Eloonext - Analysis of Home Bias

Transcription

1 The Ability of Global Stock Exchange Mechanisms to Mitigate Home Bias: Evidence from Euronext by Grace Pownall Goizueta Business School, Emory University Maria Vulcheva Florida International University Xue Wang Goizueta Business School, Emory University February 2012 We appreciate research funding from the Goizueta Business School at Emory University and Florida International University. We are grateful for comments from Pedro Matos, Darren Roulstone, James Sinclair, Isabel Wang, Emanuel Zur, and workshop participants at Emory, Florida State, Michigan State, and Ohio State Universities, the University of Virginia, Chinese University of Hong Kong, the 2012 Hong Kong University of Science and Technology Accounting Research Symposium, and the 2012 AAA Financial Accounting and Reporting Midyear Meeting.

2 Abstract This paper examines the effects on equity Home Bias of two mechanisms adopted by Euronext when it was formed by the merger of four European countries' stock exchanges in Home Bias is the well-documented tendency of investors everywhere to over-invest in domestic securities relative to optimal portfolio diversification. These two mechanisms are the integration of trading platforms across the four national predecessor exchanges, and the creation of named segments of the new exchange on which firms could voluntarily list by pre-committing to enhanced disclosure and transparency. We test for changes in Home Bias associated with each mechanism by examining changes in domestic and foreign ownership between Euronext firms that joined the named segments and those that did not, controlling for contemporaneous political and financial developments in the Europe Union by including other EU firms as a benchmark. Overall, we find no diminution of Home Bias for the non-segment Euronext firms, but significant increases in all categories of foreign holdings relative to domestic holdings for the segment Euronext firms. Our results indicate that the segmentation mechanism of voluntary precommitment to enhanced financial reporting and corporate governance has the potential to reduce Home Bias for firms listed on the integrated global capital markets. 1

3 The Ability of Global Stock Exchange Mechanisms to Mitigate Home Bias: Evidence from Euronext 1. Introduction Global financial markets are consolidating across national borders, with mergers involving U.S. and European exchanges such as the merger between NYSE and Euronext, the London Stock Exchange and the Milan Stock Exchange, NASDAQ and Sweden's OMX, and the ongoing merger negotiation between NYSE Euronext and Deutsche Börse. One of the goals of global stock exchange mergers is to create a consolidated trading platform that makes listed firms more available to an expanded investor clientele, thus providing larger pools of liquidity. 1 However, there is no direct empirical evidence about the effects of the cross-border integration of capital markets on foreign investment decisions. In this paper, we study the ability of global stock exchange mechanisms to reduce Home Bias. Home Bias refers to the phenomenon that investors everywhere tend to over-invest in domestic securities and under-invest in foreign securities relative to optimal global portfolio diversification, and has been observed in many contexts and time periods (French and Poterba 1991; Kang and Stulz 1997; Ahearne et al. 2004; Bradshaw, Bushee, and Miller 2004; Covrig, DeFond, and Hung 2007; Yu 2009; Leuz, Lins, and Warnock 2009). Firms benefit from reducing Home Bias and having greater access to foreign investors. For example, prior research suggests that firms with greater access to foreign capital face a lower cost of capital because of improved 1 See Federal Reserve Bank of New York (2002) for discussion of the goals and benefits of stock exchange consolidation in Europe, particularly liquidity and reduced fragmentation. Cross-border stock exchange mergers may or may not result in an integrated trading platform. It is the integration of trading platforms that makes securities more available to investors from the domiciles of all of the merged exchanges. The integration of trading platforms is desirable to maximize liquidity, and was achieved shortly after the formation of Euronext but has not been achieved for some cross-border mergers, such as that between the NYSE and Euronext. See Nielsson (2007) for a discussion of the limited interdependence of cross-border stock exchanges after demutualization and merger. One disadvantage of more integration following cross-border stock exchange mergers would be a few monolithic stock exchanges' entrenchment in monopoly positions. 2

4 investor diversification (Foerster and Karolyi, 1999; Doidge, Karolyi, and Stulz 2004; Chan, Covrig, and Ng 2006). Given that the integration of global capital markets has the potential to reduce cross-border investment barriers, it is important to understand the ability of global stock exchange mechanisms to reduce Home Bias. The empirical setting in which we investigate these issues is the formation in 2000 and 2002 of the Euronext stock market from the merger of the Amsterdam, Brussels, Paris, and Lisbon stock exchanges. The merger of the four stock exchanges and the subsequent integration of the trading platform across the four markets made firms from each country readily available to investors from all four countries through a single marketplace. Euronext also made a first step toward the integration of de jure reporting, disclosure, and trading regulation by issuing one Rulebook covering many aspects of reporting and trading across the four exchanges and separately codifying other reporting and trading regulations that were specific to each exchange in four secondary Rulebooks. However, the common and jurisdiction-specific regulations were enforced by each country individually, creating potential unevenness in the de facto regulation. Therefore, to achieve even more consistent regulation of at least some firms from the markets in each country, Euronext established two voluntary named segments of the integrated stock market on which firms could choose to list by pre-committing themselves to enhanced financial reporting quality and corporate governance. This mechanism of voluntary pre-commitment to transparency, if credible to investors, is a solution to the problem of regulating integrated global capital markets (Grundfest 1990; Leuz 2010; Federal Reserve Bank of New York 2002). The two named segments were NextEconomy (for firms from high-tech industries) and NextPrime (for firms from more traditional sectors). 3

