How do individual investors react to global IFRS adoption?

Transcription

1 How do individual investors react to global IFRS adoption? Ulf Brüggemann Lancaster University Holger Daske University of Mannheim Carsten Homburg University of Cologne Peter F. Pope Lancaster University March 2011 We appreciate the helpful comments of Kevin Aretz, Hans Christensen, Gary Entwistle, Markus Glaser, Joerg- Markus Hitz, Christian Leuz, Steve Lin, Steve Zeff, and seminar participants at HU Berlin, the University of Chicago, Lancaster University, KU Leuven, Erasmus University Rotterdam, WHU Vallendar, the 2009 AS- VHB/IAAER meeting in Munich, the 2009 EAA meeting in Tampere, the 2009 AAA meeting in New York, the 2010 FARS Mid-Year meeting in San Diego, the 2010 Accounting Research Conference at Penn State and the INTACCT meetings in Frankfurt, Cyprus, London and Valencia. We are grateful to Markus Glaser for providing us with statistics from his brokerage dataset. Thanks are also due to Paul Rayson for expert advice and Eddie Bell for programming assistance on the collection of Google News archive search results. Ulf Brüggemann, Holger Daske and Peter F. Pope gratefully acknowledge the financial contribution of the European Commission Research Training Network INTACCT (Contract MRTN-CT ). Part of this research was carried out while Ulf Brüggemann was visiting the University of Chicago. Correspondence:

2 How do individual investors react to global IFRS adoption? Abstract We examine the impact of global IFRS adoption on cross-border equity investments by individual investors. Our proxy for these cross-border equity investments is trading volume in the Open Market at Frankfurt Stock Exchange. The Open Market is a segment designed for German individual investors to trade a large selection of foreign stocks. Using a sample of 5,637 firms from 31 countries around the world, we find that stocks experience an increase in Open Market trading volume following mandatory adoption of IFRS. This increase is both economically and statistically significant. Our results are consistent with the idea that collective IFRS adoption has the potential to reinforce cross-border equity investments by individual investors. JEL classification: G14, G38, K22, M41, M48 Key Words: Individual Investors, International Accounting, IFRS, Open Market, Cross-Border Investments

3 1. Introduction Over the last decade, International Financial Reporting Standards (IFRS) have been introduced in over 100 countries around the world (see useias.htm). Regulators justify the move towards IFRS by the expectation that collective adoption of IFRS will, among other benefits, enhance the comparability of financial statements across countries and, thus, reinforce foreign equity investments (e.g., EC Regulation No. 1606/2002). In this paper, we evaluate this claim by analyzing the impact of mandatory IFRS adoption on cross-border equity investments by individual investors. 1 Despite the focus on institutional investors in prior literature, individual investors play a vital role in financial markets. At the end of 2007, domestic individuals directly owned 14% of the market value of listed stocks in Europe (FESE, 2008). In the United States (U.S.), more than 20% of equity is held directly by individual investors (French, 2008). Anecdotal evidence suggests that individual investors are more likely to pursue long-term objectives than their institutional counterparts. Companies therefore make great efforts to attract individual investors, e.g. via corporate websites and investor relations departments (Vogelheim et al., 2001). The relevance of individual investors is also recognized by regulators. For example, one of the explicitly stated purposes of the U.S. Securities and Exchange Commission (SEC) is to extend individual investor protection (SEC, 2008). Mary Shapiro, the current SEC chair, emphasizes that without rules to protect [individual] investors, financial systems will not raise capital and the economy will not grow (FINRA, 2008). 1 We use the term individual investors to refer to non-institutional investors. Retail investors and private investors are synonymous expressions used in prior studies. It is interesting to note that some of the earliest research examining individual investors use and understanding of financial statement information was provided by Sir David Tweedie, the long-time Chairman of the IASB (e.g., Lee and Tweedie, 1977). 1

4 We predict that following global IFRS adoption 2 individual investors increase their investments in foreign IFRS stocks. This prediction is based on two assumptions. First, accounting information affects cross-border equity investments of individual investors, either because they use financial statements by themselves and/or because they consult information intermediaries (e.g., the business media or financial advisors) that in turn rely on financial statements. The second assumption is that country-specific accounting standards create entry barriers to investments in foreign stocks, because individual investors or the respective information intermediaries do not have the resources to familiarize themselves with local GAAPs. Global IFRS adoption removes these entry barriers by replacing country-specific accounting rules with one single set of standards. We test our prediction by using trading volume in the Open Market at Frankfurt Stock Exchange (FSE) as a proxy for cross-border equity investments by individual investors. The Open Market is an unofficial trading segment designed for German individual investors to trade a selection of foreign (i.e., non-german) stocks that have their main listing at a home market abroad. This selection is very large (about one quarter of all firms in the Datastream Universe see Table 1), because Open Market lead brokers are allowed to independently include securities in the Open Market at low cost and without any involvement by the issuer. 3 Although these lead brokers set higher bid-ask spreads, the Open Market provides a cost-efficient alternative to the home markets when trade sizes are low. German retail banks and brokers pass on high, mostly 2 3 The European Union, Australia, Hong Kong, South Africa and many other countries around the world mandated IFRS or IFRS equivalents for most listed firms from fiscal year 2005 onwards. The treatment group in our empirical analysis is confined to IFRS adopters from these countries (see Table 1, Panel A). Here and in the following, we therefore use the term global IFRS adoption to refer to the mandatory introduction of IFRS in The lead brokers are not required to inform issuers, let alone seek approval for inclusion of securities in the Open Market. This feature of the Open Market is similar to unsponsored (involuntary) cross-listings in the U.S. that became possible following a recent SEC disclosure deregulation (Iliev et al., 2010). 2

