Autoria: Eduardo Kazuo Kayo, Douglas Dias Bastos

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1 Frequent Acquirers and Financing Policy: The Effect of the 2000 Bubble Burst Autoria: Eduardo Kazuo Kayo, Douglas Dias Bastos Abstract We analyze the effect of the 2000 bubble burst on the financing policy. Employing differencein-difference estimator we show a significant shift in the firms capital structure. Frequent increased their leverage when compared to the control group of non-frequent. This was mainly due to a decrease in new equity issuance after bubble. In contrast, non-frequent increase net equity financing. We also show that leverage adjustments occur more likely when firms are overleveraged rather than underleveraged. Overleveraged frequent adjust their capital structure by issuing new equity whereas overleveraged non-frequent adjust by decreasing debt issuance. 1

2 1. Introduction The relationship between firms capital structure and their respective investment policy is a fundamental problem in corporate finance research. Contrarily to the Modigliani and Miller s (1958) irrelevance proposition, several studies have been showing important association between investment and financing decisions in the presence of imperfect markets. Aivazian, Ge and Qiu (2005), for instance, show a negative and significant effect of leverage on firm s capital expenditures investments, although this effect holds only for firms with low growth opportunities. For firms with high growth opportunities, the effect of leverage on investment is positive. In other words, when firms have low growth opportunities (i.e., they are in the maturity lifecycle) debt leverage plays an important control mechanism against excessive capital expenditures and, thus, mitigating overinvestment problems. This result corroborates a long standing literature on the disciplinary role of debt in the presence of agency conflicts (e.g., Jensen & Meckling, 1976; Jensen, 1986; Stulz, 1990). On the other hand, when firms have high growth opportunities, leverage exacerbates underinvestment problems. As a result, the trade-off of costs and benefits of using debt in the context of agency theory lead the firms to pursue an optimal structure. In the same sense, firms can also pursue a target capital structure by balancing the tradeoff between the tax shields benefits and costs of financial distress. Regardless the motive and assuming the existence of such a trade-off, firms then start pursuing an appropriate target leverage that take into account their own characteristics (i.e., growth opportunities, size, profitability, etc). Several studies have been studying the determinants of this target leverage and the speed of adjustment toward this target (e.g., Jalilvand & Harris, 1984; Fama & French, 2002; Leary & Roberts, 2005; Flannery & Rangan, 2006) generally concluding for a long term leverage adjustments. Harford, Klasa and Walcott (2009) and Uysal (2011) put the discussion of target capital structure in the context of mergers and acquisitions. Harford et al (2009) show that overleveraged bidders tend to finance their acquisitions with equity. In addition, they also show that when bidders finance their acquisition with debt, their managers actively adjust the capital structure to its target. Uysal (2011) shows that the probability of overleveraged firms making acquisitions is lower and similarly to Harford et al. these firms rebalance their capital structure to their targets. Both papers, however, assume that all firms have the same behavior in relation to financing decisions and, thus, estimate the target capital structure for all firms simultaneously. We extend these two papers by analyzing the heterogeneity of frequent and non-frequent. Our assumption is that frequent have particular characteristics regarding their investment policy (i.e., their main focus is on acquisition strategy as a growth strategy) that make them to have different financing policy as well. Thus, we estimate the target capital structure for frequent and non-frequent separately. In effect, we show that target capital structures are significant different in most of years. In addition, their investment and financing policies tend to be stable over time at least while environmental conditions remain the same. External environment is also an issue we analyze in our paper. In particular, we investigate the effect of the 1990 s technology bubble. Campello and Graham (2013) found evidence that the 1990 s technology bubble substantially affected corporate decisions. They show that during the 1990 s decade the overvalued stock prices relaxed financing constraints allowing particularly constrained firms to issue new equity and increase their investments. This phenomenon did not occur with tech or unconstrained non-high-tech firms, making them conclude that overvaluations in a given industry may lead to positive externalities in another industry. Results from Campello and Graham (2013) suggest the influence of an important exogenous factor on the corporate decisions, namely 1990 s tech bubble. Our main interest is 2

