, the median, which is also employed by previous researchers. By estimating on the median we are able to overcome any problems of non-normality in our dataset, thereby reducing the effect of outliers. The quantile regression specification is the same as for the OLS regression. 4.2.2 Autocorrelation In previous research on the validity of the DID model Bertrand et al. (2004) state that many researchers typically fail to correct for autocorrelation within the data. As a consequence this leads to standard errors that understate the standard deviation of the DID estimator. This Page 37 of 101

would lead to a large overestimation of t-statistics as well as significance levels. Bertrand et al. (2004), suggest a way to circumvent this by ignoring the time-series aspect when computing standard errors. The logic behind this is to simply average the performance variables into preperformance and post-performance, resulting in 2 groups and thus ignoring the time-series information. As mentioned earlier we collapse the performance variables in question into an average of the three years prior and an average of the three years post. As a result of this, over-rejection on the basis of t-statistics is relatively small. The downside to this procedure is that the power of the model diminishes fast with sample size. However due to the fact that we have a relatively large sample size and a control firm for each of these, we expect that this will not be a problem in this case 15. 4.2.3 Parallel trend assumption The main assumption behind the DID model is the parallel trend assumption. The basic idea behind this is that in the absence of a treatment the average change in the response variable should be the same for both the treated as well as control group (Roberts & Whited, 2013). Basically this means that the treated- and control group over time should be affected by the same things, such as changes in politics, economy etc.. If the two groups are not affected by the exact same things over time, the parallel trend assumption is violated and the model will not be well specified. 4.2.4 Selection of operating performance This section describes the selection of operating performance measure. There are several measures that can be applied in order to measure performance most precisely. In this thesis we will be working with three performance measures: Operating Return on Assets (OROA) = Operating Return on Sales (OROS) = Free Cash Flow Return on Assets (FCF/TA) = 15 Bertrand et al. (2004) performs his test on the basis of a sample of 50 Page 38 of 101

Previous research suggest the use of operating income before interest, taxes, depreciation and amortization (EBITDA), operating income before interest and tax (EBIT) or earnings as a performance measure, whereas most recent studies tend to favor the use of EBITDA over earnings ( (Barber & Lyon, 1996), (Loughran & Ritter, 1997), (McLaughlin, et al., 1996) & (Bayless, et al., 2005)). The reason for favoring EBITDA over earnings as a performance measure is that it excludes interest expense, special items, income taxes and minority interest which are included in earnings. This is due to the fact that a SEO typically will have an effect on the capital structure of a firm, which consequently has an effect on the interest expenses and thereof earnings. Furthermore many issuers temporarily park some of their issue proceeds in interest earning instruments prior to investing in operating assets (Loughran & Ritter, 1997). It is therefore argued that EBITDA is a more clean measure than earnings, in measuring performance. This means we disregard the cost of capital and the tax implications, which are included in earnings as a performance measure. Even though some of the previous literature suggests the use of EBITDA we favor the use of EBIT. EBIT does not disregard a firm s depreciation and amortization in the income statement. The reason for using this performance measure is that we argue that even though depreciation and amortization is a non-cash measure, we can use this as a proxy for a firm s capital expenditures 16, which is a necessity in order to grow. Moreover we argue that a firm s tangible assets are the assets generating the earnings. These tangible assets typically always include depreciation and amortization in order to derive at a reliable book value of those assets, which makes this item a part of the core operations of a firm. However the level of depreciation and amortization can vary much in connection to capital expenditures, which might skew the assumption behind using this as a proxy. Nevertheless EBIT is used as a performance measure, which includes depreciation and amortization. Besides using EBIT as a performance measure Barber & Lyon (1996) and McLauglin et al. (1996) suggest the use of a cash flow measure as a performance measure. The logic behind this is that a cash flow measure can overcome the potential earnings manipulation problem that is exist with EBIT as a performance measure. A cash flow measure could therefore be 16 Capital expenditures are neither included in EBITDA nor EBIT Page 39 of 101

more appropriate than the accrual-based measure that EBIT is. However in this paper we will not apply the suggested cash flow measure provided by Barber & Lyon (1996) and McLauglin et al. (1996) but instead we will be applying the free cash flow as a performance measure. This might give a clearer view on performance and the firm s ability to generate cash, compared to EBIT because it will not be affected by accounting or depreciation. Moreover we are able to include a firm s capital expenditures, which are not included in EBIT, thereby overcoming some of the shortcomings of EBIT as a performance measure. Worth noticing is however that a negative free cash flow not necessarily is negative, because it can be skewed by the investments made by a firm. The free cash flow is defined as: Free cash flow = EBIT - Taxes + Depreciation Capital Expenditures Change in Working Capital ( 9 ) Barber & Lyon (1996) furthermore argue that the operating performance measure has to be scaled. Since we are interested in measuring the productivity of our assets in place, they suggest scaling EBIT with the current value of operating assets, which however is not reported in the financial statements. Therefore we favor the use of end-of-period book value of total assets. However there are some limitations to the use of end-of-period book value of total assets. The problem with the total assets recorded in the financial statements, are that they are measured at historic cost, whereas operating income is recorded in current prices. Moreover total assets do not distinguish between current assets and non-current assets, meaning the use of total assets could understate the true productivity of the operating assets (Barber & Lyon, 1996). Due to the pitfalls with scaling by total assets, Barber & Lyon (1996) suggest to scale operating income by alternative measures, such as cash adjusted assets, sales and market value of assets. However the alternative performance measures do not prove to provide higher significance, meaning the choice of scaling measure does not seem to have a big relevance. Nevertheless in order to try and overcome some of the pitfalls with total assets as a scaling measure, sales will be used as a second scaling measure in this paper. By using sales as a scaling measure we are able to overcome the pitfall associated with total assets, because sales is measured in current dollars just as our performance measures EBIT and FCF, because both the scaling measures and performance measures are derived from the income Page 40 of 101

statement 17. Furthermore this scaling measure could be more appropriate when testing SEO firms because a these firms will experience a large increase in cash thereby effecting total assets. Also many firms issue new equity after a recent acquisition, thus have higher book values than control firms, which consequently means that the OROA and FCF/TA measure for sample firms would be lower because of the recent acquisition. Even though sales as a scaling measure overcomes some of the pitfalls with total assets, it adds some new concerns to deal with. For instance the disadvantage by using sales as a scaling measure is that we are not able to measure the productivity of our assets in place, meaning we are not able to measure an increase in a firm s productivity because sales and EBIT might increase proportionally. Based on the above reflections both scaling measures have some pitfalls, but combined provide a good scaling measure in order to measure operating performance. 4.2.5 Event window Just as with the short-term event study an event window has to be defined. As many of the previous studies we will be using an event window of [-3;+3] years, where the announcement of the SEO is regarded as year 0. Year 0 is excluded in the analysis due to fact that we do not want the event to skew the actual numbers. First of all the event window should be long enough in order to capture the effect of the SEO. Moreover with a longer event window we are able to take the effect of mean reversion into account. Mean reversion relates to the tendency of a firms performance to revert to its actual mean. For instance, if a firm performs well before an event, mean reversion might lead a researcher to conclude that post-seo performance is poor (Barber & Lyon, 1996). On the other hand the length of the event window should be short enough to derive at an appropriate sample size. Moreover with a short event window we minimize the effect of survivorship bias, which reflects the tendency to omit firms from the sample because of lacking financial data, because they went bankrupt, hence no longer exist, or if they were purchased by another firm. Therefore the choice of event window is a trade-off, however we follow previous literature and employ an event window of [-3:+3] years. 17 FCF includes the change in working capital, which is not derived from the income statement Page 41 of 101

4.2.6 Choice of benchmark In order to measure the long-term performance effects of a SEO, our sample has to be compared to a benchmark (control group). The reason for comparing the sample to a benchmark is to catch the developments or events in the economy that otherwise might skew the real performance picture and lead a researcher to draw false conclusions. There are different theories on how to perform this matching procedure. We follow the previous work from Barber & Lyon (1996), and use two benchmark samples: One match based on size and one based on performance. Furthermore we contemplated to follow the method from Rosenbaum & Rubin (1983), who uses propensity score to derive an appropriate benchmark. This method did however not produce any usable result. This leaves us with two sets of benchmarks that will be applied in our Difference-In-Difference model. A more detailed description regarding the matching procedure will be provided in section 5.3. 4.2.7 Mean comparison test As with the short-term event study, we are also interested in investigating if the performance changes differ between SEO motives. The comparison is done through a pairwise comparison of the estimated DID regression coefficients of the SEO dummy variables across models. Thus the SEO coefficient from the DEB regression will be tested against the SEO coefficient from the GEN and INV regression and so on. More specifically we use the Wald test 18. Note that the SEO variable indicates the relative change in performance compared to the benchmark samples, and the pairwise test is therefore a test of difference between the relative performance changes. By using the SEO coefficient the effect of the control variables has been taken into account. 4.2.8 Challenges of long-term performance study The long-term study relies heavily on the parallel trend assumption, which basically states that the firms within our sample and the two benchmarks should be affected by the same things over time. Even though we assume this argument to hold, we still suggest that there might be some dissimilarity between the observations. We have included firms from 16 different countries spread across 11 years, and thus assume that the firms are affected by the exact same economic and political initiatives across borders. However since we don t match 18 Practically we run each DID regression in Stata, store the results and then test the coefficients using Stata s Suest and test functions. Page 42 of 101

on country, either sample or match might be affected by country specifics. In general we do however assume that the parallel trend assumption holds. Since we are interested in measuring the effect a SEO has on firm performance, no confounding effects should be present, meaning that the performance after a SEO should be isolated from other events. Since we have included six years of data, three year ex-ante and three years ex-post, one would get the idea that there potentially are some confounding effects that could affect firm performance. However, since it is almost impossible to control for confounding effects, and since it is not practice in previous studies to try to control for them, we assume that our result are not biased by this. 4.3 Use of proceeds study To test whether the sample firms actually use the issue proceeds from the SEO s as they announce, we use a method developed by Kim & Weisbach (2008). The overall purpose of the method is to assess how the proceeds from SEO s affect the balance sheet items or the cash flow measures post-seo. We observe these variables over a four-year period from the issue year to three years after. We use OLS regressions with balance sheet item changes and accumulated cash flows measures post-seo as dependent variables. On all regressions the total proceeds from the SEO and all other sources of income are used as explanatory variables, which means we thereby estimate whether or not the SEO proceeds are used for the given dependent variable. The dependent variables reflect possible uses of funds such as investments, stocking of cash and reduction in debt. By using this model we are furthermore able to estimate how an increase of one EUR in SEO proceeds affects these variables. As with the long-term study we add a number of control variables to the regression specification. More specifically the OLS regression model in question is specified in the below equation ( 11 ). Page 43 of 101

[( ) ] [( ) ] [ ] ( 11 ) where [( ) ] [( ) ] for t = 0, 1, 2 and 3 after the SEO. Source: Kim & Weisbach (2008), own contribution The SEO proceeds is the capital raised in the offering, while other sources is any income from operations, sale of assets and issuance of new debt, thus constitutes any other income in the SEO year and the year following, besides the issue proceeds. SEO proceeds are calculated in the year of the SEO, while Other Sources are accumulated throughout the years. We examine changes in total assets and cash & short-term investments from the fiscal year ending prior to the SEO and forth. Moreover we examined the accumulated cash flows from capital expenditures, acquisitions, R&D and reduction in long-term debt from the SEO and forth. Worth noticing is that the reduction in long-term debt is not off-set by any newly issued debt, as new issued debt is included in other sources of income. The variables total assets, capital expenditures, acquisitions and R&D should give us insights in any investments stimulated by the SEO. Long-term debt reduction should give an indication on any debt repaid with the issue proceeds, while cash & short-term investments shows if any issue proceeds are held as cash. All variables are normalized by total assets in the fiscal-year ending prior to the SEO. Furthermore they are transformed with the natural log of the variable plus one. This is done to accommodate for outliers and to follow the method of Kim & Weisbach (Kim & Weisbach, 2008). The test results are presented in section 6.4. Page 44 of 101

5 Data This section depicts the process undergone, along with relevant choices, for collecting the data that constitutes the final sample of seasoned equity offerings. The section also covers the matching procedure and comments on the final data set. 5.1 Sample Collection The main sources that have been used for collecting data are the Bloomberg Professional Service database; also known as the Bloomberg Terminal, the Thomson Reuters Datastream database and the Orbis database. The Bloomberg database covers all listed firms in the world with information on accounting data, detailed information on IPO s and SEO s and other firm specific information with varying degree of details. We mainly used this database as our data source for SEO information s and accounting data. Datastream contains daily data on share prices and indices. We used Datastream as our source for daily stock prices and market index prices. The Orbis database (By bureau Van Dijk) contains data on firm financials, related news and other specific information on nearly 150 million listed and unlisted firms. We used Orbis as a supplementary source for accounting data and furthermore to check the quality of some of the data provided by Bloomberg. The process of collecting the final sample was done in two stages. Firstly, the primary criteria for the final sample of SEO s was filtered directly through Bloomberg, and secondly, a manual process was undertaken to remove entities that did not match the final sample criteria and which could not be filtered out in Bloomberg. The initial list of SEO s was obtained through Bloomberg by setting the following criteria s. First, we required Bloomberg only to list occurrences were an additional offering had been issued, which contained an amount of primary shares. This ensured that the offering contains an amount of newly issued primary shares, and thus generates cash to the firm, as opposed to secondary shares. Secondly, the time frame of the sample has been set to include SEO s announced from 01/01/2000 to 31/12/2010. Thirdly, we required the firm to be listed on one of the following countries main market: Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Luxembourg, Netherland, Norway, Portugal, Switzerland, Spain, Sweden and Great Britain. This first extraction resulted in 2.957 SEO s, of which 2.319 occurred in the UK. The reason for this relatively large proportion of SEO s in the UK is due to Page 45 of 101