5 fixed order fees when local clients choose to trade directly on a foreign exchange, whereas fixed charges for trading at FSE are considerably lower. For small trade sizes, the higher bid-ask spreads are therefore outweighed by lower order processing costs in the Open Market (see Appendix A for an illustrative example). In short, Open Market lead brokers provide German individual investors with cheaper access to foreign stocks. There are two reasons why increases in Open Market trading volume proxies for increases in cross-border equity investments. First, liquidity in the Open Market is low so that the lead broker typically cannot match offsetting orders by the individual investors. Instead, the lead broker has to carry out a countertrade in the respective home market to rebalance her inventory. Trading volume in the Open Market therefore largely reflects changes in cross-border equity investments by German individual investors rather than trades between these investors. The second reason is that Open Market investors have no restrictions on the buying side, while they can only sell stocks they own. We conclude from this observation that increases in Open Market trading volume are more likely to reflect increases rather than decreases in cross-border equity investments. Appendix B provides statistics from a brokerage dataset that support this conclusion. Our empirical analysis is based on a proprietary dataset from FSE that contains information on trading volume in the Open Market for the period January 2002 to June The Open Market sample comprises 5,637 (43,671) unique firms (firm periods) from 31 countries around the world. Descriptive statistics confirm that liquidity in the Open Market is low. For example, during the average firm period, trading occurs on slightly less than 25% of all trading days. The mean (median) trade size in the Open Market is about 2,700 (1,700) Euro which corroborates that this segment is used by individual investors. Event study results show Open Market trading 3

6 volume increases significantly around annual earnings announcements. This finding lends support to the assumption that accounting information affects Open Market trading volume as our proxy for cross-border equity investments by individual investors. In our main analysis, we employ a difference-in-differences design to compare the impact of global IFRS adoption on Open Market trading volume of the treatment group (IFRS adopters) versus the control group (non-adopters). The regression results show that Open Market trading volume of mandatory IFRS adopters increases by more than 50% relative to the control group. This effect is robust to the inclusion of variables that control for concurrent changes in market value, home market trading volume, stock return volatility and media coverage in Germany. Further tests show that the estimated IFRS effects are not driven by firms that start preparing their financial statements in English or by concurrent reductions in the bid-ask spread difference between the Open Market and the respective home markets. The effect for voluntary IFRS adopters following global IFRS adoption is weaker and not always statistically significant. In additional tests on the identification of the IFRS effect, we first repeat the difference-indifferences analysis by artificially moving the starting period of global IFRS adoption. The results confirm that the estimated IFRS effects in the main analysis reflect a structural break in Open Market trading volume rather than the continuation of country-specific time trends. In the second set of additional tests, we perform within-country analyses for the United Kingdom and Australia. The results show that mandatory IFRS adopters listed in the Main Market at London Stock Exchange (LSE) experience a significant increase in Open Market trading volume following global IFRS adoption relative to LSE companies that were not required to adopt IFRS before fiscal year In the Australian analysis, we test whether the estimated IFRS effect is 4

7 stronger for early than for late mandatory adopters. The relevant coefficient estimates have the expected sign but are not statistically significant. In the final set of analyses, we examine cross-sectional variation in the estimated IFRS effects by partitioning the treatment group. In contrast to the majority of IFRS literature, our results provide no evidence that the estimated IFRS effects are associated with proxies for the strength of the enforcement system or other country level variables. One explanation for this finding is that our analyses are based on the Open Market sample. Descriptive statistics show that this sample is a non-representative subset of the universe of global stocks comprising highly visible and transparent companies. Potential variation in the estimated IFRS effect due improper implementation of IFRS is therefore likely to be mitigated in our setting. In firm level analyses, we find that the estimated IFRS effect is significantly stronger for firms with high differences between the IFRS net income and net income under local GAAP (based on the restated numbers for fiscal year 2004), and for firm periods where Open Market trading volume is clustered around earnings announcements. We hesitate to draw causal conclusions and instead interpret these findings as interesting descriptive evidence. In additional tests, we show that the estimated IFRS effect is confined to stocks with low pre-ifrs Open Market trading volume. This finding further supports that our results reflect increases in cross-border equity investments rather than increased trading between Open Market investors. Finally, we provide tentative evidence that the estimated IFRS effect is not associated with subsequent stock returns. This result alleviates concerns that the estimated IFRS effect may reflect an increase in cross-border equity investments that turn out to be detrimental to the Open Market investors. Taken together, our empirical analyses provide strong evidence that global IFRS adoption is associated with increases in trading volume in the Open Market. While we cannot fully identify 5

8 the causal mechanism that drives this association, our results are generally consistent with the idea that collective IFRS adoption enhances cross-border investments by individual investors. This paper makes several contributions to literature. First, we add to the emerging research on the economic consequences of IFRS. To our knowledge, we provide the first analysis of how individual investors react to global IFRS adoption. Our study is most closely related to DeFond et al. (2011) and Yu (2009) who find that foreign mutual fund ownership increases following mandatory IFRS adoption. Since institutional investors exhibit systematically different investment patterns than individuals (e.g., Bhattacharya, 2001; Malmendier and Shantikumar, 2007), it is not clear ex-ante whether these insights from the mutual fund industry predict individuals investors reaction to global IFRS adoption. Second, our study is related to the home bias literature. Prior research offers both information-based and behavioral explanations for individuals tendency to overinvest in local assets (e.g., Ivković and Weisbenner, 2005; Graham et al., 2009). We add to this research by providing evidence that cross-country heterogeneity in accounting standards also contributes to the home bias of individual investors. Finally, we introduce a novel and unique setting into the currently highly dynamic literature on individual investors (e.g., Lawrence, 2011; Loughran and McDonald, 2010). This setting allows us to directly observe the aggregate trading activities of a large and homogenous group of individual investors, i.e. German individual investors trading in foreign stocks. 2. The Open Market 2.1. INSTITUTIONAL BACKGROUND The Open Market ( Freiverkehr in German) is an unofficial trading segment at FSE. In contrast to official stock market segments in Europe (e.g. Prime and General Standard at FSE, Main Market at London Stock Exchange), the Open Market is not subject to regulations and 6