3 to know whether the ending of periods such as this is sufficiently strong to promote a structural break in financing and investment policy. In this context, we analyze the investment and financing policies of frequent and non-frequent assuming that these two groups have different decision behavior patterns. Our results show that the 2000 bubble burst promoted a significant shift in the frequent financing policy. We show that before 2000 both market leverage and target market leverage of frequent were lower than of non-frequent-. After 2000 frequent showed higher levels of leverage and target leverage. A second important result shows that the higher the leverage deficit (i.e., the difference between actual leverage and target leverage), the lower the probability of making an acquisition. However, this result only holds to overleveraged frequent. Third, as expected overleveraged firms tends to issue more equity and underleveraged firms issue less equity. This effect is especially stronger after bubble and for overleveraged firms. Finally, overleveraged firms issue less debt, especially after bubble. Altogether these results suggest that frequent have target leverage, although this target changed in response to a structural break. 2. Acquisitions, capital structure and environmental conditions Rhodes-Kropf and Viswanathan (2004) findings suggest that merger waves are strongly associated with periods of high market valuations. These periods, in turn, may be defined by specific events. In this sense, Harford (2005) states that economic, regulatory and technological shocks are the main drivers of merger waves. However, he also shows that such waves only occur if sufficient capital liquidity is available. Accordingly, Dittmar and Dittmar (2008) show that cycles of economic growth lead to waves of stock issuance as well as waves of mergers and acquisitions. These waves would be a response for an economic expansion during which firms experience a decrease in financial constraints and an increase in their external sources of capital (Harford, 2005). Thus, Dittmar and Dittmar (2008) and Harford (2005) suggest that mergers tend to occur when cash surplus are higher. Incidentally, cash surplus generate the necessary financial slack for firms with high growth prospects to take advantages of acquisition opportunities (Almazan, De Motta, Titman, & Uysal, 2010). Almazan et al. (2010) state that low levels of leverage may improve potential bidders credit rating, which is an important factor for subsequent capital raising aimed at funding future acquisitions. This argument supports the view that frequent should have lower levels of leverage when compared to firms that engage in these deals infrequently. Relevant investment events such as mergers, acquisitions, or even big capital expenditures may be opportune moments for capital structure adjustments (Dudley, 2012). Overleveraged firms (i.e., firms with actual leverage higher than their target) for instance is less likely to make acquisitions (Uysal, 2011) and when they make the preferred source of capital is equity (Harford et al., 2009). 3. Methods 3.1. Sample and data We collected data from two data sources. We use ThomsonOne for acquisition data and Compustat North American for firms financial data. The period of analysis is from 1992 through An important issue in building our matrix is that acquisition data from ThomsonOne is based on events whereas Compustat s data is based on firms. Thus, our first task was to merge different formats of data. Since our main interest is not to analyze individual events but the firm s behavior instead, our matrix takes the form of a panel data of firm-year observations. We then imported the number of acquisitions each firm made in a 3

4 given year. Based on this information, we classified firms into two groups: frequent and nonfrequent. Frequent are firms that made an annual average of at least one acquisition across our period of analysis. Table 1 shows the result of this classification. Table 1: Number of firm-year observations of frequent and non-frequent Year Frequent Non-frequent , , , , , , , , , , , , , ,890 Total 3,496 25,152 Following previous studies (e.g., Almeida & Campello, 2010) we excluded the observations of financial firms (SIC codes ), utilities (SIC codes ) and government agencies and service companies in general (SIC codes above 8000). Moreover, we excluded firm-year observations with Tobin s q greater than 10 and fixed assets lower than US$ 5 million Measures Our study performs four types of analysis: difference-in-difference (DID), ordinary least squares (OLS), probit, and panel data with fixed effects. The definitions of the variables are as follows: Market leverage is Long Term Debt over Total Firm Value. Total Firm Value, in turn, is Price Fiscal Year Close multiplied by Common Shares Outstanding + Total Assets - Stockholders' Equity Total - Cash and Cash Equivalents - Increase (Decrease). Target market leverage, following previous studies (e.g., Harford et al., 2009; Uysal, 2011) is the fitted value of the annual OLS regression of market leverage against some traditional independent variables. We estimate target market leverage for each year of our sample within our subsamples of frequent and non-frequent. The independent variables for target estimation are return on assets, Tobin s q, non-debt tax shield, size, tangibility, research & development expenses, and industry market leverage. Market leverage deficit is the observed market leverage minus target market leverage. Overleveraged firm is a dummy variable that equals 1 if a given firm-year observation has a market leverage deficit falling in the largest quartile. Firms in this quartile have actual market leverages higher than target leverage. Underleveraged firm is a dummy variable that equal 1 if a given firm-year observation has a market leverage deficit falling in the lowest quartile. 4