the Bloomberg Terminal not separating the UK main market listings from the AIM listings 19. This blending was not the case with the remainder of the countries. On a large proportion of the UK stocks we were able to separate the main market listings from the AIM, by extracting segment codes, specific for the London stock exchange, for each stock which revealed its listing. The remaining stocks were crosschecked with a list of listed firms, provided monthly by the London Stock Exchange (LondonStockExchange.com, 2014). This separation resulted in 515 SEO s performed on the UK main market and thus 1613 observations from the AIM market were removed. We were unable to identify the correct market for 191 observations, and as a consequence these were also removed. The above steps resulted in a sample of 1153 SEO s. We then removed cases were the status of the SEO was either withdrawn, pending or postponed. Due to regulatory capital requirements in the financial and utility industry, 276 SEO s in these industries where also removed. Furthermore, observations with an offer size of 0, observations that appeared as duplicates and observations with missing ISIN numbers were removed. To avoid overlap we follow the same procedure as among others, Loughran & Ritter (1997) and Walker & Yost (2008) and exclude observations where a firm has made an IPO or SEO within three years before or after our observation. Thus, for an observation to remain in the sample we require that the observation is the only SEO by a firm within three years before and after. This is quite a strict criterion, which excluded 373 observations, but was done to ensure that we reliably could categorize the individual observations, and to ensure that our observations and results were not biased by previous or subsequent SEO s. On the other hand this might lead to bias within our sample, since we remove certain type of firms, i.e. firms that issue equity on a regular basis. Lastly, we removed 65 observations were no stock data were available and 12 observations with underlying thinly traded stocks i.e. traded less than 40% 20 of the 200 days prior to the announcement. During our examination of intended use of issue proceeds, we excluded a number of announcements that appeared not to be SEO s, but instead A to B shares conversion and the likes. Moreover by investigating some of the SEO prospects, we were able to detect that Bloomberg in some cases provided wrong SEO announcement dates. In the 19 The Alternative Investment Market (AIM) is a sub-market of the London Stock Exchange, with a less restrictive regulatory system, allowing growth firms to raise capital without the same regulatory burden as fully regulated stock markets. 20 Stocks that trade less than 40% are regarded as thinly traded stocks by Bartholdy et al.(2007) Page 46 of 101

cases where we were able to detect a wrong date, we corrected it on the basis of the prospectus. Our final sample for the short-term analysis thereby consists of 342 SEO s. For our long-term operational performance study we also require available accounting data for at least one year ex-ante and ex-post. Bloomberg was able to deliver most of this data and were data was missing we crosschecked with Orbis to find this missing data, if available. On 19 observations we were not able to find any accounting data in the years either prior or post the SEO, and as a consequence these observations were removed. The final sample for the long-term analysis hereby consists of 323 observations. For a overview on the sample collection procedure see. 5.2 Use of issue proceeds grouping As we aim to investigate any effects of the issuing firms stated intended use of issue proceeds, we extracted this information from Bloomberg, who had already classified each SEO into the intended use of issue proceeds based on the SEO prospectus. This information was however not available for all observations, and we were only able to extract this information from Bloomberg on approximately 50% of our observations. Some observations only had one intended use of issue proceeds assigned while others had multiple, thus on a large proportion of our observations we manually assessed the intended use of issue proceeds. This was done, primarily by examining the filed prospectus on the Bloomberg database, corporate websites and local exchange websites, as no complete database in Europe exist. If the prospectus was not available, we studied press releases regarding the SEO s and determined the intended use of issue proceeds from this. We follow the same classification for intended use of proceeds as Walker & Yost (2008) and Autore, Bray & Peterson (2009) to make our results comparable and to ensure large enough subsample sizes. The three classifications are as follows: debt repayment, investment and general corporate purpose. SEO s classified as debt repayment are those that state that the majority of the proceeds will be used for repaying debt obligations (DEB). Investments issuers state that the majority of the proceeds will be used for investments, be it M&A, R&D or investment in assets (INV). Lastly, general corporate purpose issuers neither state investment nor debt repayment, but simply state general corporate purpose or continuing operations as Page 47 of 101

intended use of issue proceeds (GEN). If the issuers listed several intended uses of issue proceeds we classified the observation by the intended use of the majority of the issue proceeds of the given SEO. 5.3 Matching procedure In order to determine whether SEO issuers operational performance is better or worse than non-issuers, a benchmark must be determined. This benchmark will allow us to apply the Difference-In-Differences method and thus measure any change between pre-seo and post- SEO performance. Previous studies on operational performance of SEO firms have mainly followed the relatively simple matching procedure proposed by Barber & Lyon (1996). This matching algorithm uses pre-event characteristics such as industry, size and performance to find a matching firm with similar characteristics. The choice of matching criteria does however vary between the studies. Another not so widely used matching procedure is the multi-dimensional propensity score matching proposed by Rosenbaum & Rubin (1983). They use propensity score matching, a multivariate matching technique, to determine the probability of a firm being treated i.e. conducting a SEO, on the basis of the treated groups pre event characteristics (Rosenbaum & Rubin, 1983). As previously mentioned we were not able to appropriately match using the propensity score method, and thus we choose to use two matching samples in our study based on the method by Barber & Lyon (1996). In general, for a firm to be considered a potential match, we first require that the firm does not issue equity during the event window in which it is matched. Secondly, we require at least five years of accounting data. This ensures that we have at least one year of data either exante or ex-post. Practically we extracted all firms listed on the exchanges included in our study and cross referenced these with the list of SEO s and AIM listings. This left us with 2.328 unique firms as potential matches during our period from 2000 to 2010, varying from 1.491 to 2.047 potential matches per year. Furthermore when conducting the match, we require that a non-issuer only once can be a match. 5.3.1 Matching algorithm The matching procedure suggested by Barber & Lyon (1996) is fairly simple. They propose a matching algorithm by matching the sample firms to firms within the same two- or one-digit Page 48 of 101

SIC code 21, similar asset size or similar performance. This matching method is widely used in other studies regarding operating performance of SEO issuers ( (Andrikopoulos, 2009), (Loughran & Ritter, 1997)). We choose the following two algorithms proposed by Barber & Lyon (1996), and reach two 100% matching samples, i.e. we find a match for each sample firm: 1. Industry and size match, 2. Industry and performance match. The first matching algorithm requires that a match had to be in the same industry, based on the two-digit NACE code 22. Secondly, the match had to be of similar size. This was based on book value of assets one year prior to the SEO (t=-1), which had to be within 70%-130% of a given sample firms book value of assets. If no match was available we loose the size constraint and find a match within the same two-digit NACE code and asset size closest to the given sample firm. First, by matching on industry, we both prevent any performance differences being explained by industry specific events since we assume that operating performance is different between industries. Secondly, by adding the size criteria we also imply that operating performance is size depended, as documented by Fama & French (1995). The second matching algorithm again requires a match with the same two-digit NACE code. We also require a matching firm to be within 90%-110% of return on assets, three years before (t=-3) the SEO of a given sample firm. If we cannot find any match, we continue with one-digit NACE code and same performance constraints. If still no match is available we drop the NACE code and match only on performance and lastly we drop the 90%-110% constraint and find the nearest return on assets match available. By matching on performance three years prior to the SEO we try to control for any mean reversion effects due to under- or overperformance of a firm before the SEO 23. Furthermore, Barber & Lyon find that test statistics are best defined when matching with pre-event performance (Barber & Lyon, 1996). Both 21 We will be using one- and two-digit NACE codes, which is the European equivalent of SIC codes. Thus a twodigit NACE code is more detailed than a one-digit NACE code. 22 See Appendix D for description of Nace codes 23 Briefly this means that if a firm has performed well before an event, and performance drops after the event a researcher would have the tendency to conclude that the firm experiences poor performance, even though the performance simply might revert to its true mean (Barber & Lyon (1996)). Page 49 of 101

matching methods are performed with the use of Visual Basics for Application in Excel. The VBA Excel file can be found on the enclosed CD and scatterplots of the final matches compared to our sample are available in Appendix B. 5.3.2 Winsorizing All regressions have been run with the original dataset as well as a dataset with winsorized data. The original dataset seems to be skewed by extreme outliers, and thus we have chosen only to show the results from the winsorized data in section 6. Basically all accounting items within our two samples are winsorized at the 1% and 99% fractile, meaning that all values below the 1% and above the 99% fractile are set equal to the value of the 1% and 99% fractile value. This might cause some bias, since we adjust extreme observations to the lowest and highest fractile. The observations might be real observation, however the effect of the few outliers effect our test statistics and results which could cause a type I error. However, to accommodate for the outliers, we use the un-winsorized dataset for the quantile regression, as this method is robust to non-normality in the dataset. 5.4 Comments on final data set Due to our requirement of at least one year of post-seo accounting data, we implicitly exclude firms that go bankrupt in the year of the SEO or the year after, and therefore do not report any income statement in the post period. This was the cause for 13 of the firms that we excluded from the short-term sample, while the remaining 6 were subject to an acquisition. To staunch further survivorship-bias, our sample contains firms that go bankrupt, are delisted or are acquired during the post SEO period, but who report at least one year of accounting data. A minority of the observations in the final sample suffers from their accounting period being changed during the event window. This practically means that some of the income statement data covers e.g. 9 or 15 months and not the normal 12 months, and therefore are larger or smaller than what a full year would be. This has been corrected by manually multiplying the income statement data with a factor such that the data reflects a 12-month period. As previously described Bloomberg provided us with the announced SEO dates. However by further investigation we discovered that some of the proposed announcement dates provided from Bloomberg where wrong, typically deviating a couple of days from the actual Page 50 of 101

announcement. Since we have not gone through all SEO prospects, there is chance that there still might be a few dates that are incorrect, which could bias our short-term event study. However since the dates typically only deviate a couple of days we, as discussed in section 4.1.2, have included longer event windows, which should capture the announcement effect. 6 Empirical results The aim of this section is to give an overview of the final data set as well as the results of the test statistics. First the characteristics of the final sample will be described. This is followed by a detailed description of the descriptive statistics through the Difference-In-Difference model, containing pre- and post-seo characteristics as well as the DID estimates which has been described in section 4.2.1. At last the OLS regression on the use of issue proceeds will be presented. 6.1 Sample characteristics As described in section 5.1 our final sample consists of 342 firms who conduct a SEO between 2000 and 2010. The figure below provides an overview of the sample firms in terms of the year of the SEO announcement with respect to the intended use of issue proceeds, which is described in section 5.2. Figure 2 - Sample overview 70 60 DEB GEN INV 50 40 30 20 10 0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 The data is based on the final data set for the short-term event study, which consists of 342 sample firms. The firms are divided by announcement year and SEO motive. Source: Bloomberg, Own contribution Page 51 of 101

We see that the number of SEO s is quite steady throughout different time periods. From 2000 to 2004 we experience almost the same level of SEO s each year, ranging between 16 and 26 SEO s a year. From 2004 the number of SEO s increases, jumping from 16 to 37 in 2005. This number is relatively stable ranging between 24 and 39 until 2010. In 2009 we do however see a substantial higher number of SEO s, where 65 were announced, more than doubling the number from 2008. The number of SEO s in 2009 constitutes 20% of the final sample. We see that the main motives for conducting a SEO in 2009 are general corporate purposes and repayment of debt, whereas in all other years the main SEO motive was investment purposes. The number of SEO s in 2009 could indicate that the profit of firms have been put on pressure during the financial crisis, which could indicate that more firms than usual are in the need of liquidity, which could explain the increase in the SEO motive GEN. Table 2 - Offer size & offer size scaled by assets in mio. EUR Scaled by assets Offer size Total Median Average Median Average 2000 12.548,16 128,35 538,82 0,44 0,97 2001 11.910,09 63,04 515,23 0,24 0,39 2002 2.666,33 65,98 179,55 0,07 0,11 2003 1.589,42 61,38 179,62 0,08 0,49 2004 1.620,03 22,00 213,18 0,09 0,24 2005 3.153,99 32,71 135,76 0,16 0,26 2006 4.023,17 42,57 172,26 0,13 0,27 2007 3.609,88 40,92 140,42 0,16 0,53 2008 1.777,23 37,92 151,41 0,06 0,37 2009 7.736,59 33,57 149,92 0,04 0,11 2010 1.285,04 25,62 102,97 0,13 0,22 The figure represents the offer size of all SEO s in mio. EUR. Scaled by assets represents the offer size scaled by assets in t=-1. All numbers are based on the final sample from the short-term event study. Source: Bloomberg, own contribution If we look at actual offer size in millions of EUR in the above Table 2 we see a large total offer size in the years 2000 and 2001. Moreover when looking at the median and averages this might indicate that the offers made in these two years have been relatively larger compared to the other years. We get about the same picture when we look at the median and average offer size scaled with assets, indicating that the offers made were quite large relative to firm size. Looking at the rest of the years we in general do not see a greater consensus between neither the offer size nor the offer size scaled by assets, which suggests that the actual offer Page 52 of 101

size and the size of the offer in relation to the firm size vary much across years. Worth noticing is though the year 2009, where we as shown in Figure 2 see a large number of SEO s, more than doubling from the previous year. We see that the offer size in millions of EUR in 2009 is higher compared to the other years, due the higher number of SEO s. However we do see an average and median that is below the average of the other years. Moreover when we scale the offer size by assets, we see that even though there has been a higher number of SEO s in 2009, the offer size relative to firm size is relatively small. Table 3 - SEO s divided by country and industry A B C D F G H I J L M N P Q R Total AT 1 3 1 1 1 7 BE 2 2 1 2 7 CH 1 5 2 1 9 DE 1 20 3 5 4 1 2 36 DK 2 1 1 1 1 3 1 1 2 13 ES 1 1 2 FI 1 7 1 1 10 FR 12 1 1 1 1 7 6 2 1 32 GB 2 17 36 1 10 19 3 5 25 1 7 6 1 2 5 140 IE 1 2 3 IT 5 1 1 7 LU 2 1 1 4 NL 3 18 6 3 2 4 36 NO 7 4 3 4 18 SE 2 9 1 1 4 1 18 Total 4 33 12 6 16 29 14 7 55 1 27 8 1 7 9 342 5 The table presents the numbers of SEO s divided by country and industry. Portugal is not represented in this table since our final sample did not include any firms from Portugal. Source: Bloomberg, own contribution When looking at the SEO distribution by country, it is relatively easy to see that some of the larger countries such as Great Britain, Germany, France and the Netherlands have a tendency to conduct more SEO s than the relatively smaller countries. The reason for this skew distribution could be the larger number of listed firms on the respective stock markets. Moreover a higher liquidity in these stock markets might ease the willingness to conduct a SEO. In total we see that the above-mentioned countries account for about 72% of the total sample, whereas Great Britain alone represents 42%. If we look at the distribution within industry we see an overload of SEO s within industry C, manufacturing, which represents 37% Page 53 of 101

of the total sample, whereas the second largest industry J, Information and communication, represents about 16%. 6.2 Short-term results As mentioned in section 4.1, the purpose of the short-term study is to capture the market reaction and any value destruction of a SEO. Our analysis will be based on the cumulative abnormal returns, CAR, with the test statistics discussed in section 4.1.5. Descriptive statistics regarding the total sample in different event windows can be seen in Table 4. Furthermore, all data and tests regarding the short-term event study are available on the enclosed CD. Table 4 - Descriptive statistics CAR [-1;+1] CAR [-3;+3] CAR [-10;+10] Mean -1,19% -1,31% -2,71% Median -1,12% -1,06% -1,69% Std. Dev. 0,089 0,104 0,171 Skewness 0,538 0,414-0,180 Kurtosis 11,972 6,975 6,189 Source: Own contribution From the descriptive statistics we see some indications that firms that offer seasoned equity do in fact experience negative abnormal returns around the announcement. Thus it seems that the offering is seen as value destroying from the perspective of the financial markets. Depending on the event window, the mean CAR is between -1,19% and -2,71%, and the median CAR ranges between -1,06% and -1,69%. From Table 4 we observe positive skewness for the three- and seven day event window, and negative skewness for the 21-one day event window, whereas we observe excess kurtosis for all three windows. This indicates a fat tailed distribution and that the observations are not normally distributed, which will lead to more type 1 errors; rejection of the null hypothesis when it is in fact true (Bartholdy, et al., 2007). We confirm the indication with the Shapiro-Wilk test, which strongly rejects normality in our data. The results of the Shapiro-Wilk test are available in Appendix E. Unlike the parametric tests, the non-parametric tests are not based on the assumption of normality, and thus the non-parametric tests are given more weight in our analysis. Page 54 of 101