9 directives of the European Union (EU), but is exclusively governed by stock exchange rules. It covers a variety of financial instruments such as stocks (both from Germany and abroad), bonds, certificates and warrants. The stocks segment is structured into the First Quotation Board and the Second Quotation Board. The First Quotation Board contains companies with a primary listing in the Open Market. 4 Companies whose stocks are already listed at another domestic or foreign trading venue (home market) are included in the Second Quotation Board. Since the Open Market is an unofficial trading segment, the EU regulation mandating IFRS is not applicable to companies in the First Quotation Board. In contrast, many companies in the Second Quotation Board are obliged to prepare their financial statements in accordance with IFRS due to regulation in the respective home markets. In this study, we focus on the foreign (i.e. non-german) stocks in the Second Quotation Board. For simplicity, we refer to this sub-segment as the Open Market. 5 Established in 1987, the Open Market has become increasingly popular with German investors in recent years. At the end of 2000, a total of 4,544 foreign stocks were traded in the Open Market. This number doubled to 9,170 by the end of For comparison, the number of domestic stocks traded at FSE increased by only 12% from 905 to 1,018 during the same period (FSE, 2000; FSE, 2009). The remarkably high number of foreign stocks available for trading in 4 5 In 2005, FSE introduced the Entry Standard as a sub-segment of the First Quotation Board. Transparency requirements in the Entry Standard are higher than in the rest of the Open Market, but considerably lower than in official FSE stock market segments. While the Entry Standard is open to all companies, it is specifically targeted at small- and mid-caps that seek low cost access to the capital market. The Entry Standard is marketed as an alternative to the Alternative Investment Market at London Stock Exchange (e.g. Sudmeyer et al., 2005; Schlitt and Schäfer, 2006). At the end of 2009, 103 (13) German (foreign) companies were listed in the Entry Standard (FSE, 2009). Official resources and the academic literature provide only little information on the Second Quotation Board of the Open Market. Much of the following description is based on the insights we gained from interviews with FSE staff and brokers. For more general information on the Open Market see e.g. Müller-Michaels and Wecker (2005), Harrer and Müller (2006) or the website of FSE: 7

10 the Open Market is a consequence of its unique set of rules. 6 These rules permit eligible brokerage houses, i.e. those that are accredited for trading at FSE, to include securities in the Open Market on their own initiative ( 2 (3) AGB). The stock issuing company need not be informed, nor need it approve inclusion of its securities in the Open Market. For the brokerage house, the inclusion process involves two basic requirements. First, it has to guarantee orderly fulfillment of transactions by acting as a lead broker ( 14 (1) AGB). Second, it has to pay a nonrecurring fee of 750 Euro ( 35 AGB). Follow-up obligations of the lead broker are confined to informing the FSE about essential company news concerning the issuer that can be acquired by generally accessible information sources in a reasonable way ( 16 AGB). Lead brokers are authorized to exclude securities from the Open Market at any time with a notice period of four weeks, or without notice upon good cause ( 33 (2) AGB). 7 In summary, brokerage houses face very few constraints or institutional barriers relating to inclusion or exclusion of securities in the Open Market. Once a security has been included, the lead broker holds the exclusive right to set bid and ask quotes. 8 Although officially non-binding ( 79 FSE Exchange Rules), these quotes are de-facto tradable up to a size the lead broker specifies (Freihube et al., 1999). When an investor places an order to trade on the bid (ask) quote, the lead broker buys (delivers) the agreed number of stocks. The resulting position would then be immediately closed with an offsetting order to entirely We refer to this set of rules as AGB in the following. AGB stands for Allgemeine Geschäftsbedingungen für den Freiverkehr an der Frankfurter Wertpapierbörse (General Terms and Conditions for the Regulated Unofficial Market). Order books of Open Market securities can also be terminated by FSE ( 33 (1) AGB). For example, in December 2005, FSE suspended trading in Turkish stocks until further notice because of unanswered questions about a planned tax on Turkish equities (Greil, 2005). In case more than one party applies to be the lead broker for a particular stock, the allocation of the order book is decided by lot. Baader Bank AG, mwb fairtrade Wertpapierhandelsbank AG and Wolfgang Steubing AG Wertpapierdienstleister are the leading brokerage houses in the Open Market (Hiller von Gaertringen, 2006), but there are a number of other competitors. Detailed information on the allocation of Open Market order books is not publicly available. 8

11 eliminate inventory risk. However, due to market illiquidity (see below for supporting evidence), perfect offsetting may not be possible for Open Market stocks. The lead broker is then forced to carry out inventory rebalancing countertrades in another market, typically the home market where liquidity is generally much higher. Hence, in setting bid and ask quotes the lead broker faces a trade-off. On one hand, she has an incentive to offer low bid-ask spreads to generate trades and earn brokerage fees. On the other hand, there is the risk of losing out on trades if countertrades at the home markets are carried out at unfavorable prices. The resulting Open Market quotes are therefore likely to be determined by the home market bid-ask spread as a lower bound plus a premium that reflects the price risks faced by lead brokers in executing inventory rebalancing and due to currency risk exposure during trade execution. Other factors with a potential impact on the bid-ask spread premium in the Open Market include trading volume (i.e. the likelihood that the lead broker is able to match offsetting orders) and competition to the lead broker s services. Competition may arise from other German exchanges, e.g. in Berlin, Stuttgart or Munich, where similar but much smaller trading segments exist. Within the FSE, trading volume can shift from floor trading where the lead broker operates to the fully electronic platform XETRA where quotes are automatically determined by an open limit order book. We provide more details on these alternative trading channels in Appendix C. Despite high bid-ask spreads, the Open Market provides a cost-efficient alternative to the home markets under certain circumstances. German retail banks and brokers pass on high, mostly fixed order fees when local clients choose to trade directly on a foreign exchange, whereas fixed charges for trading at FSE are considerably lower. Hence, for small trade sizes the higher bid-ask spreads (i.e. variable transaction costs that increase with trade size) are outweighed by lower order processing costs (i.e. mostly fixed transaction costs that are 9