5 Acquisition is a dummy variable that equal 1 if a firm makes at least one acquisition in a given year. Frequent acquirer is a dummy variable that equals 1 if a given firm makes an average of at least one acquisition per year in the period in analysis. This dummy is constant across time. After bubble is a dummy variable that equals 1 for the years following 2000 bubble burst, more specifically for the years 2003 through High-tech firm is a dummy variable that equals 1 for the following 3-digit SIC codes: drugs (SIC 283), office and computing equipment (SIC 357), communications equipment (SIC 366), electronic components (SIC 367), scientific instruments (SIC 382), medical instruments (SIC 384), and software (SIC 737). This criteria is from Brown, Fazzari, and Petersen (2009). Net equity financing is the difference between Sale of Common and Preferred Stock and Purchase of Common and Preferred Stock. Net debt financing is Long-Term Debt Issuance minus Long-Term Debt Reduction. Return on assets is Operating Income Before Depreciation over Total Assets. Tobin s q is the ratio between the total firm value and total assets. Total Firm Value, in turn, is Price Fiscal Year Close multiplied by Common Shares Outstanding + Total Assets - Stockholders' Equity Total - Cash and Cash Equivalents - Increase (Decrease). Non-debt tax shield is Depreciation and Amortization over Total Assets. Size is the natural logarithm of net sales. Tangibility is Plant, Property, and Equipment over Total Assets. R&D investments is the ratio of Research and Development Expense and Total Assets. Industry market leverage is the median value of market leverage by 2-digit SIC code. Stock return is the natural logarithm of the ratio of the contemporaneous fiscal year closing price over the lagged fiscal year closing price. Herfindhal index, following the criteria of Uysal (2011), is the sum of squares of all the firms market share by 3-digit SIC code Empirical models Our first main interest relies on the shift that frequent promoted in their financing policy after 2000 bubble burst. To test this hypothesis we perform a quasiexperiment running difference-in-difference (DID) estimator. In this context, we test whether an exogenous event such as the bubble burst have affected firm s capital structure in different ways. Our treatment group that has supposedly suffered an important effect from the bubble burst comprises frequent firms. Our control group, in turn, comprises non-frequent. In a typical quasi-experiment, the exogenous event would not affect the control group. In our analysis, however, the control group may also suffer the influence of the exogenous event but in a different way when compared to the treatment group. Eq. (1) shows our DID specification for frequent market leverage. In this model, 1 is the estimate for the market leverage of frequent acquirer, 2 is the estimate for the After Bubble dummy and 3 is the interaction term. MarkLev i,t =.FreqAcq i,t +.AfterBubble i,t +.FreqAcq i,t.afterbubble i,t + i,t (1) Once we identify different patterns in the financing policies of frequent and nonfrequent, the next step is to analyze the factors that affected this evolution. We then perform a panel data analysis with fixed effects as we specify in Eq. (2). In this model, subscripts i and t represents firm i and time t respectively. The parameter is the coefficient for net equity financing, is for net debt financing, is for After Bubble dummy, and 5