In order to give a visual overview of the development in CAR the cumulated abnormal returns during the event of 21 days is shown in Figure 3 below. Figure 3 - Cumulative abnormal returns [-10;+10] 2% 1% 0% -1% CAR Event day -10-9 -8-7 -6-5 -4-3 -2-1 0 1 2 3 4 5 6 7 8 9 10-2% -3% -4% ALL DEB GEN INV The graph shows the average cumulative abnormal returns for the complete sample (bars) and divided into the DEB, GEN, and INV (lines) sample in a 21-day event window. Source: Own contribution By looking at the figure it is quite obvious that the announcement has a negative effect on the stock price, for both the full sample as well as the subsamples. For the full sample and INV subsample we observe small movements close to zero, in the ten days prior to the announcement. For the DEB and GEN subsamples we observe larger fluctuations in the days prior where the DEB subsample seems to have negative abnormal returns and GEN positive. However, none of the four samples show any sign of information slip prior to the announcement, as the first pronounced drift occurs around day -1. In the days following the announcement, we observe quite different movements depending on the subsample. The DEB subsample experiences a dip at the event day, before returning to its pre-announcement level again within two days. For the GEN and INV sample, we however see negatively increasing CAR s from the day prior to the announcement and continuing in the 10 days afterwards. This continuing negative drift leads to some doubt regarding the efficient market hypothesis, as the CAR should have stabilized post-announcement, whereas it seems as the market are longer to incorporate the new information. This further supports the inclusion of longer event windows. Page 55 of 101

As mentioned in section 4.1.5, we employ a battery of parametric and non-parametric tests, to test the significance of the announcement effect. The null hypothesis of all the test-statistics are that the announcement of a SEO does not lead to significant negative abnormal returns around the announcement. In Table 5 we present the test results for the full sample of SEO firms. Table 5 - Short-term test statistics: Full sample Full Sample CAR T1 T2 T3 T4 T5 T6 [-1;+1] -1,19% -3,569*** -4,941*** -4,907*** -3,550*** -1,996** -1,252 [-3;+3] -1,31% -2,566** -3,117*** -3,092*** -2,255** -0,201 0,759 [-10;+10] -2,71% -3,047*** -3,370*** -3,347*** -2,596*** -1,044 0,690 The test statistics are calculated on the basis of the full sample of 342 observations. ***, ** and * indicate significance at a 1%, 5% and 10% level. Source: Own contribution As indicated by Table 5, the results give more evidence to a negative market reaction to the SEO announcement. Within the three-day event window, we find a CAR of -1,19%, which based on all of the parametric and two of the non-parametric tests, is a significant negative abnormal return. Since normality-tests indicate that our data is not normally distributed, we emphasize the results of the non-parametric test more. Here we see that only the T6-test does not show a significant negative CAR. T4 and T5 show significant negative abnormal returns and as T4 in general performs better than the other non-parametric tests, we conclude that firms experience significant negative abnormal returns in the three-day event window 24. When expanding the event window to the seven and 21-day event window we find a negative CAR of -1,31% and -2,71% respectively, however the results are not as robust as previous due to lower rejection levels 25 and insignificance in some of the test. The parametric tests all still show significance with rejection levels at 1%, except for T1 within the seven-day window, which only can be rejected at 5%. The non-parametric tests T5 and T6 show an insignificant negative and positive CAR respectively. The non-parametric T4-test however still rejects at 5% and 1% for the seven- and 21-day event window. The significant negative abnormal 24 According to Bartholdy et al. (2007), when doing CAR analysis the non-parametric Rank (T4) and standardized parametric tests (T2 & T3) have reasonable power and size to identify abnormal returns above 2%. Corrado & Zivney (1992) also finds that the Rank test dominates the sign and t-tests in detecting abnormal performance (Corrado & Zivney, 1992). 25 A lower rejection level is defined as a higher significance level and vice versa. Page 56 of 101

returns around a SEO announcement are in line with- and of approximately same magnitude as previous empirical findings. Moreover the findings are consistent with most of the described theories in section 2.2, except for the trade-off theory on optimal capital structure. The significant findings follow our expectations and thus support our H1 hypothesis of significant abnormal returns around the announcement of a SEO. For the remainder of the section we split the sample into the DEB, GEN and INV samples defined in section 5.2, for further analysis of the SEO announcement. Table 6 - Short-term test statistics: Debt repayment DEB CAR T1 T2 T3 T4 T5 T6 [-1;+1] -0,48% -0,484-0,005-0,006 0,566 0,799 1,039 [-3;+3] 0,39% 0,483 0,620 0,617 1,016 1,410* 1,936** [-10;+10] -0,98% -0,517-0,432-0,430-0,031 0,187 1,010 The test statistics are calculated on the basis of the DEB sample of 81 observations. ***, ** and * indicate significance at a 1%, 5% and 10% level. Source: Own contribution The test results for the DEB subsample shown in Table 6 indicate an insignificant announcement-effect of -0,48% and -0,98% for the three- and 21-day event windows. On the other hand the seven-day event window shows a positive CAR with significance at 10% and 5% for T5 and T6, thus not a very strong rejection level. As opposed to the previous findings, we do not find significant negative abnormal returns. The theories are more unambiguous in relation to the short-term market reaction when the issue proceeds are used to repay debt. Our results seem to follow the indications given by the adverse selection models of Myers & Majluf (1984) and Cooney & Kalay (1993) as well as the signaling model of Miller & Rock (1985), who argue that equity issues intended for debt repayment is free of any negative investment signal. In regards to the capital structure theories, our results do not find support for any of the discussed theories in section 2.2.1. The findings support our H1A hypothesis, thus issuing equity with the purpose of repaying debt does not seem to result in significant abnormal stock returns. Page 57 of 101

Table 7 - Short-term test statistics: General corporate purpose GEN CAR T1 T2 T3 T4 T5 T6 [-1;+1] -2,22% -3,580*** -4,540*** -4,520*** -2,642*** -1,305* -0,871 [-3;+3] -2,08% -2,214** -2,296** -2,279** -0,613 0,806 0,634 [-10;+10] -3,92% -2,352** -3,069*** -3,048*** -2,064** -1,211-0,492 The test statistics are calculated on the basis of the GEN sample of 107 observations. ***, ** and * indicate significance at a 1%, 5% and 10% level. Source: Own contribution When turning our focus to the results of the GEN subsample presented in Table 7, we immediately see a different picture. The abnormal returns for the three-day event window is negative, -2,22%, and significant at 1% for all tests except the T6, whereas T5 is significant at a 10% level. For the seven-day event window all parametric test show a significant negative CAR of -2,08% with rejection levels at 5%. None of the non-parametric tests do however show significance for the seven-day event window. The 21-day window provides similar results, showing significance on all parametric tests, but also for T4 with rejection levels between 1% and 5%. As also depicted by Figure 3, the negative abnormal returns continue to increase expost and results in a CAR of -3,92% in the 21-day event window. As mentioned, the market does not seem to be able to incorporate the announcement fully within the shorter event windows. Our results are in line with previous research both in terms of magnitude and direction when firms issue equity for general corporate purposes. This significant negative reaction is consistent with the adverse selection models of Myers & Majluf (1984) and Cooney & Kalay (1993), the signaling model of Miller & Rock (1985) and the free cash flow theory of Jensen (1976). With the superior performance of T4 in mind, we conclude that firms specifying general corporate purpose as the intended use of issue proceeds, experience abnormal negative returns around the announcement. Thus we find support for our H1B hypothesis. Table 8 - Short-term test statistics: Investment INV CAR T1 T2 T3 T4 T5 T6 [-1;+1] -0,86% -1,996** -3,575*** -3,541*** -3,901*** -2,744*** -1,898** [-3;+3] -1,67% -2,521** -3,181*** -3,155*** -3,425*** -2,143** -0,805 [-10;+10] -2,77% -2,291** -2,151** -2,136** -2,388*** -0,795 0,704 The test statistics are calculated on the basis of the INV sample of 154 observations. ***, ** and * indicate significance at a 1%, 5% and 10% level. Source: Own contribution Page 58 of 101

For the INV subsample our results show a significant negative abnormal return of -0,86% in the tree-day event window for both the parametric- and non-parametric tests. As we expand the event windows, the negative CAR increases, again suggesting a longer incorporation time of the new information. The seven-day window is rejected by all, but the T6 test, while the 21- day event window is rejected by the parametric tests and the non-parametric T4. As previous findings, our results also show a negative market reaction, however the abnormal returns in the three-day event window is not of the same magnitude. Our results are in line with the adverse selection model of Myers & Majluf (1984), the signaling model of Miller & Rock (1985) and the free cash flow theory of Jensen (1976). According to Cooney & Kalay (1993), the negative market reaction indicates that the projects are worse than market expectations. Overall we therefore conclude that firms announcing a SEO for investment purposes experience negative abnormal returns in all three windows, which supports our H1C hypothesis. 6.2.1 Means comparison of motives In the above-presented tests we have examined abnormal returns around the announcement of a SEO. The results indicated that the magnitudes of the market reactions are different, as also shown by the studies of Gajewski & Ginglinger (2002) and Hull et al. (2009). It is therefore appropriate to test whether the means of the abnormal returns are significantly different between the three motives. We therefore employ the parametric one-way ANOVA and the non-parametric Kruskal-Wallis 26 test to test for significantly different means in the subsamples. These tests do however not specify which motive is different, and thus the Bonferroni and LSD confidence intervals are applied. In Table 9 below the test results for the one-way ANOVA and Kruskal-Wallis tests are presented. Table 9 - Difference between subsamples ANOVA F-test p-value Kruskal-Wallis p-value CAR [-1;+1] 1,07 0,344 4,377 0,112 CAR [-3;+3] 1,45 0,235 2,445 0,294 CAR [-10;+10] 0,68 0,507 3,571 0,168 ***, ** and * indicate significance at a 1%, 5% and 10% level. Source: Own contribution 26 The Kruskal-Wallis test is the strongest of the two mean comparison test, when the sample is not normally distributed Page 59 of 101

The test results show no significant difference between the subsamples, as both the one-way ANOVA and the Kruskal-Wallis tests are unable to reject that the means are different across the subsamples. Thus in an overall comparison the three motives for SEO s do not have different means in the abnormal returns. However, our test results showed that the DEB subsample did not experience significant negative abnormal returns, whereas the GEN and INV subsamples did. Despite this both the LSD and Bonferroni test reject any significant difference between the abnormal returns in a pairwise comparison. The results for the LSD test are reported in Table 10 and confidence intervals for Bonferroni and LSD can be found on the enclosed CD. Hence our results do not find support for our H1D hypothesis, stating significant differences between the abnormal returns for the different SEO motives. This contradicts the findings of Gajewski & Ginglinger (2002) and Hull et al. (2009). The results are however in line with Walker & Yost (2008) and Masulis & Korwar (1986), who do not find any significant differences between the subsamples. Even though our results show no significance, the relatively low p-value for the DEB vs. GEN subsample might suggest some difference. This could be a slight indication that when issue proceeds are used for debt repayment, the announcement is free from any negative signals as stated by Myers & Majluf (1984) and Miller & Rock (1985). Table 10 - Pairwise difference between subsamples p-value DEB vs. GEN DEB vs. INV GEN vs. INV CAR [-1;+1] 0,188 0,760 0,227 CAR [-3;+3] 0,109 0,152 0,755 CAR [-10;+10] 0,244 0,447 0,593 The table reports the p-value of the Least Significant Difference (LSD) confidence intervals. ***, ** and * indicate significance at a 1%, 5% and 10% level. Source: Own contribution 6.2.2 Cross-sectional analysis of abnormal returns For the purpose of examining whether any other factors influence the abnormal returns we employ a cross-sectional regression. All three event-windows will be analyzed using the full sample as well as each subsample. For the purpose we include a number of variables that theoretically could have an effect on the abnormal returns. These include the size of the offer proceeds relative to the firm size, as a larger issue ceteris paribus should have a larger Page 60 of 101

negative effect as argued by Krasker (1986) and Miller & Rock (1985). Moreover we include this variable since we, as shown in Table 2, see a variation in the offer size across years. We include a dummy variable for secondary shares that, according to the signaling theory of Leland & Pyle (1977), indicate an insider selling of shares and thus should signal an overvalued stock. We also include a FCF variable that follows Jensen s Free Cash Flow theory, and thus a positive FCF 27 should have a negative effect on the abnormal returns. Debt to total assets measures the leverage and follows the theory that higher leverage is associated with higher firm quality. For low quality firms, high leverage would be costly to imitate due to bankruptcy costs etc. as proposed by Ross (1977). Therefore, firms with higher leverage should experience less negative returns around the announcement. Lastly, age is included as older firms theoretically are of higher quality and have gained more investor confidence than younger firms as suggested by Frank & Goyal (2003) and Diamond (1989). We observe some problems with heteroskedasticity and therefore apply heteroskedastic robust standard errors 28. The results of the cross-sectional regressions are presented below in Table 11. If we start examining the model it is worth noticing the relatively low explanatory power, as indicated by R 2, thus our included variables do not explain much of the variation in CAR. Generally we only find weak evidence for all included variables in all three event windows, besides relative SEO offer size. The difference in the number of observations in respect to the short-term event study is due to lack of financial data. 27 Note that this FCF measure differs from the performance measure applied in the long term. This measure includes interest payments and taxes. 28 See enclosed CD for further details Page 61 of 101