12 independent of trade size) in the Open Market. The combination of low fixed and high variable fees in the Open Market is likely to be particularly attractive for German individual investors who trade small sizes of foreign stocks. 9 Put differently, the Open Market lead broker provides German individual investors with cheaper access to foreign stocks. 10 Appendix A provides an illustrative example on these links. The first part of Appendix B confirms empirically that individual investors of a German online broker trade foreign stocks primarily through the Open Market, particularly when trade sizes are low DATA AND DESCRIPTIVE STATISTICS In this section, we describe the quantitative features of the Open Market. The analysis is based on two main samples: (1) a main sample defined as the Datastream (DS) Universe, and (2) the Open Market sample - a subset of the DS Universe. The DS Universe includes all firms covered by Datastream that meet the following requirements: At the country-level, we focus on firms domiciled in countries that either introduced IFRS in 2005 (the treatment group) or mandated domestic accounting standards throughout the sample period (the control group). 11 At the firmlevel, the DS Universe is restricted to companies that have their primary listing on the main exchange of their country of domicile (home markets) 12 and for which sufficient data on trading Here and in the following, we refer to Open Market investors as German individual investors for two reasons. First, the economics of the Open Market show that Open Market investors trade through German retail banks and brokers (see Appendix A). Due to institutional barriers most German retail banks and brokers require their clients to be domiciled in Germany. Second, German retail banks and brokers typically target their services at investors who speak German. For example, the web-portal of comdirect bank ( the leading online broker in Germany, provides information in German language only. Note that institutional investors typically (1) have preferred and cheaper access to foreign markets through their lead brokerage houses and (2) trade in volumes well above the break-even where the foreign market turns into the more cost-efficient trading alternative. We deliberately exclude Germany (our focus is on foreign, i.e. non-german, stocks), New Zealand (IFRS introduction in 2007), Singapore (IFRS introduction in 2003), Switzerland (no mandatory IFRS introduction, many listed firms use IFRS by choice) and Turkey (IFRS introduction in 2006) from the DS Universe. The main exchange is defined as the trading venue with the largest number of companies listed. We consider only one exchange per country except for the United States where firms from both New York Stock Exchange 10

13 volume, stock returns and required accounting data is available. We only include companies in the treatment group that switched from local GAAP to IFRS in 2005 (mandatory IFRS adopters) or before 2005 (voluntary IFRS adopters). 13 The control group consists of companies that used domestic accounting standards throughout the sample period. The Open Market sample covers all firm-years within the DS Universe during which the respective stock could be traded on the FSE. We identify this sample using a proprietary dataset from FSE containing daily trading volume data (both in Euros and in number of shares traded) as well as the number of ticks for every stock traded in the Open Market during the sample period. The FSE trading volume dataset spans the period January 2002 to June For consistency, we confine capital market data from Datastream (e.g. trading volume for the home markets) to the same period. We segment the dataset into firm periods of six months each. Thus, our dataset covers a maximum of 13 periods per firm: 2002H1 to 2008H1 with H1 (H2) indicating the first (second) half of the respective year. Table 1 presents details on the composition of the DS Universe and the Open Market sample. Panel A focuses on the treatment group, i.e. countries that introduced IFRS in The DS Universe consists of 266 (6,192) voluntary (mandatory) IFRS adopters from 22 countries. The Open Market sample covers 50% (27%) of all voluntary (mandatory) IFRS adopters in the DS Universe. Panel B shows that the DS Universe comprises 15,822 firms from 19 countries within the control group. 24% of these companies are part of the Open Market sample. Open Market 13 (NYSE) and NASDAQ are included. By focusing on the main exchange(s) in each country, we exclude companies listed at less regulated trading venues (such as the OTC Bulletin Board in the U.S.) and thus ensure a minimum level of transparency among sample firms. Thus, in order to obtain a clean sample, companies from the treatment group countries that did not switch to IFRS during the sample period (e.g., firms that need not prepare consolidated financial statements), did so after 2005 (e.g., firms listed on the Alternative Investment Market at London Stock Exchange) or applied U.S.-GAAP (e.g., due to a cross-listing in the U.S.) are not considered. We include firms from the Alternative Investment Market in an additional analysis in section

14 coverage differs substantially across countries and firms. For example, while the majority of Austrian and U.S. stocks are tradable in the Open Market, some countries (e.g., Poland, Morocco or South Korea) are not represented at all. At the firm-level, the considerable difference in coverage rates across accounting standards and IFRS adopter types gives a first indication that the lead brokers do not randomly choose the securities they offer in the Open Market. From Panel C, we learn that the number of Open Market firms covered in the DS Universe increases over time, both in absolute as well as in relative terms. The total number of unique Open Market firms (Open Market share) climbs from 2,188 (16%) in 2002H1 to 4,936 (25%) in 2008H1. In total, the Open Market sample (DS Universe) comprises 5,637 (22,280) unique firms and 43,671 (217,772) firm periods. 14 Table 2 shows descriptive statistics of various firm characteristics for the Open Market sample (Panel A) as well as for the rest of the DS Universe (Panel B). Panel A presents trading volume, number of trades, trade size and bid-ask spread statistics from FSE and the respective home markets for the same set of firm-years. Panel B is naturally confined to data from the home markets, because the covered sample (DS Universe excluding the Open Market sample) is not traded at FSE. In addition to liquidity measures, both panels show statistics on variables that are independent of the trading venue (other variables). Liquidity in stocks from the Open Market sample is quite low at FSE. During the average firm period, trading occurs on slightly less than 25% of all trading days during 3,856 firm periods (about 9% of the Open Market sample) there is no trading at all. On average, each stock is traded little more than twice per day, average daily trading volume is about 18,000 Euro. In contrast, daily trading volume in the home markets averages nearly 30 Million Euro. These massive liquidity differences across exchanges are 14 Differences in the size of the Open Market sample and the official numbers from the FSE Factbooks stem from the data requirements we impose on the DS Universe. 12

15 hardly surprising. While trading at FSE is confined to a small subset of individual investors (i.e. German individual investors), institutional investors who account for most trading volume will prefer to trade in the respective home market. 15 Average trade size at FSE is about 2,700 Euro which is well below the threshold typically used in the prior literature to distinguish between individual and institutional investors (e.g., Bhattacharya, 2001; Malmendier and Shantikumar, 2007). Comparison of bid-ask spreads across exchanges indicates that the variable fee the lead broker charges on Open Market transactions is in fact substantial: the median bid-ask spread is 3.13% at FSE compared to only 0.28% in the respective home markets. Taken together, these descriptive statistics confirm that the Open Market at FSE is a trading segment that is specifically designed for individual investors CHARACTERISTICS OF THE OPEN MARKET In this section, we complement the descriptive analysis of the Open Market by providing evidence on (1) the characteristics of stocks tradable in the Open Market and (2) trading volume around earnings announcements Determinants of Open Market Inclusion Stocks are tradable in the Open Market if they have been included by the lead broker. This decision depends on a stock s potential to generate sufficient Open Market trading volume and thus brokerage fees. Potential trading volume in the Open Market is ultimately determined by individual investors demand for a particular stock. 15 Untabulated statistics show that trading volume at FSE aggregated over the whole Open Market sample varies between 10 and 20 billion Euros per year. Hence, despite its relative lack of liquidity the Open Market offers substantial income opportunities for its participants. For example, with an average brokerage fee of 0.08% of the order volume (see Appendix A) Open Market lead brokers earn a total of 8 to 16 million Euros per year for their services. 13