6 are the coefficients for the interactions, is the coefficient for an x vector of control variables, year is a set of year dummies, u i is the time invariant firm fixed effect and is the error term. MarkLev i,t+1 =.NetEqFin it +.NetDebtFin it +.AfterBubble t +.NetEqFin it. (2) AfterBubble t +. NetDebtFin it. AfterBubble t + x it + year t + u i + it In the next step we analyze the effect of leverage deficit in the probability of a firm making an acquisition. Eq. (3a) shows our model. Our dependent variable, Acquisition, is a variable that equals 1 if the firm makes at least one acquisition in a given year and zero otherwise. Market leverage deficit is the actual leverage minus the target leverage. We define the remaining variables above. P(Acq = 1 x) it = N( +.MarkLevDef it + X it + year t ) (3a) Additionally, we analyze whether overleveraged and underleveraged firms present different behaviors regarding their acquisition strategies. Eq. (3b) shows the model. P(Acq = 1 x) it = N( +.OverLev it +.UnderLev it + x it + year t ) (3b) By analyzing Eq. (4a) and (4b) we turn our attention to capital structure adjustments. Eq. (4a) analyzes how over and underleveraged firms adjust their capital structure with net equity issuance before and after the bubble burst. On the other hand, the dependent variable of Eq. (4b) is net debt financing. NetEqFin i,t+1 =. OverLev it +.UnderLev it +.AfterBubble t +. OverLev it. (4a) AfterBubble t +.UnderLev it. AfterBubble t + x it + year t + u i + it NetEqFin i,t+1 =. OverLev it +.UnderLev it +.AfterBubble t +. OverLev it. (4b) AfterBubble t +.UnderLev it. AfterBubble t + x it + year t + u i + it Finally, Eq. (5a) and (5b) are DID estimations for the effect of a firm being high-tech in the issuance of equity and debt, before and after bubble burst. NetEqFin i,t =.HighTech i,t +.AfterBubble i,t +.HighTech i,t.afterbubble i,t + i,t (5) 4. Results 4.1. Summary statistics Our main concern in this paper is to understand why and how frequent shift their financing policy after 2000 bubble burst. We begin our analysis presenting the evolution of the average market leverage in Fig. (1a). As we can see, the average market leverage of frequent is lower than of non-frequent during the 1990 s. However, the frequent leverage became higher than non-frequent in the years following the bubble burst. As expected, the same phenomenon happened to target market leverage. 6

7 Figure 1a. Average market leverage Figure 1b. Average target market leverage Table 2 shows the tests for differences in the means of these two groups. We observed that most differences are statistically significant before and after bubble burst. Differences are systematically non-significant in the years surrounding the bubble burst, that is, 1998 through Table 2: Mean values and t-tests for market leverage and target market leverage Market leverage Target market leverage Year FA NFA p-value FA NFA p-value All sample Note: This table shows the mean values of market leverage and target market leverage. FA is frequent and NFA is non-frequent. P-value are of group-mean differences between frequent and non-frequent Difference-in-difference results for market leverage Table 3 shows the results for the estimation of Eq. (1). We estimated DID with OLS and Tobit since a significant number of firms present zero level of leverage. Both estimations led us to observe a significant effect of the aftermath of the bubble burst on market leverage. The parameter for frequent acquirer is negative and statistically significant at 1% level indicating that market leverage for frequent before the bubble was lower than non-frequent. The parameter of the interaction term is our DID estimator. The positive and 7

8 significant coefficient suggests that after bubble the frequent leverage became higher than of non-frequent. Table 3: Difference-in-difference estimation for market leverage Dep.Var.: Market leverage [1] [2] OLS TOBIT Frequent acquirer *** ** (0.003) (0.003) After bubble *** *** (0.002) (0.002) Frequent acquirer x After bubble 0.033*** 0.041*** (0.005) (0.005) Constant 0.134*** 0.121*** (0.001) (0.002) Year fixed effects No No Observations 28,648 28,648 Note: Robust standard errors are in parentheses and ***, **, * represent statistical significance at 1%, 5% and 10%, respectively. Figure 2 shows an intuitive graph of the DID results. We can see that the intercept of frequent is lower in the early 1990 s but increases as time passes by. On the opposite, market leverage of non-frequent decreased in the same period. Figure 2. Fitted values for market leverage across time This finding partly supports the argument that firms predicting the probability of making an acquisition should have low levels of leverage in order to take advantage of this growth opportunity (e.g., Almazan et al., 2010). However, we observed that the bubble burst changed the leverage profile of frequent. Next we analyze the determinants of this change What explains the raising of frequent leverage? Table 4 shows the results of Eq. (2) through which we analyze the factors that affected the evolution of market leverage of frequent and non-frequent. The first column in this table shows the results for our full sample, while columns 2 and 3 show the results for frequent and non-frequent groups. Net equity financing present negative and significant relationship with market leverage, but this effect is not symmetric across the 8