Table 11 - Short-term cross-sectional regression Dependent Sec. Motive Constant Offer size Age FCF D/TA N R variable shares 2 CAR [-1:+1] ALL -0,019-0,011* 0,000 0,008* -0,009-0,053* 314 0,025 (0,020) (0,006) (0,022) (0,005) (0,030) (0,025) DEB -0,016-0,131* 0,079 0,011 0,181-0,056 (0,045) (0,068) (0,058) (0,009) (0,231) (0,085) GEN 0,01-0,007-0,064* 0,000-0,003-0,092** (0,041) (0,010) (0,038) (0,010) (0,028) (0,035) INV -0,010-0,009 0,018 0,004-0,039-0,032 (0,025) (0,009) (0,026) (0,006) (0,059) (0,034) CAR [-3:+3] ALL -0,033-0,009-0,008 0,012* 0,008-0,036 (0,023) (0,016) (0,031) (0,006) (0,036) (0,031) DEB 0,013-0,201** 0,096 0,016 0,028-0,134 (0,048) (0,091) (0,087) (0,011) (0,204) (0,094) GEN -0,049 0,029-0,068 0,016 0,007-0,078 (0,049) (0,024) (0,045) (0,013) (0,039) (0,051) INV -0,005-0,021* -0,020-0,002-0,031 0,034 (0,031) (0,012) (0,033) (0,009) (0,063) (0,046) CAR [-10:+10] ALL -0,028-0,042 0,043 0,009 0,072-0,056 (0,036) (0,038) (0,042) (0,010) (0,059) (0,047) DEB 0,038-0,067 0,032 0,010 0,038-0,232* (0,077) (0,114) (0,110) (0,018) (0,341) (0,118) GEN -0,082 0,045 0,060 0,015 0,029-0,037 (0,085) (0,034) (0,085) (0,021) (0,057) (0,097) INV -0,039-0,088*** -0,029 0,011 0,013 0,038 (0,046) (0,020) (0,034) (0,014) (0,092) (0,076) 78 0,116 96 0,115 140 0,019 314 0,025 78 0,130 96 0,088 140 0,047 314 0,047 78 0,051 96 0,053 140 0,252 The table presents the cross-sectional regression on CAR: The dependent variables are the CAR s for the three-windows divided by the full sample and subsamples. Offer size is total proceeds from the SEO divided by total assets in the year prior to the SEO (t=-1). Secondary shares is a dummy variable equal to 1 if the offering includes secondary shares and 0 otherwise. Age is the natural log (ln) of firm age in the SEO year. FCF is the Free Cash Flow divided by total assets in t=-1. D/TA is the leverage in t=-1 calculated as debt to total assets. Heteroskedastic robust standard errors are reported in parentheses (). ***, ** and * indicate significance at a 1%, 5% and 10% level. Source: Bloomberg database, Datastream database and own calculations Page 62 of 101

Examining the results for the total sample, we find that both relative offer size and leverage are significant at the 10% level, where both variables have a negative effect on CAR in the threeday event window. In the other event windows, these measures still show a negative impact on CAR, however both measures are insignificant. If we look at age, this measure on the other hand has a positive effect on CAR, again at a 10% significance level for the three and seven-day event window and an insignificant positive effect on CAR in the 21-day event window. Thus as expected, the results seem to indicate that a larger SEO offer does entail more negative CAR s while older firms generally experience less negative CAR. However, with the low significance level the results are not strong enough to make any definitive conclusions. Secondary shares and FCF all show an insignificant effect on CAR, with no consensus of a positive or negative influence of each of the measures. For the DEB subsample we only find a significant negative effect for the relative size of the SEO at 10% and 5% for the three and seven-day window respectively. Moreover leverage proves to have a significant negative effect on CAR in the 21-day event window, however only at 10% significance. The variables for secondary shares and FCF all indicate a positive relationship, which contradicts our projection. The age variable however seem to follow our expectation. For the GEN subsample we find significance for the secondary share dummy at 10%, giving some indication that including sales of secondary shares in a SEO does in fact send a negative signal to the market. The seven-day event window shows an insignificant negative effect on the variation in CAR, whereas the 21-day event window on the other hand shows an insignificant positive effect, thus making the above argument inconclusive. Opposite our expectations, leverage indicates a negative effect on CAR, showing significance at 5% for the three-day event window. Offer size and FCF show no significance, and the sign of the coefficients are ambiguous. Age on the other hand has a positive effect on the variation in CAR, however still insignificant. For the INV subsample we only find significance for offer size of the SEO, at 10% in the sevenday event window and 1% in the 21-day event window. Our results indicate that neither secondary shares, age, FCF nor leverage have any significant effect on CAR around the announcement. Page 63 of 101

Generally the included variables do not provide any explanatory effect on the short-term market reaction. Most of the variables vary in sign across the different event windows, thus making it hard to draw any conclusions. The only variable that shows unambiguous results is the relative offer size of the SEO. Offer size seems to have a negative effect on the short-term market reaction as also stated by Krasker (1986) and Miller & Rock (1985). Overall for our H1E hypothesis we only find support for offer size, whereas we find no support for secondary shares, age, FCF and leverage. 6.2.3 Summary of short-term findings To summarize, we find support for an overall negative market reaction to SEO announcements. Our findings for the debt repayment sample indicate that these are free from any negative signals, as we do not find any negative abnormal returns. For firms issuing equity for general corporate- and investment purposes we find significant negative abnormal returns. Even though we find different results for our subsamples, the differences are not significant in a pairwise comparison, and thus we are not able to conclude that the market reacts different to the announced intended use of issue proceeds. The short-term market reaction should give an indication of future performance. Based on our findings, GEN- and INV firms should therefore experience a decline in operating performance, whereas operational performance for DEB firms should remain unchanged. 6.3 Long-term operational performance results This section will provide an overview of the operational performance in the form of pre- and post-seo firm characteristics, based on the three-year averages of the SEO firms and control firms. The descriptive data is outlined in Table 12 and through the DID table, as described in section 4.2.1, which also includes the DID estimator. The DID table is split in two tables, one that outlines the pre-seo characteristics, and one that outlines the post-seo characteristics including the DID estimator. Furthermore the difference between the characteristics of sample firms and each of the two benchmarks is presented. Lastly, we present the DID-regression results for the total sample as well as the subsamples. Page 64 of 101

Table 12 - Descriptive statistics OROA OROS FCF/TA Mean -0,008 0,018 0,015 Median -0,010-0,003 0,007 Std. Dev. 0,090 0,384 0,091 Skewness 0,759-0,055 0,991 Kurtosis 6,613 15,389 5,463 The table presents the descriptive statistics for the 323 sample firms, meaning control firms are disregarded. Source: Own contribution The descriptive statistics in Table 12 do not seem to indicate that SEO firms in fact do experience a decline in operating performance subsequent to a SEO. Whereas the mean change in operating performance is negative for ORA with 0,08%, OROS and FCF/TA show a positive change in operating performance of 1,8% and 1,5% respectively. Comparing the mean changes in operating performance to the median, we see some differences on the measures OROS and FCF/TA, which could indicate outliers in our dataset. This is furthermore supported by the relative high skewness, and excess kurtosis, which as with the short-term descriptive statistics, indicate that our data has a fat tailed distribution and therefore is not normally distributed. This statement is supported by the Shapiro-Wilk test, which strongly rejects normality in our data. The results of the Shapiro-Wilk test are available in Appendix F. To give a more detailed overview over the change in operating performance, as well as relevant balance sheet items, the next section will present the DID-table, which takes the SEO motives and control firms into account. 6.3.1 Pre and post-seo performance descriptive Table 13 shows that all of our samples do not seem to be of different size compared to the size benchmark, which is fortunate, since we found our control firms by matching on size. Compared to the performance benchmark all of our samples are significantly larger, except for the GEN subsample, which is insignificantly larger. What leaps to the eye is the size of the DEB firms, as this subsample seems to be quite larger than both GEN and INV firms. Taking into account the natural log transformation, the size difference is substantial. Page 65 of 101

Table)13)+)DID)table:)Pre+SEO)characteristics) % % The$table$represents$the$DID$table$that$shows$the$pre1SEO$characteristics$of$the$SEO$firms$and$the$control$firms$within$ the$performance$and$size$benchmark.$the$pre1seo$difference$is$the$difference$between$the$seo$firms$and$control$firms$ within$each$benchmark.$all$the$characteristics$are$three1year$averages$for$each$measure$pre1seo.$each$measure$is$ divided$into$the$three$subsamples.$standard$errors$are$reported$in$parentheses$(),$while$the$number$of$observations$are$ reported$in$brackets$[$].$***,$**$and$*$indicate$significance$at$a$1%,$5%$and$10%$level.$source:$own$contribution$ % When%inspecting%the%three%performance%measures%the%total%sample%is%not%significantly%different% to%either%of%the%benchmark%samples%on%any%of%the%measures.%the%performance%benchmark%was% Page% %66%of%101%!

expected to be alike on OROA due to its nature, however the two other measures seem to follow as well. Overall, DEB and GEN firms have positive OROA, while INV firms are negative. For OROS and FCF/TA, only DEB firms have positive performance, whereas GEN and INV are negative. The DEB firms are not different to either benchmarks on OROA or FCF/TA, while the OROS measure indicates better pre-seo performance, however only significant at 10% for the size match. The GEN firms overall seem to experience worse performance compared to both benchmarks, yet not significantly worse. INV firms stand out by having significant lower pre- SEO OROS compared to both benchmarks, while both OROA and FCF/TA are slightly lower but not significantly. Our results indicate that the DEB firms have better pre-seo performance than both GEN and INV firms. Overall the SEO firms seems to have higher pre-seo leverage ratios, which is supported by the significance on debt to total assets for both benchmarks and debt to equity for the performance benchmark. If we look at the capital structure measures on the subsamples, it is not unexpected that debt firms have higher pre-seo leverage than the other sample firms. However if we isolated look at the debt to equity ratio DEB firms have what seems to be a much higher ratio than GEN and INV firms. Moreover DEB firms also have significant higher ratios for both measures compared to benchmarks. GEN firms have significantly higher pre- SEO debt to total assets ratio while insignificantly lower debt to equity ratio compared to both benchmarks. The INV firms, which have the lowest ratios of our samples, are not significantly different from the benchmarks, however they are slightly lower. At last we focus on the working capital measures, which also indicate differences between the sample firms and the two benchmarks. If we look at current asset to current liabilities we see that INV firms are the firms with the highest working capital, whereas DEB firms are the ones with the lowest. Compared to the benchmarks, the working capital for DEB firms is significantly lower, while GEN firms are significantly lower than the performance match. The INV firms seem to be on par with the benchmarks. The working capital measure cash flow from investing activities to total assets does not provide any significant difference between the total sample, GEN and INV firms when comparing it to the size benchmark. Only the ratio for DEB firms is significantly different at 5%. Page 67 of 101

The full sample and GEN firms on the other hand use significantly more funds on investing activities prior to the SEO than firms in the performance benchmark, with rejection levels at 1% and 5% respectively. Noticeable is that INV firms seem to invest the least relative to assets of the three subsamples. When looking at the cash & short-term investments to total assets ratio, we see that DEB firms have almost half the liquidity ratio than INV- and DEB firms. If we compare this measure across both benchmarks it seems as if the full sample and the three subsamples all have significantly lower pre-seo liquidity ratios, with high rejection levels. Only the size match for GEN firms is not significant. This suggest that SEO firms overall are more in need of liquidity, measured as cash & short-term investments. To sum up the pre-seo characteristics of the sample and control firms in Table 13, there seems to be some differences in some of the parameters between our samples and benchmark firms, but also between our subsamples itself. However, most relevant is that our sample does not seem to be different in terms of performance measures compared to the benchmarks, except from the INV firms low OROS. The differences mainly lie in the capital structure and working capital ratios. Table 14 shows the post-seo firm characteristics as well as the DID estimator, which is described in section 4.2.1. The DID-estimator concludes whether there is a significant difference between pre- and post- SEO characteristics for the SEO firms, relative to the control firms within the two benchmarks. With regards to size, there are indications that SEO firms overall have had an increase relative to the benchmarks as shown by the DID measure. The same is indicated for the GEN and INV firms, the increase is however not significant. As opposed to ex-ante, the GEN firms are now significantly larger than the performance benchmark. The DEB firms have remained relatively similar to the benchmarks. Numerical the DEB firms are relatively unchanged in size, while GEN and INV firms have grown. Page 68 of 101