16 Table 4, Panel A, presents results from probit regressions relating the likelihood of inclusion of a stock in the Open Market to various firm- and country-specific variables. The analysis yields two key findings. First, when compared with stocks that are not tradable at FSE, firms in the Open Market feature higher market values (Market Value (Firm Level)), higher volatility (Return Variability), higher trading volume in the home market (Home Trading Volume) and greater media coverage in Germany (Google Ratio). 16 These results are robust to the inclusion of country fixed effects (regression models 3 and 4) and consistent with the observation that individual investors are net buyers of attention grabbing stocks (Barber and Odean, 2008). The second key finding is that transparent reporting practices are significant determinants for Open Market inclusion, both at the country and at the firm level (EM Measure). The only other significant determinant at the country level is a dummy variable that indicates countries from the Euro zone (Euro). In contrast, variables for capital market development (MCAP/GDP) and geographic proximity (Distance Berlin - Capital) do not load significantly. Finally, the results suggest that firms that start preparing their financial statements in English are more likely to be included in the Open Market (English Reporting in model 4). Taken together, our results provide strong evidence that the Open Market sample is a nonrepresentative subset of the DS Universe. Specifically, the Open Market sample is significantly tilted towards more visible and transparent companies. Thus, it seems that the lead broker acts as a gatekeeper to the Open Market, either proactively acting as a screening intermediary or explicitly responding to demand from individual investors. These findings have implications for the interpretation of our main empirical results (see section 4). 16 Note that most country- and firm-level variables are highly skewed. We transform highly skewed variables using natural logarithms to mitigate the influence of outliers. All variables are described in more detail in Table 2 and 3, respectively. 14

17 Earnings Announcement Reactions In this subsection, we analyze abnormal trading volume of Open Market stocks around annual earnings announcements. Table 4, Panel B, compares reactions at FSE with those in the respective home markets for the same set of earnings announcements. The analysis is based on a sample of 18,362 earnings announcement dates from IBES. Abnormal trading volume is the difference between trading volume on the event day and the mean daily volume for that stock over the pre-announcement window (-120, -21), scaled by the mean daily volume. The results show that abnormal trading volume at FSE increases significantly around earnings announcements. While the effect on mean abnormal trading volume is similar to that in the home markets, median abnormal trading volume remains unchanged at throughout the event window due to the low liquidity at FSE (see, e.g., the statistics on Trading Days (%) in Table 2, Panel A). These results provide direct evidence that the release of earnings information influences trading volume in the Open Market. 3. Empirical Analysis 3.1. GLOBAL IFRS ADOPTION AND OPEN MARKET TRADING VOLUME Research Design In this section, we test our main prediction that global IFRS adoption enhances cross-border equity investments by individual investors. Our proxy for these cross-border equity investments is trading volume in the Open Market denoted as OM Trading Volume. 17 The key independent 17 We use trading volume from both the floor and XETRA to calculate OM Trading Volume. OM Trading Volume is identical to Trading Volume (%) for FSE in Table 2, Panel A. The results are similar when we use trading volume from the floor only (see Appendix C). Since OM Trading Volume is highly skewed (see Table 2, Panel A), we use the natural logarithm to mitigate the influence of outliers. To ensure computing the natural logarithm for all firm periods, we replace raw values of zero by a small firm-specific constant. This constant is defined as one divided by the average number of floating stocks, divided by the number of exchange trading days during the firm period (i.e., we assume that exactly one stock was traded during the firm period). 15

18 variable is Post-IFRS, a dummy variable that takes on value one (zero) for fiscal-years 2005 and later (2004 and earlier). We define that Post-IFRS switches in the first firm period following the end of fiscal year 2005, i.e. if the fiscal year ends in December 2005 Post-IFRS switches in 2006H1. 18 To test the impact of global IFRS adoption, we interact Post-IFRS with binary variables that indicate mandatory IFRS adopters (Mandatory) and voluntary IFRS adopters (Voluntary), respectively. These interaction terms capture the average effect of global IFRS adoption on Open Market trading activity for the respective group of companies relative to the control group of non-ifrs adopters. Combining these variables results in the following basic regression specification: OM Trading Volume = β 0 + β 1 Post-IFRS + β 2 Post-IFRS*Mandatory + β 3 Post-IFRS*Voluntary + β 4 Voluntary*IFRS + Σ β j Controls j + ε [1] where Voluntary*IFRS is an interaction term that equals one (zero) after (before) voluntary IFRS adoption and Controls j denotes the set of control variables. Consistent with our main prediction, we expect the coefficient estimate on Post-IFRS*Mandatory to be positive, i.e. β 2 > 0. To the extent that the comparability benefits of global IFRS adoption spill over to voluntary adopters, we also expect a positive coefficient estimate on Post-IFRS*Voluntary (β 3 ). However, the latter effect is of second order and therefore likely to smaller, i.e. β 2 > β 3 > 0. Following related IFRS literature (e.g., Daske et al., 2008) we estimate regression specification [1] using a firm fixed effects model. The goal of this difference-in-differences 18 Many listed companies in Germany adopted IFRS well before global IFRS adoption took off, either voluntarily (e.g., Leuz and Verrecchia, 2000; Daske, 2006) or due to exchange regulation of the former New Market (e.g., Leuz, 2003). These early adoptions enabled German investors to familiarize themselves at an early stage with IFRS through their investments in domestic stocks. We therefore assume that Open Market investors react promptly to global IFRS adoption. We test the sensitivity of our results to this assumption in section