9 different groups. For frequent the sign is positive and significant whereas for nonfrequent is negative and significant. Note that since we have an interaction between net equity financing and the dummy for after bubble, the coefficient of net equity financing refers to the period of pre-bubble burst. Thus, during this pre-bubble period, the higher the net equity financing, the higher the market leverage. Despite the equity increase, market leverage increases because net debt financing was even higher for frequent, as we can see from the positive and significant coefficient of net debt financing in the column 2. The same did not happen to non-frequent. Table 4: Fixed effects panel regression for leaded market leverage Dependent variable: Market leverage t+1 [1] [2] [3] Full sample Frequent Non-Freq. Aquirers Net equity financing ** 0.089** *** (0.014) (0.034) (0.015) Net debt financing 0.125*** 0.167*** 0.118*** (0.024) (0.029) (0.027) After bubble (0.008) (0.013) (0.009) Equity financing x After bubble 0.065*** * 0.080*** (0.017) (0.047) (0.017) Debt financing x After bubble 0.058** ** (0.029) (0.046) (0.032) Return on assets *** *** (0.016) (0.050) (0.017) Tobin's q *** *** *** (0.001) (0.003) (0.001) Non-debt tax shield (0.044) (0.112) (0.046) Size 0.024*** *** (0.003) (0.008) (0.004) Tangibility 0.062** *** (0.026) (0.053) (0.028) R&D investment *** *** (0.032) (0.094) (0.034) Industry market leverage 0.329*** *** (0.064) (0.119) (0.070) Stock return ** (0.002) (0.004) (0.003) Year fixed effects Yes Yes Yes Observations 13,400 1,885 11,515 R-squared Number of firms 1, ,541 Note: Robust standard errors are in parentheses and ***, **, * represent statistical significance at 1%, 5% and 10%, respectively. Table 4 also shows the effect of equity and debt issuance after bubble, through the interactions variables. For the full sample we see positive and significant coefficients for both the interactions with equity and debt financing. Again, these effects are not symmetric across 9

10 the different groups. For frequent after bubble, the sign is negative and significant while for non-frequent the sign is positive and significant. Thus, for frequent, equity financing was the main driver for leverage increase Market leverage deficit and the probability of making acquisitions Table 5 shows the results of Eq. (3a) and (3b) though which we perform probit analyses to test the effect of leverage deficit on the probability of firms making acquisitions. We repeat the analysis of Uysal (2011) but with one important difference. Our measure for target market leverage takes into account the differences between frequent and non-frequent. Uysal (2011) found that a negative relationship between leverage deficit and acquisitions, with this effect being more relevant for overleveraged firms. That is, the higher the firm s leverage in relation to its target, the lower the probability of making acquisitions. In our analysis, we show that this effect holds only with frequent. For the full sample and for the group of non-frequent the market leverage deficit was not statistically significant. The same happened to overleveraged firms. Table 5: Probit regression for the probability of making acquisitions Dependent variable: Acquisition [1] [2] [3] [4] [5] [6] Full Frequent Non-freq. Full Frequent sample Acquirers sample Market leverage deficit * (0.159) (0.385) (0.164) Non-freq. Acquirers Overleveraged firm ** (0.036) (0.070) (0.039) Underleveraged firm (0.037) (0.079) (0.040) Size 0.153*** 0.068** 0.036** 0.175*** 0.076*** 0.066*** (0.016) (0.027) (0.016) (0.014) (0.022) (0.014) Stock return (0.025) (0.067) (0.027) (0.020) (0.056) (0.022) Tobin s q 0.042*** 0.087** *** 0.095*** (0.015) (0.036) (0.016) (0.013) (0.032) (0.014) Return on assets 0.527*** 1.529*** 0.644*** 0.305** 0.850* 0.447*** (0.156) (0.547) (0.154) (0.136) (0.460) (0.136) Herfindahl index (1.837) (3.475) (1.548) (1.235) (2.616) (1.093) Constant *** *** *** *** (0.128) (0.255) (0.124) (0.111) (0.216) (0.109) Year fixed effects Yes Yes Yes Yes Yes Yes Observations 17,601 2,450 15,151 25,918 3,301 22,617 Number of firms 1, ,677 2, ,277 Note: Robust standard errors are in parentheses and ***, **, * represent statistical significance at 1%, 5% and 10%, respectively Market leverage deficit and capital structure adjustments Table 6 shows the results of Eq. (4a). We analyze how frequent and non-frequent adjust their capital structure by issuing new equity. Models 1, 2 and 3 of Table 6 show the results without the effect of bubble burst. As expected, overleveraged firms regardless of being frequent or not tend to issue more equity. The coefficients are 10