Table 14 - DID table: Post-SEO characteristics and DID estimator Post-SEO Post-SEO difference Difference-in-difference SEO firms Perf.Match Size Match Perf.Match Size Match Avg. N Std. Error Avg. Std. Error Avg. Std. Error Avg. Std. Error Avg. Std. Error Size measure Ln(TA) ALL 6,333 [323] (0,118) 0,975 (0,167***) 0,389 (0,159**) 0,223 (0,237) 0,176 (0,224) DEB 7,264 [79] (0,233) 1,555 (0,33***) 0,303 (0,292) -0,026 (0,466) -0,019 (0,412) GEN 5,950 [99] (0,200) 0,692 (0,283**) 0,326 (0,272) 0,276 (0,4) 0,177 (0,385) INV 6,087 [145] (0,178) 0,853 (0,252***) 0,480 (0,233**) 0,322 (0,357) 0,281 (0,329) Performance measure OROA ALL 0,015 [323] (0,007) -0,026 (0,010***) -0,013 (0,010) -0,023 (0,014*) -0,007 (0,014) DEB 0,042 [79] (0,012) -0,014 (0,017) -0,005 (0,018) -0,011 (0,025) -0,006 (0,025) GEN 0,016 [99] (0,016) -0,026 (0,022) -0,008 (0,022) -0,016 (0,032) 0,008 (0,031) INV -0,016 [145] (0,015) -0,047 (0,021**) -0,026 (0,020) -0,046 (0,029) -0,009 (0,029) OROS ALL -0,123 [323] (0,027) -0,096 (0,039**) -0,052 (0,045) -0,045 (0,055) -0,031 (0,064) DEB 0,023 [74] (0,009) -0,007 (0,013) 0,011 (0,015) 0,002 (0,018) -0,161 (0,144) GEN -0,090 [99] (0,120) 0,181 (0,170) 0,102 (0,144) 0,188 (0,240) 0,134 (0,204) INV -0,932 [145] (0,179) -0,785 (0,255***) -0,649 (0,243) -0,207 (0,360) -0,165 (0,344) FCF/TA ALL -0,006 [291] (0,006) -0,028 (0,008***) -0,011 (0,008) -0,018 ( 0,011*) -0,003 (0,012) DEB 0,023 [74] (0,009) -0,007 (0,013) 0,011 (0,015) 0,002 (0,018) 0,015 (0,021) GEN -0,016 [89] (0,014) -0,036 (0,019*) -0,019 (0,020) -0,013 (0,027) -0,001 (0,028) INV -0,028 [128] (0,012) -0,046 (0,017***) -0,019 (0,017) -0,035 (0,024) -0,006 (0,024) Capital structure D/TA D/Eq Working Capital CA/CL CFIA/TA C&STI/TA ALL 0,240 [323] (0,009) 0,049 (0,012***) 0,016 (0,013) 0,003 (0,018) -0,020 (0,018) DEB 0,286 [79] (0,018) 0,067 (0,025***) 0,030 (0,025) -0,048 (0,036) -0,051 (0,035) GEN 0,245 [99] (0,017) 0,073 (0,024***) 0,013 (0,025) -0,021 (0,034) -0,044 (0,035) INV 0,229 [145] (0,015) 0,030 (0,021) 0,014 (0,020) 0,052 (0,030*) 0,005 (0,029) ALL 0,706 [323] (0,038) 0,126 (0,053**) 0,020 (0,055) -0,022 (0,075) -0,045 (0,077) DEB 1,227 [79] (0,212) 0,411 (0,300) 0,288 (0,202) -0,588 (0,424) -0,259 (0,285) GEN 0,657 [99] (0,156) -0,024 (0,221) 0,004 (0,171) 0,125 (0,313) 0,193 (0,241) INV 0,611 [145] (0,121) -0,153 (0,170) -0,124 (0,135) 0,162 (0,241) 0,048 (0,191) ALL 1,754 [323] (0,073) -0,242 (0,104**) -0,066 (0,095) -0,025 (0,147) 0,014 (0,135) DEB 1,378 [79] (0,147) -0,377 (0,207*) -0,349 (0,181*) 0,286 (0,293) 0,173 (0,256) GEN 1,849 [99] (0,231) -0,809 (0,327**) -0,195 (0,255) -0,117 (0,462) -0,074 (0,360) INV 2,142 [145] (0,186) -0,034 (0,263) 0,148 (0,233) -0,066 (0,372) 0,081 (0,330) ALL 0,061 [323] (0,004) 0,001 (0,005) -0,005 (0,005) -0,015 ( 0,008**) -0,007 (0,008) DEB 0,043 [79] (0,007) -0,011 (0,010) -0,016 (0,010) -0,030 (0,015**) -0,037 (0,015**) GEN 0,074 [99] (0,010) 0,011 (0,014) -0,007 (0,013) -0,017 (0,020) 0,001 (0,019) INV 0,059 [145] (0,008) 0,003 (0,011) -0,003 (0,011) -0,010 (0,015) -0,004 (0,016) ALL 0,102 [323] (0,007) -0,058 (0,010***) -0,039 (0,009***) -0,010 (0,015) 0,004 (0,013) DEB 0,073 [79] (0,011) -0,055 (0,015***) -0,058 (0,019**) -0,001 (0,022) 0,016 (0,027) GEN 0,105 [99] (0,018) -0,075 (0,025***) -0,051 (0,021) -0,017 (0,036) -0,020 (0,030) INV 0,121 [145] (0,014) -0,072 (0,020***) -0,039 (0,019**) -0,017 (0,029) 0,009 (0,027) The table represents the DID table that shows the post-seo characteristics of the SEO firms and the control firms within the performance and size benchmark. The post-seo difference is the difference between the SEO firms and control firms within each benchmark (SEO firms control firms). The Difference-In-Difference is the difference between pre- and post-seo characteristics relative to the benchmark. All the characteristics are three year averages for each measure post-seo. Each measure is divided into the three subsamples. Standard errors are reported in parentheses (), while the number of observations are reported in brackets [ ]. ***, ** and * indicate significance at a 1%, 5% and 10% level. Source: Own contribution Page 69 of 101

OROA has overall decreased for SEO firms and for DEB, GEN and INV firms individually. Relative to the benchmarks, the DID-estimator indicates the same deterioration, however only for the total sample in relation to the performance match the decrease is significant at 10%. Both OROS and FCF/TA are not as unambiguous. The total sample experiences a relative deterioration in these measures compared to both the performance and size benchmark, yet the deterioration is only significant at 10% for FCF/TA in relation to the performance benchmark. None of the subsamples improve or deteriorate OROS or FCF/TA significantly relative to either of the benchmarks. The DEB firms experience a decline in OROS, however the performance benchmark firms decrease relatively more, while the size match improves their OROS. For FCF/TA, DEB firms improve their performance relative to both benchmarks. GEN firms experience a relative increase in OROS and a relative decrease in FCF/TA, while INV firms on both parameters decrease in comparison to both benchmarks. Overall our predictions regarding the negative change in performance for SEO firms are slightly indicated by these results. However, low rejection levels for the total sample in respect to the benchmarks does not in any way confirm it, nor can anything be confirmed for the subsamples. The capital structure measures suggest that SEO firms still have higher leverage after the SEO, than the benchmark firms. Overall, both DEB and GEN firms decrease the debt to total assets ratio, while INV firms increase it slightly, both numerically and in terms of relative to benchmarks firms. For debt to equity DEB firms decrease the ratio, while GEN and INV firms increase it, and again this is both numerically and relative to benchmarks. Only on the debt to total assets ratio for INV firms, relative to the performance benchmark we detect significance, though only at 10%. Thus besides indications of ratio changes, the changes are not significant. Moreover as expected we get indications that DEB firms in fact repay debt as also announced, which we further look into in section 6.4. Looking at the current assets to current liabilities ratio we see that DEB firms decrease this ratio slightly, while GEN and INV firms increase it slightly. In comparison, DEB firms, despite decreasing the ratio, actually improve it to both the performance and the size benchmark. Both GEN and INV firms ratios remain relatively unchanged compared to both benchmarks. If we look at the cash flow from investing activities we see that DEB firms have halved their spending, which relative to both benchmarks is a significant negative change. GEN and INV Page 70 of 101

firms also show smaller cash flows from investing activities, where the relative change to the benchmark though is insignificant. This is a bit surprising since we would expect INV firms to increase their investment activity. The DID estimator for the total sample is also negative, but shows significance in relation the performance match. If we look at the last working capital ratio, which is cash & short-term investments to total assets we see that DEB firms still have the lowest liquidity measure compared to the other SEO motives. However, from ex-ante to ex-post DEB firms increase their relative cash holdings while GEN and INV firms have decreased it. If we compare the sample firms to the control firms in both benchmarks, SEO firms still have significant lower cash and short term investments holdings. DEB firms have a relative unchanged amount of liquid holdings compared to the performance match, while slightly increased compared to the size match. The GEN firms seem to have relatively less liquid holdings compared to both benchmarks. As for DEB firms, INV firms have increased and decreased the liquid holdings compared to the performance and size match respectively. None of the changes are however significant in any way. One might have expected that since GEN firms announce their use of issue proceeds as intended for general corporate purposes, such as for instance obtaining an adequate capital buffer they would stack up their cash holdings, which however does not seem to be the case. We will look into where the funds in fact are being used in section 6.4. 6.3.2 Operational performance of SEO firms This section will provide the results of the Difference-In-Differences regression, which previously has been described in section 4.2.1. A number of regression are applied in order to test our long-term hypotheses and to further check the results and indications given by the DID table in section 6.3. With the regression approach we are able to overcome some of the shortcomings from the DID table, by including control variables that might explain some of the variation in the outcome variable. We include pre-seo performance, as Mclaughlin et al. (1996) finds that this is a significant variable in explaining the change in performance for SEO firms, and due to the ability of this variable to control for any mean reversion in performance. Furthermore, we add age and size, both measured as the natural log. As described in section 6.1 the distribution of SEO firms in terms of industry and year is slightly skewed. Therefore all regressions include dummy variables that controls for variability between industry and years. Page 71 of 101

As with the DID-table the regressions are performed for the total sample as well as the three subsamples, compared to both the performance benchmark and the size benchmark. Furthermore, we continue only to include the benchmark firms in the regression when their respective sample firms are examined. As with the short-term regressions we apply heteroskedastic robust standard errors. The results of the regression on the total sample are presented in Table 15 below. If we start analyzing the model, the model seems to be relatively well specified with explanatory power R 2 ranging from 0,30 to 0,58. When comparing the two benchmarks, the regressions with the performance benchmark in general provide a higher explanatory power than the regression with the size benchmark. This might support the statement by Barber & Lyon (1996) that the best matching procedure is when the match is based on performance. Table 15 - Long-term DID regression: Full sample Independent variables Dependent Pre-SEO Match SEO Age Assets Ind Year N R variables perf. 2 OROA Size -0,013** -0,385*** 0,006** 0,126*** Yes Yes 646 0,300 (0,006) (0,040) (0,003) (0,002) Perf. -0,032*** -0,508*** 0,003 0,010*** (0,006) (0,039) (0,003) (0,002) OROS Size -0,035-0,441*** 0,021* 0,028*** (0,025) (0,045) (0,012) (0,008) Perf. -0,080*** -0,471*** 0,014 0,024*** (0,023) (0,045) (0,010) (0,006) FCF/TA Size -0,012** -0,551*** 0,004 0,008*** (0,006) (0,036) (0,003) (0,002) Perf. -0,025*** -0,672*** 0,002 0,005*** (0,005) (0,034) (0,002) (0,002) Yes Yes 646 0,448 Yes Yes 643 0,385 Yes Yes 644 0,483 Yes Yes 614 0,430 Yes Yes 641 0,580 The table represents the DID regression on the performance measures for the full sample. The dependent variables are defined as the difference between pre-seo and post-seo characteristics, thus OROA is defined as pre-seo OROA(3 year average) - post-seo OROA(3 year average). The same calculations are conducted for the other performance measures. Match defines the benchmark sample. SEO is a dummy variable equal to 1 if a firm conducts a SEO and 0 otherwise. Pre-SEO perf. is the pre-seo performance of the dependent variable. Age and Assets are the natural log (ln) of firm age and assets in the SEO year and year t=-1 respectively. Ind. and Year are dummy variables that define the industry of the SEO firm and the SEO year. Heteroskedastic robust standard errors are reported in parentheses (). ***, ** and * indicate significance at a 1%, 5% and 10% level. Source: Own contribution The DID regression in general seems to show a decline in performance on all performance measures when a firm performs a SEO, relative to benchmarks. As mentioned, the coefficient measures the change in performance for SEO firms as calculated by post performance minus Page 72 of 101

pre performance. Hence, on average OROA decreases with 1,3 pp 29 compared to the size match and 3,2 pp compared to the performance match. This is significant at a 5% and 1% level respectively. For OROS the decrease is 3,5 pp and 8 pp in comparison to the size- and performance match, however only in relation to the performance match the decrease is significant, but at a 1% level. Finally for FCF/TA the decrease is on average 1,2 pp and 2,5 pp at a 5% and 1% significance level for the performance- and size match, respectively. The pre-seo performance for all performance measures relative to both benchmarks indicate that the performance of the SEO firms ex-ante has a significant negative effect on post-seo performance, with rejection levels on all measures at 1%. Thus a higher performance ex-ante leads to a higher decline in performance in the years subsequent to the SEO. As discussed in 4.2.5, we have included three years of pre- and post-seo data in order to control for mean reversion, which basically means that this conclusion should not be based upon performance reverting to its true mean after the SEO. Age does not seem to have a significant effect on the performance of SEO firms ex-post. Age only shows a significant impact on the performance measures OROA and OROS, when matching on size, with rejection levels at 5% and 10% respectively. The rest of the coefficients show an insignificant positive effect on performance. Since all coefficients seem to have a positive sign, there might be an indication that elder firms in general are performing better than younger firms. However since most of the coefficients are insignificant we are not able to draw a conclusion upon this. If we look at assets, this control variable has a significant positive effect on all performance measures regardless of benchmark, with rejection levels at 1%. The regression coefficients of the performance measures for SEO firms, support some of the findings in the DID Table 14 in section 6.3.1. However after including control variables we get higher rejection levels, and therefore stronger results. The significant decline in operating performance is line with the previous literature, and is furthermore underpinned by the theory, that a SEO offer leads to subsequent declining operating performance. On this basis we can therefore conclude that firms performing a SEO experience a significant decline in 29 pp = percent point Page 73 of 101

operating performance ex-post relative to benchmarks, hence we find support for our H2 hypothesis. 6.3.3 Operational performance of the subsamples We now shift focus and run the regression on the basis of the subsamples, and their respective control firms. The results for firms within the debt repayment subsample are presented in Table 16 below. Table 16 - Long-term DID regression: Debt repayment Independent variables Dependent Pre-SEO Match SEO Age Assets Ind Year N R variables perf. 2 OROA Size -0,006-0,125** 0,002 0,005* Yes Yes 158 0,191 (0,008) (0,060) (0,003) (0,003) Perf. -0,015* -0,336*** -0,001 0,004 (0,009) (0,072) (0,003) (0,003) OROS Size -0,026-0,367*** 0,001 0,023** (0,029) (0,088) (0,007) (0,011) Perf. -0,024-0,290*** -0,007 0,010 (0,029) (0,063) (0,009) (0,007) FCF/TA Size 0,004-0,424*** 0,001 0,003 (0,009) (0,075) (0,004) (0,003) Perf. -0,016* -0,734*** 0,001 0,007** (0,009) (0,084) (0,004) (0,003) Yes Yes 158 0,334 Yes Yes 158 0,373 Yes Yes 158 0,364 Yes Yes 153 0,353 Yes Yes 158 0,595 The table represents the DID regression on the performance measures for the DEB sample. The dependent variables are defined as the difference between pre-seo and post-seo characteristics, thus OROA is defined as pre-seo OROA(3 year average) - post- SEO OROA(3 year average). The same calculations are conducted for the other performance measures. Match defines the benchmark sample. SEO is a dummy variable equal to 1 if a firm conducts a SEO and 0 otherwise. Pre-SEO perf. is the pre-seo performance of the dependent variable. Age and Assets are the natural log (ln) of the firm age and assets in the SEO year and year t=-1 respectively. Ind and Year are dummy variables that define the industry of the SEO firm and the SEO year. Heteroskedastic robust standard errors are reported in parentheses (). ***, ** and * indicate significance at a 1%, 5% and 10% level. Source: Own contribution As can be seen the model still seems to be relatively well specified. We once again see that when the regressions are performed with the performance benchmark, the explanatory power is higher. Overall DEB firms seem to experience a decline in performance compared to both benchmarks after conducting a SEO, though most coefficients are insignificant. The decrease in OROA is 0,6 pp and 1,5 pp, in comparison to the size- and performance benchmark. Only in comparison to the performance benchmark the decrease however is significant at 10%. For OROS we find no significance, but we find a decrease of 2,6 and 2,4 pp compared to the sizeand performance benchmark. In terms of FCF/TA we find a significant decrease of 1,6 pp at a 10% level for the performance match. Page 74 of 101