19 approach is to identify a causal relationship between a treatment (IFRS introduction) and an endogenous variable (Open Market trading activity) by comparing the treatment s impact on affected firms (treatment group) with its impact on unaffected firms (control group). To ensure that OLS estimation produces consistent standard errors we use standard errors clustered by country. Note that in our model the estimated effect of global IFRS adoption is exclusively determined by firms that are part of the Open Market sample both before and after IFRS introduction. Hence, differences in the composition of the Open Market sample pre- versus post- IFRS do not directly influence the results of the trading volume analysis. Since we estimate a firm fixed effects model, our controls are confined to variables that capture firm-specific changes over time. We include Market Value (meuro), Return Variability and Home Trading Volume (%) to control for changes in market values, stock return volatility and trading activity in the respective home markets, respectively. These control variables are designed to ensure that our results do not merely reflect second-order effects from global IFRS adoption such as decreases in the cost of equity capital or liquidity increases in the home markets (e.g., Daske et al., 2008; Li, 2010). Google Ratio controls for changes in media coverage in Germany and, thus, for news that may grab the attention of individual investors (Barber and Odean, 2008). English Reporting is included to control that the results are not influenced by firms that start preparing their financial statements in English. Finally, Spread Difference controls for changes in trading costs as reflected by the difference between the bid-ask spread at FSE and the respective home market. We expect the coefficient estimate(s) on Spread Difference (all other control variables) to be negative (positive) Again, we use the natural logarithm for those variables that have highly skewed raw values. For details on all control variables, see Table 2 to 4. 17

20 Empirical Findings Table 4 presents regression results of the trading volume analysis. Regression models 1 (without controls) and 2 (with controls) are based on the full sample and show that both mandatory and voluntary IFRS adopters experience a strong and statistically significant increase in Open Market trading volume following global IFRS adoption. For example, the coefficient estimate on Post-IFRS*Mandatory (Post-IFRS*Voluntary) in model 2 is (0.423) which corresponds to an increase in percentage trading volume of 90% (53%) relative to the control group. Untabulated analyses reveal that this coefficient estimate drops to (0.183) if U.S. firms, which constitute over 50% of the full sample, are excluded. This result demonstrates that U.S. firms substantially affect the main results. To avoid that our results are driven by sample firms from few countries such as the U.S., we define a subsample that allows a maximum of 100 firms per country. We create this subsample by (1) focusing on firms that are part of the Open Market sample both before and after IFRS introduction, 20 (2) sorting these firms within each country by their average market value over the sample period and (3) selecting every N/100 th firm if the number of firms per country N is greater than 100. Regression models 3 (without controls) and 4 to 6 (with controls) confirm that the strong IFRS effects for mandatory adopters hold when the subsample is used. Specifically, mandatory IFRS adopters experience an increase in Open Market trading volume of at least 56% (model 4) relative to the control group. The IFRS effect for voluntary adopters is weaker in the subsample regressions with t-statistics below 2. The coefficient estimates on Voluntary*IFRS are statistically significant throughout all regression specifications and suggest that following voluntary IFRS adoption Open Market 20 Note that firms that do not fulfill this requirement have no direct impact on the estimated IFRS effect due to the inclusion of firm fixed effects (see section ). 18

21 trading volume increases by at least 70% (model 6) relative to the rest of the respective sample. Note, however, that these estimates are determined by merely 27 (21) voluntary adopters in the full sample (subsample) that switched to IFRS in fiscal year 2002, 2003 or 2004, and were tradable in the Open Market both before and after adoption. For the remaining 105 (73) voluntary adopters in the full sample (subsample), the firm fixed effects capture potential effects of voluntary IFRS adoption on Open Market trading volume. In all regressions, the coefficient estimates on the control variables have the expected sign and are statistically significant, except for English Reporting. 21 Taken together, the regression results provide strong evidence consistent with our prediction that (1) global IFRS adoption enhances Open Market trading volume and (2) this effect is stronger for mandatory than for voluntary adopters ANALYSES ON THE IDENTIFICATION OF THE IFRS EFFECT Shifting the Switch of the Post-IFRS Dummy The regression results presented in the previous section are based on the assumption that Open Market trading volume reacts to financial statement information immediately after the respective fiscal year end. We therefore defined that the key independent variable Post-IFRS switches in the first firm period following the end of fiscal year 2005, i.e. if the fiscal year ends in December 2005 Post-IFRS switches in 2006H1. In this section, we gauge the impact of this research design choice by defining Post-IFRS to switch up to two periods earlier or later (see, e.g., Christensen et al., 2010, for a similar strategy). 21 Since our dataset does not comprise financial reports before fiscal year 2003, we only capture switches to English reporting between fiscal year 2004 and We document 47 (269) switches in fiscal year 2005 (in total). At the country level, most switches are carried out by firms from Hong Kong (71), France (43), Greece (27), Spain (25) and Sweden (23). Where comparable, our statistics are similar to those presented by Jeanjean et al. (2010). 19

22 Table 5, Panel A, reports the results which are based on re-estimations of model 2 and 4 in Table 4, respectively. While the coefficient estimate on Post-IFRS*Mandatory remains largely unchanged for the regressions based on the full sample, the subsample results show that both the coefficient estimate and the t-statistic peak when Post-IFRS is defined as in the previous section. The untabulated coefficient estimates on Post-IFRS*Voluntary are similar and (not) statistically significant for the full sample (subsample) throughout all regressions. The subsample results suggest that there is a structural break in Open Market trading volume around global IFRS adoption. More importantly, this structural break is confined to stocks of mandatory IFRS adopters. To the extent that the subsample is more powerful than the full sample in disentangling a potential IFRS effect, the findings of this section lend some support to our prediction of a causal relation between global IFRS adoption and Open Market trading volume Within-Country Analyses In this section, we exploit institutional peculiarities in two countries to gain further insights on the existence of an IFRS effect on Open Market trading volume. To this end, we perform withincountry analyses for the United Kingdom and Australia. For the analysis of firms from the United Kingdom, we use mandatory IFRS adopters listed in the Main Market at London Stock Exchange (LSE) as the treatment group and companies listed in the Alternative Investment Market (AIM) at LSE as the control group. AIM companies were not required to adopt IFRS before fiscal year 2007 and therefore not included in the Open Market sample presented in Table 1. We delete firm periods of AIM companies after these adopted IFRS. This procedure yields a sample of 2,104 (437) firm periods from 302 (93) mandatory IFRS adopters from the Main Market (AIM companies). Based on our main prediction, we expect that following global IFRS adoption firms from the Main Market experience an increase in Open 20