11 positive and statistically significant at 1% level. Underleveraged firms, in turn, present a negative and significant sign. However, results are somewhat different when we consider the effect of bubble in models 4, 5 and 6. These differences appear to be more relevant for underleveraged firms. In the pre-bubble period, we found a negative and significant sign for underleveraged firms, except for frequent. After bubble, results are statistically insignificant for underleveraged firms. Table 6: Fixed effects panel regression for net equity financing Dependent variable: Net equity financing [1] [2] [3] [4] [5] [6] Full sample Frequent Non-freq. Full sample Frequent Non-freq. Overleveraged firm 0.011*** 0.018*** 0.010*** 0.006* (0.003) (0.005) (0.003) (0.003) (0.005) (0.004) Underleveraged firm *** * *** *** *** (0.002) (0.005) (0.003) (0.003) (0.006) (0.004) After bubble 0.021*** *** (0.007) (0.013) (0.007) Overlev. firm x After bubble 0.009* 0.022** (0.005) (0.009) (0.005) Underlev. firm x After bubble (0.005) (0.009) (0.005) Return on assets ** * ** * (0.027) (0.056) (0.029) (0.027) (0.055) (0.029) Tobin's q 0.004** *** 0.004** *** (0.002) (0.003) (0.002) (0.002) (0.003) (0.002) Non-debt tax shield 0.116*** *** 0.115*** *** (0.037) (0.072) (0.041) (0.037) (0.071) (0.041) Size *** *** *** *** *** *** (0.004) (0.007) (0.004) (0.004) (0.007) (0.004) Tangibility 0.071*** *** 0.070*** *** (0.018) (0.035) (0.020) (0.019) (0.034) (0.020) R&D investment 0.325*** *** 0.325*** *** (0.058) (0.120) (0.062) (0.058) (0.118) (0.062) Industry market leverage 0.076** *** 0.077** *** (0.031) (0.057) (0.035) (0.031) (0.058) (0.035) Stock return 0.010*** 0.008** 0.009*** 0.009*** 0.008** 0.009*** (0.002) (0.003) (0.002) (0.002) (0.003) (0.002) Year fixed effects Yes Yes Yes Yes Yes Yes Observations 14,179 2,067 12,112 14,179 2,067 12,112 R-squared Number of firm_id 1, ,494 1, ,494 Note: Robust standard errors are in parentheses and ***, **, * represent statistical significance at 1%, 5% and 10%, respectively. Table 7 shows the results of Eq. (4b), which analyzes the capital structure adjustments with respect to debt issuance. Results for overleveraged firms seem to be more consistent across all models. As expected, overleveraged firms issue less debt and underleveraged more debt. These results support the idea that firms have target capital structures and actively use debt issuances to make the appropriate adjustments toward this target. However, this pattern 11

12 was more evident in the pre-bubble period. After bubble, only non-frequent overleveraged firms systematically reduce debt to pursue their target. Underleveraged firms, in turn, in the pre-bubble period made the expected adjustments in their capital structure increasing debt or decreasing equity, but only the non-frequent. After bubble, however, underleveraged firms did not make any adjustments at all. Table 7: Fixed effects panel regression for net debt financing Dependent variable: Net debt financing [1] [2] [3] [4] [5] [6] Full sample Frequent Non-freq. Full sample Frequent Non-freq. Overleveraged firm *** *** *** *** *** *** (0.003) (0.005) (0.004) (0.004) (0.006) (0.005) Underleveraged firm 0.017*** 0.015** 0.017*** 0.017*** *** (0.003) (0.006) (0.003) (0.003) (0.009) (0.003) After bubble (0.006) (0.012) (0.007) Overlev. Firm x After bubble *** *** (0.005) (0.010) (0.006) Underlev. Firm x After bubble (0.004) (0.013) (0.004) Return on assets *** *** (0.021) (0.060) (0.022) (0.021) (0.061) (0.022) Tobin s q 0.008*** *** 0.008*** *** (0.002) (0.003) (0.002) (0.002) (0.003) (0.002) Non-debt tax shield (0.033) (0.051) (0.037) (0.033) (0.051) (0.037) Size (0.003) (0.007) (0.003) (0.003) (0.007) (0.003) Tangibility 0.050** ** 0.052*** ** (0.020) (0.045) (0.021) (0.020) (0.044) (0.021) R&D investment 0.071** * 0.069** * (0.032) (0.110) (0.033) (0.031) (0.109) (0.032) Industry market leverage *** *** *** *** (0.044) (0.082) (0.048) (0.044) (0.081) (0.048) Stock return (0.002) (0.006) (0.002) (0.002) (0.006) (0.002) Year fixed effects Yes Yes Yes Yes Yes Yes Observations 14,897 2,112 12,785 14,897 2,112 12,785 R-squared Number of firm_id 1, ,533 1, ,533 Note: Robust standard errors are in parentheses and ***, **, * represent statistical significance at 1%, 5% and 10%, respectively. Altogether, our results suggest that target capital structure adjustments are more important for overleveraged firms in general. From a certain point of view, our result supports Byoun s (2008) findings that the speed of adjustment toward target leverage is higher when firms are overleveraged. These adjustments were even stronger after bubble. However, the way overleveraged firms adjust their capital structure depends on whether they are frequent 12