As with the total sample, pre-seo performance has a significant negative impact on all performance measures regardless of benchmark. Age does not have any significant effect on firm performance, showing low coefficients with various signs. The coefficients for firm size are all positive, however we only get significance on three coefficients with rejection levels at 5% or lower. Most of the performance coefficients are negative, none of them are however significant at a level where we can safely confirm that the SEO has an effect on performance. These results are consistent with our short-term findings. The lack of short-term market reaction also suggested unchanged long-term performance, which our results also confirm. This is in line with the findings of Autore et al. (2009), but contradicts the results of Walker & Yost (2008). Moreover our results follow the predictions of the adverse selection model of Myers & Majluf (1984) and Cooney & Kalay (1993) and the signaling model of Miller & Rock (1985). Since DEB firms in fact decrease their leverage as shown in Table 14, the optimal capital structure theory (Brennan & Schwartz, 1978) suggests better performance ex-post, which our results however contradicts. Overall we find support for our H2A hypothesis, thus we conclude that SEO firms that issue equity to repay debt do not experience a significant change in operating performance after a SEO, when compared to non-issuers. Table 17 shows the DID regression for firms that use their issue proceeds for general corporate purposes. The model still seems to be well specified, however with higher explanatory power compared to the regression with DEB firms. If we look at the performance of these SEO firms, we find insignificant coefficients with varying signs in comparison to the size benchmark. The performance benchmark provides a more unambiguous picture as all coefficients are negative, proving significant at 5% for OROA and FCF/TA with a decline of 2,2 pp and 2,5 pp respectively. This could be an indication of a relative decline in performance subsequent to a SEO, however the different results in comparison to the two benchmarks leave no clear conclusion. Pre-SEO performance still shows a significant negative impact on performance expost, with rejection levels at 1%. Age and assets show a positive effect on each performance measure, providing significance in some of the cases. Page 75 of 101

Table 17 - Long-term DID regression: General corporate purpose Independent variables Dependent Pre-SEO Match SEO Age Assets Ind Year N R variables perf. 2 OROA Size 0,003-0,341*** 0,010** 0,008** Yes Yes 198 0,415 (0,010) (0,054) (0,005) (0,003) Perf. -0,022** -0,460*** 0,002 0,006* (0,011) (0,061) (0,005) (0,003) OROS Size 0,034-0,569*** 0,037 0,030** (0,045) (0,087) (0,024) (0,014) Perf. -0,013-0,504*** 0,025 0,029* (0,040) (0,106) (0,017) (0,014) FCF/TA Size -0,012-0,568*** 0,012*** 0,005 (0,010) (0,062) (0,005) (0,003) Perf. -0,025** -0,635*** 0,006 0,004 (0,011) (0,061) (0,005) (0,003) Yes Yes 198 0,489 Yes Yes 196 0,482 Yes Yes 198 0,564 Yes Yes 188 0,562 Yes Yes 196 0,598 The table represents the DID regression on the performance measures for the GEN sample. The dependent variables are defined as the difference between pre-seo and post-seo characteristics, thus OROA is defined as pre-seo OROA(3 year average) - post- SEO OROA(3 year average). The same calculations are conducted for the other performance measures. Match defines the benchmark sample. SEO is a dummy variable equal to 1 if a firm conducts a SEO and 0 otherwise. Pre-SEO perf. is the pre-seo performance of the dependent variable. Age and Assets are the natural log (ln) of the firm age and assets in the SEO year and year t=-1 respectively. Ind and Year are dummy variables that define the industry of the SEO firm and the SEO year. Heteroskedastic robust standard errors are reported in parentheses (). ***, ** and * indicate significance at a 1%, 5% and 10% level. Source: Own contribution Our performance measure results for the GEN subsample are for us quite surprising since we expected this subsample to experience the highest decline in operating performance ex-post. Therefore our results do not fully support the previous findings of Autore et al. (2009) and Walker & Yost (2008). Our short-term findings implied a downturn in performance, which however is not the case. Contrary to the theories, who argue for a decline in performance due to bad projects, empire building etc., GEN firms do not seem to engage in value destroying activities. Since the results cannot strongly support our H2B hypothesis, we conclude that firms issuing equity with the issue proceeds intended for general corporate purposes, do not experience a significant decline in operating performance ex-post relative to benchmarks. The lack of support for our hypothesis questions why the market reacts negative around the announcement for this subsample. Page 76 of 101

Table 18 - Long-term DID regression: Investment Independent variables Dependent Pre-SEO Match SEO Age Assets Ind Year N R variables perf. 2 OROA Size -0,029*** -0,465*** 0,008 0,020*** Yes Yes 290 0,357 (0,010) (0,061) (0,006) (0,004) Perf. -0,044*** -0,561*** 0,006 0,013*** (0,011) (0,059) (0,006) (0,003) OROS Size -0,084* -0,433*** 0,036 0,023 (0,046) (0,059) (0,026) (0,015) Perf. -0,149*** -0,577*** 0,014 0,023** (0,043) (0,061) (0,023) (0,011) FCF/TA Size -0,023** -0,582*** 0,001 0,014*** (0,009) (0,049) (0,005) (0,003) Perf. -0,029*** -0,670*** 0,002 0,004* (0,009) (0,047) (0,004) (0,003) Yes Yes 290 0,492 Yes Yes 289 0,401 Yes Yes 288 0,504 Yes Yes 273 0,475 Yes Yes 287 0,602 The table represents the DID regression on the performance measures for the INV sample. The dependent variables are defined as the difference between pre-seo and post-seo characteristics, thus OROA is defined as pre-seo OROA(3 year average) - post-seo OROA(3 year average). The same calculations are conducted for the other performance measures. Match defines the benchmark sample. SEO is a dummy variable equal to 1 if a firm conducts a SEO and 0 otherwise. Pre-SEO perf. is the pre-seo performance of the dependent variable. Age and Assets are the natural log (ln) of the firm age and assets in the SEO year and year t=-1 respectively. Ind and Year are dummy variables that define the industry of the SEO firm and the SEO year. Heteroskedastic robust standard errors are reported in parentheses (). ***, ** and * indicate significance at a 1%, 5% and 10% level. Source: Own contribution Table 18 above shows the DID regression for the INV subsample. The model has quite high explanatory power and therefore seems to be well specified. Opposite the DEB and GEB subsample we see a clear direction for SEO firms that use the issue proceeds for investments, which is a significant decline in performance after a SEO compared to benchmarks. Regardless of benchmark all coefficients prove to be significant with high rejection levels. Pre-SEO performance also seems to have a significant negative effect on firm performance with rejection levels at 1%, hence higher pre-seo performance indicates a higher decline in performance ex-post. Age has a positive, however insignificant, effect on firm performance while assets seem to have a positive effect on the change in performance, proving significance on almost all coefficients. Thus larger firms experience better performance ex-post, than smaller firms. Our findings are in line with our short-term findings, where we see significant negative abnormal returns around announcement. Our results could indicate that the investments undertaken are in value destroying projects, as also argued by Jensen s (1986) free cash flow theory, which could explain why these firms experience a significant relative decline in Page 77 of 101

performance. These results therefore support our H2C hypothesis, that firms issuing equity to fund investments experience a significant decline in operating performance in the years subsequent the SEO relative to benchmarks. 6.3.4 Quantile regression results As described in section 4.2.1.1 we employ quantile regressions to test the robustness of our results in the previous section. The regressions are run with the same control variables as for the OLS regression. The results of the quantile regression can be found in Appendix G. The quantile regressions report the same sign of the coefficients where the major difference is that we see rejection levels being lower with the quantile regressions. The lower rejection levels could imply that outliers affect the OLS regressions, even though the data has been winsorized. Overall the quantile regressions also show significant negative performance for the full- and INV sample, whereas the DEB- and GEN sample show no change in performance relative to benchmarks. The results of the quantile regressions do not change our conclusion from the OLS regression results. 6.3.5 Pairwise comparison of motives The previous section indicated that the change in operational performance ex-post for the DEB, GEN and INV firms is different. As with the short-term event study, it is therefore appropriate to test whether there is a significant difference between the performance changes across motives. As mentioned, we do this by pairwise comparing the estimated regression coefficients of the SEO dummy variable across the separate models. Thus, the tests are based on the SEO variable regression coefficients for each performance measure from Table 20, Table 21 and Table 22. Below, Table 1Table 19 reports the results from the pairwise comparison. In general we do not detect any significant difference between the subsamples DEB and GEN regardless of benchmark. This supports our findings in section 6.3.3, were we concluded that neither the DEB nor GEN subsample experiences a decline in performance subsequent to a SEO when compared to a benchmark. If we compare both the DEB and GEN sample individually to the INV sample the overall conclusion is, that there are significant differences between the samples, based on most of the performance measures. INV firms therefore experience a significant decline in performance in comparison to DEB and GEN firms, which also supports our Page 78 of 101

conclusion in section 6.3.3. Autore et al. (2009) and Walker & Yost (2008) find different results on this subject, and our results do not follow any of their findings. We are therefore not able to get any complete clarification on the change in performance ex-post across SEO motive. On the basis of our results we conclude that there is no difference in the performance change ex-post between DEB and GEN firms, whereas compared to the INV sample, INV firms experience a higher decline in performance ex-post. Thus we find support for our H2D hypothesis. Table 19 - Pairwise comparison between subsamples Performance match Size match p-value OROA** OROS** FCF/TA** OROA** OROS** FCF/TA** DEB vs. GEN 0,594** 0,803** 0,467** 0,435** 0,229** 0,180** DEB vs. INV 0,029** 0,011** 0,256** 0,072** 0,253** 0,020** GEN vs. INV 0,129** 0,015** 0,745** 0,021** 0,052** 0,395** The table reports the p-value of the Wald chi-square test. OROA, OROS, FCF/TA are the dependent variables in the regression from which the estimated SEO coefficient originate. The test is performed on the basis of the estimated SEO coefficients from section 6.3.3. ***, ** and * indicate significance at a 1%, 5% and 10% level. Source: Own contribution 6.3.6 Summary of long-term findings Our findings in section 6.3 did not entirely turn out as we had expected. The performance measures overall indicate that firms that issue equity experience a significant decline in performance ex-post relative to benchmarks. The same holds for firms that announce they intend to use the issue proceeds to fund investments, which is in line with the market reaction in our short-term findings. The sample of GEN firms do not experience a decline in performance ex-post when compared to a benchmark, which is kind of surprising since, our short-term findings, previous research and theory points in the direction of a decline. As anticipated, the results for DEB firms, do not show any significant change in performance expost relative to benchmarks, and is therefore in line with the short-term market reaction. In a pairwise comparison, firms that issue equity and intend to use the issue proceeds for investments, experience a significant larger change in performance ex-post, compared to GEN and DEB firms. In the next section we investigate whether the issue proceeds, actually are spend in accordance to the SEO prospectus, which will give us an indication of what drives the results presented in section 6.3. Page 79 of 101

6.4 Actual use of issue proceeds The purpose of the following section is to examine the actual use of issue proceeds. As described in section 4.3, we employ the regression model of Kim & Weisbach (2008) using the three subsamples. Specifically we therefore estimate and examine the link between SEO issue proceeds and subsequent investments, cash holdings and debt repayments, for each subsample separately. As described we should get valuable insight into whether the SEO firms actually use the issue proceeds as announced. We present the estimated coefficients for SEO proceeds and other sources of income as these are of interest, and thus omit the coefficients for the control variables. As can be noted in the following tables, the number of observations diminishes with the number of year s post-seo, due to firms being acquired, gone bankrupt etc. We choose to include all observations to avoid survivorship bias. Table 20 presents the estimates for the change in the six accounting variables for the general corporate purpose subsample. As can be seen from the table the coefficients for SEO proceeds are significant in all years. Thus the proceeds raised in the offerings do have a significant impact on all the dependent variables. By comparing the coefficients of funds raised from SEO s to the coefficients from internal generated funds, we get insights into how the different sources of funds are disposed of. The significant difference in the coefficient on cash and short-term investments, R&D and long-term debt reduction, indicates that the issue proceeds are more likely to be used for these purposes than internal generated funds. Whereas in the SEO year internal generated funds are more likely to be used for capital expenditures. A quite useful way of viewing the coefficients is by looking at the change. From this we get that for a marginal EUR raised in a SEO, approximately 54 cents are held as cash in the fiscal year ending after the SEO. The amount decreases in the following years indicating that the proceeds are used over time rather than one lump sum. Since this is the case we might not be able to measure the full effect of the investments on operating performance within our event window, as the investments might not have become productive yet. In connexion to this the EUR change for capital expenditures and R&D both increases from the SEO year to the third year after. Capital expenditures rise from 5 cents to 37 cents, and R&D rises from 17 cents to 66 cents, both threeyears after the SEO. This again suggests that the funds are used over time rather than one big investment. We also see indications that these firms generally spend a good fraction of the Page 80 of 101