23 Market trading volume relative to AIM companies. Regression models 1 (without controls) and 2 (with controls) in Table 5, Panel B, provide evidence that is consistent with this expectation. For example, the coefficient estimate on Post-IFRS*Mandatory in model 2 is (t-statistic 1.96) which corresponds to a relative increase in percentage trading volume of more than 100%. Thus, the analysis provides evidence in support of our main prediction in a within-country context. The within-australia analysis makes use of the fact that many Australian companies end their fiscal years in June. The analysis focuses on mandatory IFRS adopters and distinguishes between early (fiscal year ends in December 2005) and late adoption (fiscal year ends in June 2006). The Australian sample comprises 311 (1,395) firm periods from 49 (359) early (late) adopters. In contrast to previous specifications, we define Post-IFRS to equal one (zero) for periods 2006H1 and later (2005H2 and earlier) for all firms, i.e. the definition is independent of the firm-specific fiscal year end. In this specification, we expect that early adopters experience an increase in Open Market trading volume relative to late adopters, because Post-IFRS switches before the late adopters publish their first financial statements under IFRS. Consistent with this expectation, regression models 3 (without controls) and 4 (with controls) in Table 5, Panel B, show that the coefficient estimate on Post-IFRS*Late is negative. However, the estimate is not statistically significant CROSS-SECTIONAL VARIATION IN THE IFRS EFFECT In this section, we examine cross-sectional variation in our estimated IFRS effect on Open Market trading volume. Similar to prior literature (e.g., Daske et al., 2008), we interact the key independent variables in regression specification [1] with a binary variable Conditional that partitions the treatment group. This approach translates into the following basic regression specification: 21

24 OM Trading Volume = β 0 + β 1 Post-IFRS + β 2 Post-IFRS*Mandatory + β 3 Post-IFRS*Mandatory*Conditional + β 4 Post-IFRS*Voluntary + β 5 Post-IFRS*Voluntary*Conditional + β 6 Voluntary*IFRS + Σ β j Controls j + ε [2] where all variables are defined as in regression specification [1]. The partitioning variable Conditional explains systematic cross-sectional variation in the estimated IFRS effect for mandatory (voluntary) adopters if the interaction term Post-IFRS*Mandatory*Conditional (Post- IFRS*Mandatory*Conditional) is statistically significant. The following two subsections discuss results from treatment group partitions at the country and industry level (section ) as well as at the firm level (section ). The results are presented in Table 6. All reported regressions are based on the subsample used in the previous sections and include the same control variables as regression model 4 in Table 4. We refer to untabulated regressions based on the full sample only if the results provide new insights Treatment Group Partitions at Country and Industry Level We use four different variables to partition the treatment group at the country or industry level. First, Euro distinguishes between countries that have adopted the Euro as their national currency (variable equals one) and countries that have not (variable equals zero). The regression result from this partition show that the estimated IFRS effect is not systematically different for firms from the Euro zone compared to the rest of the treatment group. We can therefore rule out the alternative explanation that our results reflect a Euro rather than an IFRS effect. The second partitioning variable, EM Measure (Country Level), is a binary variable that equals one (zero) if the country-specific earnings management score described in Table 3 is above (below or equal 22

25 to) the median score within the treatment group. 22 The regression does not yield significant coefficient estimates on the partitioning interaction term. This result suggests that country-level differences in earnings management practices do not explain cross-sectional variation in the estimated IFRS effect on Open Market trading volume. The third partitioning variable, Compliance CESR Std. 1, equals one (zero) for European countries that had (not) fully complied with CESR Standard No. 1 by August This Standard seeks to harmonize the institutional oversight systems in Europe by providing principles with regard to the enforcement standards on financial information. The complying countries are Belgium, Denmark, France, Greece, Italy, Norway, Portugal and the United Kingdom (CESR, 2006). The variable is missing for IFRS adopters from non-european countries. In this regression, the coefficient estimate on Post- IFRS*Mandatory*Conditional is positive (0.165) but not significant at conventional levels (tstatistic 1.21). This result provides evidence that the strength of the enforcement system at the country level is not associated with our estimated IFRS effect. 23 Finally, % Voluntary IFRS per Industry equals one (zero) if the industry-specific percentage of voluntary IFRS adopters among German listed companies in fiscal year 2002 is above (below or equal to) the median percentage in the treatment group. This partition aims to identify industries where German investors are more familiar with IFRS through their investments in domestic stocks. Again, the partitioning interaction terms are not statistically significant. Taken together, the results presented in this subsection suggest that country and industry level variables are too crude to explain cross-sectional variation in our estimated IFRS effect on Open Market trading volume The country-specific earnings management score is updated and slightly modified version of the measure introduced by Leuz et al. (2003). The correlation between their measure and our updated score is Alternative proxies for the strength of the enforcement systems such as MCAP/GDP (see Table 3) or the Worldwide Governance Indicators from Kaufmann et al. (2009) confirm this finding. 23

26 Treatment Group Partitions at Firm Level We use four different variables to partition the treatment group at the firm level. The first variable is Large IFRS Restatements and equals one (zero) if the percentage difference between the restated net income under IFRS and the originally reported net income under local GAAP for fiscal year 2004 is above (below or equal to) the median percentage difference. The regression shows that mandatory adopters that report a higher net income under IFRS than under local GAAP experience a stronger increase in Open Market trading volume following global IFRS adoption. This effect is statistically significant at the 10% level. The second variable, Strong EA Reactions, equals one if the average Open Market trading volume during the three-day window around the earnings announcement is higher than the average Open Market trading volume over the relevant firm period, and zero otherwise. The coefficient on IFRS*Mandatory*Conditional is positive (0.225) and statistically significant (t-statistic 2.21) suggesting that the IFRS effect is stronger during firm periods where Open Market trading volume is clustered around earnings announcements. The third variable is Low FSE Liquidity before IFRS and equals one (zero) if the average Open Market trading volume before fiscal year 2005 is below (above or equal to) the sample median. The sample median is calculated separately for voluntary and mandatory IFRS adopters. The regression provides strong evidence that our estimated IFRS effect is confined to mandatory adopters with low pre-ifrs Open Market trading volume. 24 The coefficient estimate on IFRS*Mandatory*Conditional of suggests that Open Market trading volume more than doubled for this group relative to the rest of the sample. Finally, High Stock Return after IFRS equals one (zero) if the stock return over three firm periods 2007H1-2008H1 is higher than (lower than or equal to) the sample median. The sample median is calculated separately for 24 The full sample regression yields similar results. Specifically, the coefficient estimate on Post-IFRS*Mandatory (Post-IFRS*Mandatory*Conditional) is (0.851) with a t-statistics of 1.79 (5.34). 24