13 or not. After bubble, frequent adjust their capital structure by issuing new equity while non-frequent reduce their debt issuances High-tech firms and external financing Our final analysis tests whether firms being from high-technology industries affect the volume of net equity and debt issuances. One may argue that the 2000 bubble burst should have affected high-tech firms more intensively than non-high-tech. Although our findings show that both net equity and debt issuances significantly decreased in the aftermath of bubble burst, the consequences were not symmetric. Table 8 shows the results of Eq. (3a) and (3b). From columns 1 and 2 which analyze the net equity financing, we can see that high-tech firms issued more equity than non-high-tech firms in the pre-bubble. However, as the negative coefficient of the dummy after bubble suggests, the issuance of new equities from non-hightech-firms significantly decreased after bubble burst. The interaction term indicates that this decrease were even stronger for high-tech firms. Fig. (3a) illustrates this phenomenon. Table 8: Difference-in-difference estimation for net equity and net debt financing Net equity financing Net debt financing [1] [2] [3] [4] High-tech firms 0.039*** (0.003) (0.003) (0.002) (0.003) After bubble *** *** *** *** (0.002) (0.002) (0.002) (0.002) High-tech firms x After bubble *** *** (0.004) (0.003) (0.003) (0.003) Return on assets *** *** (0.015) (0.012) Tobin s q 0.009*** 0.006*** (0.001) (0.001) Non-debt tax shield ** *** (0.029) (0.028) Size *** 0.002*** (0.000) (0.000) Tangibility 0.016*** 0.027*** (0.005) (0.006) R&D investment *** *** (0.025) (0.022) Industry market leverage (0.013) (0.017) Stock return 0.023*** *** (0.002) (0.002) Constant 0.011*** 0.067*** 0.011*** (0.001) (0.005) (0.001) (0.004) Year fixed effects No No No No Observations 25,889 15,740 26,882 16,578 Note: Robust standard errors are in parentheses and ***, **, * represent statistical significance at 1%, 5% and 10%, respectively. As we can see from models 3 and 4 of Table 8, the dummy variable High-tech as well as its interaction with the dummy for after bubble are statistically insignificant. However, 13