proceeds to reduce long-term debt 30. This reduction mainly happens in the SEO year and amounts to around 56 cents per EUR raised. With regards to acquisitions we don t find any propensities to use SEO proceeds rather than internal generated funds. The coefficients and EUR change however do indicate that both sources are used to fund acquisitions in the SEO year. The fact that we see significant coefficients on nearly all accounting variables indicate that funds raised through the equity issue actually are used for multiple purposes as the subsample announces. Thus we find support for our hypotheses that GEN firms use their issue proceeds for cash & short-term investments (H3A), capital expenditures (H3B), R&D (H3C), acquisitions (H3D) and long-term debt reductions (H3E). The significant amount held as cash & short-term investments, might add some support to the theory of GEN firms issuing equity due to overvaluation as stated by Myers & Majluf (1984). However seen in relation to the unchanged operating performance, this would indicate that the significant amount of issue proceeds used for investments are not used for bad projects, as otherwise suggested by Myers & Majluf (1984) & Cooney & Kalay (1993). Turning the attention to the results for the debt repayment subsample in Table 21, we clearly see a different and more focused tendency. As the case was for the general corporate purpose subsample, total assets are significantly affected by the SEO, which might indicate that not all the issue proceeds are used for debt repayments. Furthermore, the DEB subsample also shows a significant increase in cash and short-term investments in the SEO year, however not in the following years. Thus at year-end 37 cents per marginal EUR raised is still unused. Additionally it is quite clear, both indicated by the significance in the coefficient and difference in coefficients, that SEO proceeds indeed are used for repaying debt. The EUR change implies that the debt reductions primarily happens in the year of the SEO and the year following. Approximately 61 cents of the funds raised through the issue, is spend on reducing debt over the first two years. This is also indicated by the decrease in leverage from the DID Table 14. While other sources of funds do have a significant effect on acquisition spending, we do not find any significant differences in the propensity to use one source of fund over the other. However since firms use internal funds to fund investments, this could be an indication of NPV positive projects as suggested by the free cash flow theory (Jensen, 1986). Our findings suggest 30 Long-term debt reduction is not off-set by any new debt raised, as new debt is included in other sources of funds. Page 81 of 101

that firms that raise equity to reduce debt, in fact use the proceeds for this purpose, while keeping some proceeds as cash, though only in the SEO year. Hence we only find support for our H3E hypothesis. Since DEB firms do not use issue proceeds on investments, they should be free from the negative impact of bad projects. Our results in section 6.3.3 can therefore be explained by the adverse selection theory of Myers & Majluf (1984) and Cooney & Kalay (1993) as well as the signalling theory of Miller & Rock (1985). The results for the INV subsample are presented in Table 22. From this we see that like the GEN and DEB subsamples, invest firms hold a large fraction - 71 cents per EUR - of the raised issue proceeds as cash in the SEO year. This fraction decreases in the years following. The significant differences in coefficients indicate that issue proceeds clearly are preferred over other income when it comes to increasing cash holdings. Examining capital expenditures and R&D expenditures, our results indicate that issue proceeds are the preferred source of capital for these investments. Per marginal raised EUR, 4 cents are invested in capital expenditures the first year, but increases to 30 cents in the third year after. For R&D the expenditure the first year is 11 cents and increases to 66 cents in the third year. Seen in connection with the decrease in cash and short-term investments, this indicates that the investments are dispersed over time rather than one large investment. As for GEN firms, we might not be able to see the real effect of the investments on operating performance. While our results also show significant effect on acquisitions, the fact that SEO proceeds and other sources are not significant different indicate that both sources are used to fund acquisitions. Quite surprising INV firms also appear to use a large fraction of the raised capital to reduce long-term debt. For a marginal EUR raised, 80 cents is used to reduce long-term debt in the SEO year. However as shown by the DID-tables, INV firms increase their leverage in the observation period, indicating that the firms are merely replacing their debt. Overall we find support for our hypotheses that INV firms use their issue proceeds for cash & short-term investments (H3A), capital expenditures (H3B), R&D (H3C), acquisitions (H3D) and long-term debt reductions (H3E). As our results in section 6.3.3 show, INV firms experience a decline in performance ex-post relative to benchmarks, which might imply that managers do not invest in the shareholders best interest, as the free cash flow theory (Jensen, 1986) suggests. Page 82 of 101

Table 20 - Actual use of issue proceeds: General corporate purpose SEO proceeds Other sources Change Dependent Variable t N β1 t-stat β2 t-stat β1 = β2 SEO proceeds Other sources R 2 Total Assets 0 99 0,711 4,62*** 0,798 6,04*** 0,667 0,75 0,79 0,67 1 99 0,683 5,15*** 0,598 4,35*** 0,669 0,76 0,54 0,70 2 92 1,132 4,20*** 0,289 2,41** 0,001*** 1,34 0,24 0,47 3 85 0,911 4,15*** 0,529 3,46*** 0,091* 1,06 0,39 0,36 Cash & ST Inv. 0 99 0,519 3,16*** 0,096 1,49 0,019** 0,54 0,09 0,55 1 99 0,466 2,73*** 0,057 0,69 0,049** 0,49 0,05 0,49 2 92 0,558 2,70*** 0,029 0,38 0,011** 0,56 0,02 0,51 3 85 0,370 2,88*** 0,069 1,51 0,020** 0,36 0,04 0,54 Σ CAPEX 0 93 0,058 1,80* 0,278 3,27*** 0,019** 0,05 0,25 0,66 1 99 0,154 2,70*** 0,152 2,81*** 0,984 0,15 0,12 0,60 2 92 0,337 3,66*** 0,059 1,57 0,003*** 0,33 0,04 0,57 3 85 0,344 3,74*** 0,049 1,00 0,002*** 0,37 0,03 0,59 Σ Acquisitions 0 99 0,430 2,08** 0,453 2,02** 0,94 0,42 0,41 0,56 1 99 0,439 2,62** 0,250 1,60 0,460 0,43 0,20 0,49 2 92 0,537 3,35*** 0,038 0,47 0,002*** 0,53 0,03 0,41 3 85 0,478 2,88*** -0,001-0,01 0,009*** 0,47 0,00 0,39 Σ R&D 0 99 0,186 2,83*** -0,044-1,78* 0,001*** 0,17-0,04 0,66 1 99 0,375 3,04*** -0,031-1,14 0,003*** 0,36-0,02 0,66 2 92 0,442 3,44*** -0,082-1,95* 0,000*** 0,44-0,06 0,72 3 85 0,637 3,59*** -0,035-0,89 0,001*** 0,66-0,02 0,73 Σ LT Debt reduction 0 97 0,523 3,56*** 0,170 1,22 0,079* 0,56 0,17 0,58 1 99 0,469 3,39*** 0,035 0,42 0,012** 0,50 0,03 0,55 2 92 0,405 2,77*** -0,103-2,49** 0,000*** 0,43-0,08 0,60 3 85 0,461 3,34*** -0,110-2,63** 0,000*** 0,51-0,08 0,60 The table presents OLS regressions with change in two balance sheet items and the sum of four accounting variables as dependent variables. The change in balance sheet items, are the changes from t-1 to the year in question, and the accounting measures are the accumulated cash flows since the SEO. Both are relative to total assets in the fiscal year ending before the SEO. LN(assets), industry dummies and year dummies are used as control variables, but are omitted for the sake of brevity. EUR change is the estimated change in the dependent variable in question, given a one EUR change in either SEO proceeds or funds from other sources. The change is based on a median size firm ( 236,6 mio. total assets) in industry C (Manufacturing) in 2009. Median SEO proceeds are 26 mio. and median funds from other sources are 43,4 mio.. The significance level is indicated at the 10%, 5% and 1% level with *, ** and ***, respectively. Source: Bloomberg database and own calculations Page 83 of 101

Table 21 - Actual use of issue proceeds: Debt repayment SEO proceeds Other sources Change Dependent Variable t N β1 t-stat β2 t-stat β1 = β2 SEO proceeds Other sources R 2 Total Assets 0 79 0,980 2,91*** 0,885 2,93*** 0,851 0,99 0,83 0,72 1 79 1,284 2,62** 0,473 3,61*** 0,131 1,40 0,41 0,67 2 75 1,700 2,83*** 1,125 6,10*** 0,414 1,77 0,91 0,68 3 73 2,021 2,63** 0,918 4,63*** 0,207 2,14 0,66 0,60 Cash & ST Inv. 0 79 0,374 4,75*** 0,060 1,19 0,000*** 0,37 0,05 0,57 1 79 0,273 1,91* 0,095 3,06*** 0,200 0,27 0,08 0,36 2 75 0,353 1,41 0,112 2,05** 0,375 0,35 0,09 0,51 3 73 0,429 1,47 0,147 6,56*** 0,322 0,43 0,10 0,59 Σ CAPEX 0 77 0,004 0,08 0,035 1,67 0,628 0,00 0,03 0,43 1 79 0,060 0,56 0,031 2,20** 0,779 0,06 0,03 0,51 2 75 0,090 0,61 0,049 1,18 0,796 0,10 0,04 0,53 3 73 0,095 0,54 0,083 2,70*** 0,943 0,11 0,06 0,63 Σ Acquisitions 0 79 0,187 0,77 0,426 2,29** 0,476 0,19 0,39 0,62 1 79 0,408 1,19 0,143 1,77* 0,474 0,41 0,11 0,51 2 75 0,359 1,07 0,350 3,42*** 0,645 0,37 0,28 0,65 3 73 0,385 1,28 0,237 2,80*** 0,674 0,42 0,17 0,61 Σ R&D 0 79 0,061 1,63-0,040-0,86 0,153 0,06-0,04 0,51 1 79 0,116 1,45-0,018-0,99 0,178 0,11-0,01 0,53 2 75 0,129 1,14 0,051 0,96 0,571 0,13 0,04 0,53 3 73 0,218 1,24 0,045 0,78 0,372 0,22 0,03 0,53 Σ LT Debt reduction 0 77 0,410 2,59** -0,071-0,73 0,007*** 0,43-0,07 0,68 1 79 0,584 6,04*** -0,051-2,01** 0,000*** 0,61-0,04 0,62 2 75 0,559 5,80*** -0,005-0,07 0,000*** 0,59 0,00 0,55 3 73 0,604 7,23*** -0,030-0,60 0,000*** 0,64-0,02 0,56 The table presents OLS regressions with change in two balance sheet items and the sum of four accounting variables as dependent variables. The change in balance sheet items, are the changes from t-1 to the year in question, and the accounting measures are the accumulated cash flows since the SEO. Both are relative to total assets in the fiscal year ending before the SEO. LN(assets), industry dummies and year dummies are used as control variables, but are omitted for the sake of brevity. EUR change is the estimated change in the dependent variable in question, given a one EUR change in either SEO proceeds or funds from other sources. The change is based on a median size firm ( 1324,9 mio. total assets) in industry C (Manufacturing) in 2009. Median SEO proceeds are 65,9 mio. and median funds from other sources are 182,3 mio.. The significance level is indicated at the 10%, 5% and 1% level with *, ** and ***, respectively. Source: Bloomberg database and own calculations Page 84 of 101

Table 22 - Actual use of issue proceeds: Investment SEO proceeds Other sources Change Dependent Variable t N β1 t-stat β2 t-stat β1 = β2 SEO proceeds Other sources R 2 Total Assets 0 145 1,067 11,31*** 0,263 2,84*** 0,000*** 0,97 0,25 0,76 1 145 1,198 8,10*** 0,564 5,72*** 0,001*** 1,43 0,54 0,66 2 131 1,026 5,26*** 0,464 3,89*** 0,036** 1,54 0,50 0,54 3 124 1,026 4,56*** 0,409 3,25*** 0,019** 1,57 0,40 0,46 Cash & ST Inv. 0 144 0,569 6,06*** -0,152-2,07** 0,000*** 0,71-0,20 0,54 1 144 0,567 4,69*** 0,033 0,47 0,000*** 0,52 0,02 0,40 2 131 0,463 3,29*** 0,090 1,61 0,024** 0,47 0,07 0,30 3 124 0,322 1,95* 0,128 1,85* 0,301 0,38 0,10 0,30 Σ CAPEX 0 138 0,046 1,26 0,020 1,09 0,494 0,04 0,02 0,39 1 145 0,178 3,38*** 0,059 2,00** 0,017** 0,16 0,04 0,52 2 131 0,241 3,52*** 0,084 2,61** 0,018** 0,24 0,06 0,54 3 124 0,279 3,27*** 0,076 1,66 0,029** 0,30 0,05 0,50 Σ Acquisitions 0 145 0,300 2,80*** 0,388 3,96*** 0,475 0,29 0,39 0,61 1 145 0,298 3,39*** 0,358 5,37*** 0,571 0,35 0,34 0,61 2 131 0,358 3,51*** 0,207 3,22*** 0,201 0,39 0,16 0,61 3 124 0,395 3,34*** 0,150 1,87* 0,091* 0,44 0,11 0,57 Σ R&D 0 145 0,121 2,71*** -0,142-5,13*** 0,000*** 0,11-0,14 0,59 1 145 0,286 4,15*** -0,134-3,34*** 0,000*** 0,28-0,11 0,53 2 131 0,427 3,36*** -0,113-2,98*** 0,000*** 0,48-0,09 0,53 3 124 0,513 3,11*** -0,040-0,58 0,003*** 0,66-0,03 0,49 Σ LT Debt reduction 0 137 0,815 12,77*** -0,053-1,08 0,000*** 0,80-0,05 0,85 1 145 0,819 12,25*** 0,011 0,31 0,000*** 0,83 0,01 0,82 2 131 0,809 11,82*** -0,031-1,64 0,000*** 0,88-0,02 0,84 3 124 0,933 9,05*** -0,043-0,95 0,000*** 1,04-0,03 0,82 The table presents OLS regressions with change in two balance sheet items and the sum of four accounting variables as dependent variables. The change in balance sheet items, are the changes from t-1 to the year in question, and the accounting measures are the accumulated cash flows since the SEO. Both are relative to total assets in the fiscal year ending before the SEO. LN(assets), industry dummies and year dummies are used as control variables, but are omitted for the sake of brevity. EUR change is the estimated change in the dependent variable in question, given a one EUR change in either SEO proceeds or funds from other sources. The change is based on a median size firm ( 247 mio. total assets) in industry C (Manufacturing) in 2009. Median SEO proceeds are 44,8 mio. and median funds from other sources are 36,9 mio.. The significance level is indicated at the 10%, 5% and 1% level with *, ** and ***, respectively. Source: Bloomberg database and own calculations Page 85 of 101

6.4.1 Actual use of issue proceeds summary The findings in the previous section generally give a good indication on how the issue proceeds for the respective subsamples are used. DEB firms primarily repay long-term debt, while GEN and INV firms use the issue proceeds for various investments and long-term debt reductions. Worth noticing is that GEN and INV firms, seem to invest the issue proceeds over time rather than on one large investment. This could potentially affect the operating performance measures in a negative way, since the investments might not have been become productive yet. Overall our findings suggest that firms use the issue proceeds as stated in the SEO prospectus. Page 86 of 101