27 voluntary and mandatory IFRS adopters. The firm periods used for return calculation are not considered in the analysis, i.e. the regression is based on the periods 2002H1-2006H2. The goal of this partition is analyze whether the IFRS effect is particularly pronounced for stocks that experienced abnormal returns subsequently. The regression does not provide evidence of such abnormal returns. 4. Conclusions This study examines the impact of global IFRS adoption on trading volume in the Open Market. The Open Market is a segment at FSE designed for German individual investors to trade foreign (i.e. non-german) stocks. We find strong evidence that stocks experience an increase in Open Market trading volume following mandatory adoption of IFRS. This finding is consistent with the idea that collective IFRS adoption reinforces cross-border equity investments by individual investors. Taken at face value, our results support the efforts by the IASB and standard setters around the world to foster a single global set of financial reporting standards. However, we urge caution in interpreting the results in this study. First, our dataset does not allow us to directly observe if and how global IFRS adoption influences individual investors decision making and trading in the Open Market. Despite our extensive efforts to control for other determinants of Open Market trading volume, we can therefore not fully rule out the possibility that our results reflect concurrent institutional changes that are not related to global IFRS adoption. Second, our analyses and, thus, the estimated IFRS effects are based on the Open Market sample. The Open Market sample is a large but selected subset of the universe of global stocks comprising companies that are significantly more visible and transparent. It is questionable whether our result also apply to the less visible and transparent rest of the universe of global stocks. Third, we 25

28 recognize that individual Open Market investors taking active positions in individual foreign stocks are not necessarily representative of the universe of individual investors in the global economy. However, the IASB s and other standard setters efforts are naturally targeted towards investors who use financial statement information in their investment decisions. Individual investors who engage in cross-border investments are likely to be an important subset of this group. 26

29 APPENDIX A Comparison of Transaction Costs: Open Market versus Home Markets German investors have two options when trading foreign stocks that are included in the Open Market. They can either trade at FSE or they can trade abroad, i.e. in the respective home market. Both options involve transaction costs that differ considerably in nature. This appendix seeks to illustrate these differences by means of an example. Table A1 shows concurrent quotes for Fiat stocks (ISIN IT ) at the home market in Milan and at the Open Market in Frankfurt. We collect this information through the webportal of comdirect bank, the leading online broker for German individual investors. Table A2 presents order fees at comdirect bank for trading stocks at Milan Stock Exchange (MSE) and FSE, respectively. While MSE offers better prices (i.e. the MSE bid-ask spread is inside the FSE quotes), comdirect bank clients incur lower order fees (both fixed and variable) for trading at FSE. Lower order fees at FSE outweigh the price advantage at MSE if and only if the size of the trade is sufficiently small. 25 For example, a buy of 100 Fiat stocks would cost Euro at MSE, but only Euro at FSE. 26 In contrast, buying 1,000 Fiat stocks is cheaper at MSE (7, Euro versus 7, Euro at FSE). 27 The break-even where MSE turns into the more cost-efficient trading alternative is about 500 units or 3,800 Euro in this particular case. This is also reflected in the different quote sizes at MSE (around 10,000 stocks) and FSE (200 stocks). Taken together, this example confirms our depiction of the Open Market as a platform for German individual investors to trade small sizes of foreign stocks The difference in transaction costs between FSE and MSE is a monotonic function, because the price advantage of MSE (about 0.5%) is larger than the advantage of FSE in variable order fees (about 0.12%). The advantage of FSE in fixed order fees therefore has less impact on the total transaction costs the higher the trade size. The order value (order processing costs) is (are) (29.06) Euro at MSE and (12.01) Euro at FSE. The order value (order processing costs) is (are) 7, (49.42) Euro at MSE and 7, (17.48) Euro at FSE. 27

30 Table A1: Concurrent Quotes for Fiat Stocks (ISIN IT ) This table presents concurrent quotes for Fiat stocks (ISIN IT ) at the home market in Milan (German: Mailand) and at the Open Market in Frankfurt. The information was retrieved from the website of comdirect bank ( on 22 April The upper part of the table contains information on the relevant exchange (Börse), the last price (Aktuell), the time the last price was set (Zeit), the percentage difference between the last price and the price of the previous day (Diff. Vortrag) as well as the trading volume in Euro (Tages-Vol.) and in units (Gehandelte Stück). The lower part of the table provides details on the current bid (Geld) and ask quote (Brief), the time these quotes were set (Zeit), the percentage spread (Spread) as well as the size of the current bid (Geld Stk.) and ask quote (Brief Stk.). Table A2: Order Fees at comdirect bank Type of Fee Milan Stock Exchange (MSE) Frankfurt Stock Exchange (FSE) Order Provision 7.90 Euro % of order value (min Euro, max Euro) 4.90 Euro % of order value (min Euro, max Euro) Brokerage Fee % of order value Exchange Fee 0.20% of order value (min Euro) % of order value (min Euro) Delivery Fee 7.50 Euro - This table presents order fees at comdirect bank for trading stocks at Milan Stock Exchange (MSE) and Frankfurt Stock Exchange (FSE), respectively. The information was provided by comdirect bank customer support. Order Provision is the fee comdirect bank charges for its services. All other fees are charges by third parties that comdirect bank passes on. Brokerage Fee (Exchange Fee) is a charge for the FSE lead broker (respective exchange). Delivery Fee is a charge for stock clearing. 28