14 when we test if these two parameters are jointly equal to zero we reject the null hypothesis and conclude that they are significant (F = 17.83; p-value <.001). Thus, we accept the positive signs of both the dummy for high-tech firms and for the interaction variable. These results show that in the pre-bubble period the net debt financing were slightly higher for hightech firms. However, after bubble burst, net debt financing for non-high-tech firms also decreased. High-tech firms also decreased their net debt financing but not as much as the nonhigh-tech firms. Fig. (3b) illustrates this phenomenon. Figure 3a. Net equity financing for high-tech and non-high-tech firms Figure 3b. Net debt financing for high-tech and non-high-tech firms 5. Conclusion Our goals with this paper were threefold. First, we seek to analyze the financing policy of frequent versus non-frequent. We showed that frequent had lower levels of both market leverage and target market leverage before 2000 bubble burst. However, after bubble burst frequent acquirer s leverage and target leverage became higher than of non-frequent. Second, we analyze the relationship between market leverage deficit and the probability of a firm making an acquisition. We show that overleveraged frequent have lower probability of making acquisitions, but the same does not happened to overleveraged non-frequent. Third, we analyze how firms adjust their capital structure. We show that this adjustment happened more intensively in overleveraged firms, although in different ways. Frequent make adjustments by issuing new equity and non-frequent reducing their debt issuance. We believe that our results support the findings of previous studies which suggest that financial constraints decrease in periods of overvalued market values. In addition, we show evidence that this effect was more relevant for frequent in the overvalued 1990 s period. After this period, we see a price correction due to the bubble burst which led to important shifts in the financing strategies to fund acquisitions. We show that both net equity issuing and net debt issuing decreased in the aftermath of the bubble burst. Underleveraged firms, regardless whether they are frequent or not, does not seem to be concerned on pursuing target capital structure. Underleveraged firms, taking advantage of their financial slack, could increase their net debt issuance or reduce their net equity issuance. However, our results suggest that these two strategies are not relevant for them. Finally, we hope to add to literature on capital structure and mergers & acquisition, and more specifically discussing how firms adjust their leverage in response to specific growth strategies. Moreover and perhaps more importantly, we want to call the attention to the fact that the estimation of target leverage must explicitly take into account some firm s time 14

15 invariant characteristics. We argue that target leverage can be different depending on corporate strategy. In our paper, for instance, we assume that frequent are quite different from non-frequent acquirer with regard to their financial strategies. References Aivazian, V. A., Ge, Y., & Qiu, J. (2005). The impact of leverage on firm investment: Canadian evidence. Journal of Corporate Finance, 11(1 2), Almazan, A., De Motta, A., Titman, S., & Uysal, V. (2010). Financial structure, acquisition opportunities, and firm locations. The Journal of Finance, 65(2), Almeida, H., & Campello, M. (2010). Financing Frictions and the Substitution between Internal and External Funds. Journal of Financial and Quantitative Analysis, 45(3), 589. Brown, J. R., Fazzari, S. M., & Petersen, B. C. (2009). Financing innovation and growth: Cash flow, external equity, and the 1990s R&D boom. The Journal of Finance, 64(1), Byoun, S. (2008). How and when do firms adjust their capital structures toward targets? The Journal of Finance, 63(6), Campello, M., & Graham, J. R. (2013). Do stock prices influence corporate decisions? Evidence from the technology bubble. Journal of Financial Economics, 107(1), Dittmar, A. K., & Dittmar, R. F. (2008). The timing of financing decisions: An examination of the correlation in financing waves. Journal of Financial Economics, 90(1), Dudley, E. (2012). Capital structure and large investment projects. Journal of Corporate Finance, 18(5), Fama, E. F., & French, K. R. (2002). Testing trade-off and pecking order predictions about dividends and debt. The Review of Financial Studies, 15(1), Flannery, M. J., & Rangan, K. P. (2006). Partial adjustment toward target capital structures. Journal of Financial Economics, 79(3), Harford, J. (2005). What drives merger waves? Journal of Financial Economics, 77(3), Harford, J., Klasa, S., & Walcott, N. (2009). Do firms have leverage targets? Evidence from acquisitions. Journal of Financial Economics, 93(1), Jalilvand, A., & Harris, R. S. (1984). Corporate behavior in adjusting to capital structure and dividend targets: An econometric study. The Journal of Finance, 39(1), Jensen, M. C. (1986). Agency costs of free cash flow, corporate finance, and takeovers. The American Economic Review, 76(2), Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), Leary, M. T., & Roberts, M. R. (2005). Do firms rebalance their capital structures? The Journal of Finance, 60(6), Modigliani, F., & Miller, M. H. (1958). The cost of capital, corporation finance and the theory of investment. American Economic Review, 48(3), Rhodes-Kropf, M., & Viswanathan, S. (2004). Market valuation and merger waves. The Journal of Finance, 59(6), Stulz, R. M. (1990). Managerial discretion and optimal financing policies. Journal of Financial Economics, 26(1), Uysal, V. B. (2011). Deviation from the target capital structure and acquisition choices. Journal of Financial Economics, 102(3),

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