7 Conclusion The impact of a seasoned equity offering has previously been extensively studied, and most of the previous findings all point in the direction, that a seasoned equity offerings entails bad news. Many of these findings are however based on US firms, outdated data and many of the academics do not take into account what the issue proceeds are used for. The main ambition of this thesis therefore has been to quantitatively analyze the effect of a seasoned equity offering: How the market reacts, how operating performance changes and how the issue proceeds are used, while taking into account the stated use of issue proceeds. We use a sample of 342 European firms who conduct a seasoned equity offering in the period 2000 to 2010. First we analyze the short-term market reaction by measuring abnormal stock returns around the announcement. Secondly we employ the Difference-In-Difference approach, in order to test the change in operating performance. Finally we examined how the issue proceeds are used. In combination the results lead us to conclude the following: Firms offering seasoned equity overall experience significant negative abnormal stock returns of 1,19% around the announcement, and a significant decline in operating performance expost relative to benchmarks. In combination the negative market reaction and decline in operating performance provides good evidence for seasoned equity offerings having a negative effect on issuing firms. For our 81 firms issuing equity with the purpose of repaying debt, we detected no significant market reaction around the announcement. The market reaction theoretically suggests that the market does not change its anticipation of future performance, and as indicated our longterm results showed no significant change in operating performance relative to benchmark firms. Our findings revealed that these firms primarily use the issue proceeds to repay debt as stated in the SEO prospectus. Therefore seasoned equity offerings did not seem to have any effect on issuing firms, when the issue proceeds are meant and used for repaying debt. Our sample of 107 firms who are unspecific about the use of issue proceeds, showed a significant negative market reaction of 2,22% around the announcement. However we did not Page 87 of 101

obtain any strong results for a decline in operating performance relative to the benchmarks, as was otherwise anticipated from the market reaction. The analysis of issue proceeds usage revealed that the funds more or less are used for investments, debt repayments and increasing cash holdings. The fact that we did not get stronger results for a decline in operating performance could question whether the issue has an effect on this. This might however just reflect that the investments undertaken are not value destroying as expected. The 154 firms in our sample that were specific about investment opportunities, when issuing equity, experienced a significant market reaction of -0,86%. The results showed that these firms saw the largest relative decline in operating performance. Our results confirmed that these firms use the issue proceeds for investments, repaying debt and temporarily increasing cash holdings. The decline in operating performance could indicate that, ceteris paribus, the investments in fact are value destroying. In a pairwise comparison we did not find any significant differences in the market reaction between our three samples, even though there seemed to be different reactions. Comparing the change in operational performance, on the other hand showed that firms with specific investment opportunities experienced a significant larger negative change, than firms that repay debt or are unspecific about the use of issue proceeds. Empirically none of the theories are able to explain our short- and long-term findings completely. Nevertheless our short-term findings for all three samples come very close to what is also suggested by the adverse selection model of Myers & Majluf (1984) and Cooney & Kalay (1993) as well as the signaling model of Miller & Rock (1985). The free cash flow theory of Jensen (1986) also predicted the short-term findings, but does not explain the market reaction for firms repaying debt. In general the theories are also able to predict the results of our long-term findings, however none of the theories are able explain the findings for firms who intend to use the issue proceeds for general corporate purposes. Neither the market reaction nor the change in operating performance can be explained by the capital structure theories for any of our three samples. Page 88 of 101

Overall our short-term results are in most cases in line with the previous empirical findings, apart from the fact that we did not find a significant decline for firms that repay debt. Our long-term results are however different from the previous empirical findings, which could advocate for further research on the subject. 8 Critical discussion This section contains a brief discussion in relation to the methodology, data treatment and results presented in the thesis. One of the major dilemmas in our sample was whether or not to exclude firms that conduct multiple issues over a limited timeframe. We choose to exclude firms in our sample, that have conducted a SEO three years prior and three years post, since we wanted to be able to measure the effect of the specific SEO. By doing this we do however exclude the kind of firms that undertake a SEO on a regular basis, which means our dataset could be biased if the market reacts different to these types of firms or if they see different performance changes. On the basis of our use of issue proceeds study in section 6.4, we concluded that firms that issue equity for general corporate purposes as well as for investment purposes, seem to invest a significant amount of the issue proceeds in different investing activities. As seen these investments are however typically not made in the SEO year, but are invested over time subsequent to the SEO. In our long-term performance study we analyzed three years before and three years after the. Based upon the argument that an investment typically takes time to become productive and that the firms do not invest the entire issue proceeds at once, it could be beneficial to include a longer event window. This would give the investments more time to get productive and to fully invest the issue proceeds. However, it would require more data, which already can be difficult to attain. Moreover the data has been divided into pre- and post-seo characteristics, based on three-year averages. We did this in order to overcome potential problems with autocorrelation. The problem with this has been that in some cases we only had one observation pre- or post-seo, which then was included as a three- year average. This might not give at true picture of firm characteristics, however we did this in order not to exclude to many firms due to missing accounting data. Page 89 of 101

Based on the SEO prospectus and the data available from Bloomberg we classified each SEO into a motive based on the intended use of issue proceeds. However in some of the cases we detected multiple purposes for the use of the issue proceeds. We decided to group the SEO firms based on what the majority of the issue proceeds were intended for. An argument could be whether or not this was the correct way to classify the SEO s, or if the ones with multiple purposes should have been excluded. Our long-term results are based on a comparison of the performance of the firms in our sample and the two benchmarks samples. The results indicated different results in respect to the two different benchmark samples. The relatively simple matching algorithms we have used, are widely used in the previous literature, however it could be argued that these methods may be too simple. We initially assessed the possibility of using the propensity score, with which we could have created a benchmark sample of firms with the same probability of conducting a SEO as our samples firm. The probability would be based on a multiple variables and previous research has found good results on finding the probability of firms issuing equity, however none have used it to put together a benchmark sample. Our model did unfortunately not produce any usable results. Nevertheless it would be an interesting subject for further research. However, our matching method found matches within each industry, and thus our results show the relative change compared to industry peers. As mentioned in section 6.1 we see that within our sample 42% of the firms are from Great Britain, while the proportion of Great Britain stocks in relation to all listed stocks in the European area is about 25%. This leads to the question whether there are structural differences between continental Europe and Great Britain that affect the use of equity offerings as a source of financing. This was out of the scope of this thesis, but could however be worth looking into. Page 90 of 101

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Appendix A - Overview of previous findings Empirical announcement effect studies Study Region Period Size Method Overall DEB GEN INV Asquith & Mullins (1986) US 1963-1981 531 CAR [-1;0] Kim & Purnanandam (2006) US 1994-2003 597 CAR [-2;+1] Korajczyk et al. (1990) US 1974-1983 789 CAR [-1;0] Cox & Aryal (2007) US 1997-2004 77 CAR [-1;+1] Bøhren et al. (1997) Norway 1980-1993 188 CAR [-1;0] Do (2009) Finland 1996-2003 82 CAR [0;+1] Masulis & Korwar (1986) US 1963-1980 690 CAR [0;+1] Hull & Moellenberndt (1994) US 1970-1988 496 CAR [-1;+1] Johnson et al. (1996) US 1979-1988 562 CAR [-1;0] Gajewski & Ginglinger (2002) France 1986-1996 219 CAR [0;+1] Hull et al. (2009) US 1999-2005 1.290 CAR [-2;0] Walker & Yost (2008) US 1997 & 2000 438 CAR [0;+1] Empirical long-term operational performance studies Study Region Period Size Method Overall DEB GEN INV Healy & Palepu (1990) US 1966-1981 93 EPS Hansen & Crutchley (1990) US 1975-1982 109 ROA Loughran & Ritter (1997) US 1979-1989 1.338 ROA & NPM McLaughlin et al. (1996) US 1980-1991 1.296 FCF/TA Heron & Lie (2004) US 1980-1998 4.708 ROA Cox & Aryal (2007) US 1997-2004 77 Multiple ratios Andrikopoulos (2009) UK 1987-2002 1.542 Multiple ratios Bayless et al. (2005) US 1974-1990 1.752 Multiple ratios Autore et al. (2009) US 1997-2003 880 ROA & ROS Walker & Yost (2008) US 1997 & 2000 438 OROA Source: Own contribution Page 96 of 101

Step Appendix B - Sample collection procedure Initial Bloomberg extraction: - AT, BE, DK, FI, FR, DE, GB, IE, IT, LU, NL, NO, ES, SE, CH, PT - 1/1/2000 -> 31/12/2010 - Additional & Primary share offering # SEO 2.957 1. Alternative Investment Market or unidentified market 1.804 2. Withdrawn, Pending or Postponed 43 3. Industry: Financial and Utilities 276 4. Offer size = 0, duplicate or Missing ISIN 9 5. SEO overlap (-3 & +3 years) 373 6. Missing stock data 65 7. Not a SEO (e.g. A to B share swap etc.) 33 8. Thinly traded stocks ( < 40% ) 12 Short-term sample 342 9. Missing or insufficient accounting data 19 Long-term sample 323 Source: Own contribution Page 97 of 101

Appendix C - Match comparison plot Size Match Matching firm 12 10 8 6 4 LN(assets) Matching Firm OROA 0,5 0,3 0,1-1,3-1,1-0,9-0,7-0,5-0,3-0,1-0,1 0,1 0,3 0,5-0,3-0,5-0,7 2 Sample firm 0-3 2 7 12 Performance match LN(assets) Matching firm 12 10 8-0,9-1,1-1,3 Sample firm OROA 0,5 0,3 0,1-1,3-1,1-0,9-0,7-0,5-0,3-0,1-0,1 0,1 0,3 0,5 6 4 2 Sample firm 0-3 2 7 12 Matching Firm -0,3-0,5-0,7-0,9-1,1-1,3 Sample firm Source: Own contribution Page 98 of 101

Appendix D - Description of Nace codes Description Nace Code Number of SEO s Agriculture, forestry and fishing A 4 Mining and quarrying B 33 Manufacturing C 125 Electricity, gas, steam and air conditioning supply D 6 Construction F 16 Wholesale and retail trade; repair of motor vehicles and motorcycles G 29 Transporting and storage H 14 Accommodation and food service activities I 7 Information and communication J 55 Real estate activities L 1 Professional, scientific and technical activities M 27 Administrative and support service activities N 8 Education P 1 Human health and social work activities Q 7 Arts, entertainment and recreation R 9 Total 342 The table provides a description of the different Nace codes used in the thesis. The number of SEO s are the SEO within each industry. Source: Own contribution Appendix E - Short term: Shapiro-Wilk test for normality Variable obs W v z Prob>z [-1;+1] 342 0,8393 38,459 8,621 0,000 [-3;+3] 342 0,9301 16,738 6,656 0,000 [-10;+10] 342 0,9460 12,918 6,044 0,000 Source: Own contribution Appendix F - Long term: Shapiro-Wilk test for normality Variable obs W v z Prob>z OROA 323 0,9209 17,978 6,806 0,000 OROS 323 0,6515 79,291 10,302 0,000 FCF/TA 318 0,9243 16,994 6,668 0,000 Source: Own contribution Page 99 of 101

Appendix G - Quantile regressions Quantile regression - Full sample Independent variables Dependent Pre-SEO Match SEO Age Assets Ind Year N Ps. R variables perf. 2 OROA Size -0,010* -0,385*** 0,003 0,006*** Yes Yes 646 0,162 (0,006) (0,041) (0,003) (0,002) Perf. -0,018** -0,433*** 0,002 0,005*** (0,006) (0,046) (0,003) (0,002) OROS Size -0,008-0,476*** 0,002 0,010** (0,007) (0,171) (0,004) (0,004) Perf. -0,015** -0,431* 0,000 0,006* (0,007) (0,223) (0,004) (0,004) FCF/TA Size -0,002-0,512*** 0,003 0,003* (0,005) (0,062) (0,003) (0,002) Yes Yes 646 0,199 Yes Yes 643 0,102 Yes Yes 644 0,074 Yes Yes 614 0,210 Perf. -0,014** -0,708*** 0,005 0,003 Yes Yes 641 0,268 (0,006) (0,103) (0,003) (0,002) Quantile regression - DEB sample Independent variables Dependent Pre-SEO Match SEO Age Assets Ind Year N Ps. R variables perf. 2 OROA Size -0,004-0,102 0,002 0,004 Yes Yes 158 0,143 (0,008) (0,083) (0,004) (0,003) Perf. -0,009-0,203* 0,002 0,004 (0,009) (0,115) (0,005) (0,003) OROS Size -0,010-0,295*** -0,007 0,005 (0,010) (0,101) (0,005) (0,004) Perf. -0,008-0,359** -0,004 0,004 (0,011) (0,159) (0,005) (0,004) FCF/TA Size -0,001-0,356** 0,001-0,001 (0,008) (0,146) (0,004) (0,003) Perf. -0,002-0,591*** -0,002 0,005* (0,009) (0,127) (0,005) (0,003) Yes Yes 158 0,172 Yes Yes 158 0,227 Yes Yes 158 0,162 Yes Yes 153 0,188 Yes Yes 158 0,341 Page 100 of 101

Quantile regression - GEN sample Independent variables Dependent Pre-SEO Match SEO Age Assets Ind Year N Ps. R variables perf. 2 OROA Size -0,005-0,372*** 0,010* 0,004 Yes Yes 198 0,198 (0,011) (0,084) (0,006) (0,003) Perf. -0,008-0,422*** 0,004 0,002 (0,011) (0,071) (0,006) (0,003) OROS Size 0,002-0,378*** 0,003 0,001 (0,013) (0,111) (0,007) (0,005) Perf. -0,011-0,554** 0,008 0,008 (0,020) (0,247) (0,010) (0,007) FCF/TA Size -0,009-0,436*** 0,007-0,001 (0,013) (0,111) (0,006) (0,005) Perf. -0,021-0,573*** 0,011* -0,000 (0,013) (0,156) (0,006) (0,004) Quantile regression - INV sample Yes Yes 198 0,309 Yes Yes 198 0,214 Yes Yes 198 0,244 Yes Yes 188 0,285 Yes Yes 198 0,246 Independent variables Dependent Pre-SEO Match SEO Age Assets Ind Year N Ps. R variables perf. 2 OROA Size -0,005-0,372*** 0,010* 0,004 Yes Yes 290 0,150 (0,011) (0,084) (0,006) (0,003) Perf. -0,028** -0,453*** 0,003 0,006 (0,012) (0,097) (0,007) (0,004) OROS Size -0,011-0,207*** -0,003 0,005 (0,011) (0,047) (0,007) (0,004) Perf. -0,028* -0,293-0,010 0,004 (0,016) (0,319) (0,009) (0,007) FCF/TA Size -0,016-0,479*** -0,001 0,009** Source: Own contribution (0,010) (0,073) (0,006) (0,004) Perf. -0,022* -0,808*** 0,004 0,003 (0,012) (0,132) (0,007) (0,003) Yes Yes 290 0,214 Yes Yes 289 0,240 Yes Yes 288 0,046 Yes Yes 273 0,254 Yes Yes 287 0,320 Page 101 of 101