5 While there are many explanations of Home Bias, two of the most likely are the costs of accessing trading opportunities and unfamiliarity with the mechanics of transacting in foreign firms (transaction costs hypothesis); and lack of familiarity with and the costs of accessing and interpreting information about foreign firms (information costs hypothesis). Based on these two explanations, we expect that the two mechanisms adopted by Euronext, the integration of the trading platforms and the creation of named segments, reduced Home Bias. Although the two hypotheses are not mutually exclusive or collectively exhaustive, the Euronext empirical setting also allows us to test the relative strength of the transaction costs hypothesis and the information costs hypothesis in explaining the Home Bias puzzle. The transaction costs hypothesis attributes Home Bias to the existence of market frictions, which make it costly for investors to buy and sell foreign stocks. Transaction costs in general include trading commissions and implicit impact costs due to liquidity. The integration of trading platforms as a result of the Euronext merger is likely to greatly reduce transaction costs of trading stocks. As mentioned in Euronext s annual report of 2002, the integration of trading platforms provided market participants with the benefit of a market that is much wider, more liquid, and more efficient in terms of transaction costs. In particular, Euronext suggested that the significant investment they made in the IT system to make possible the market integration enabled Euronext to lower its trading costs considerably, and part of the savings would likely be passed on to the investors. As documented in Pagano and Padilla (2005), the integration of the Euronext trading platform was associated with a 15% reduction in transaction costs in Paris, and 31% in Amsterdam. Additionally, according to a European Commission document quoted in The Economist (2006), cross-border trading commissions were very high: buying and selling shares in another European Union country could cost up to six times more 4

6 than dealing at home. Therefore, Euronext s integration of trading platforms had the potential to reduce transaction costs particularly for foreign investors among the four countries participating in the stock exchange merger. If transaction costs explain Home Bias, we predict that the reduction in transaction costs through the integrated trading platform will be associated with reduced Home Bias for firms traded on Euronext after the merger. The attenuation of Home Bias should be particularly reflected in increased foreign relative to domestic ownership for companies traded on Euronext by investors from the other countries with stock exchanges involved in the merger (the Netherlands, Belgium, Portugal, and France). Alternatively, if information costs explain Home Bias, we should observe that the reduction in Home Bias for firms traded on Euronext after the merger is a function of the information quality of these firms. Pownall, Vulcheva, and Wang (2011) present evidence that firms choosing to be listed on the named segments increased their disclosure and to a certain extent their financial reporting quality relative to their practices prior to joining the named segments and relative to firms that did not choose to join the segments. Segment firms were more likely than non-segment firms to have fully functioning websites, to use IFRS and global auditors, to use English in their primary or secondary financial statements, and to report quarterly. Pownall et al. (2011) also find that segment firms had increased trading liquidity relative to their experience prior to joining the segments and also relative to non-segment Euronext-listed firms. These empirical regularities suggest reduced costs of accessing, processing, and interpreting information about the segment firms' financial position and performance. Therefore, if information costs are a credible explanation for Home Bias, we will observe reduced Home Bias for the segment firms, driven by increases in foreign ownership by 5

7 investors from all over the world as opposed to only investors from the four countries with national exchanges involved in the Euronext merger. Using a database that reports firm-level holdings of mutual funds from around the world, we conduct empirical tests on a sample of 21,760 firm-year observations (2,310 unique firms) from 16 European Union (EU) countries during the period We compare changes in foreign and domestic mutual fund ownership of Euronext-traded firms with concurrent changes in foreign and domestic ownership of other EU companies to isolate the effects of the integration of the Euronext market from the effects of other developments in the EU financial and political regime taking place concurrently. For these comparisons we use a sample of publicly-traded EU firms with data available from Worldscope and Datastream as a control group. Briefly, we find that the integration of the Euronext market is associated with a reduction in Home Bias for firms listed on the named segments of the Euronext exchange, but not for the non-segment Euronext firms. We interpret this as suggesting that the reduction in trading costs from the integration of trading platforms did not make the non-segment Euronext firms more attractive to the specific investors for whom the transaction costs were reduced. While there is no diminution of Home Bias measured as the holding of other Euronext countries relative to domestic holdings for non-segment firms, we document significant increases in all categories of foreign holdings relative to domestic holdings for Euronext segment firms. We interpret this as suggesting that the decrease in information costs due to the pre-commitment to enhanced transparency made the segment firms more attractive to all categories of foreign investors, consistent with the information costs hypothesis. Our empirical results on segment firms are robust after using propensity score matching to select a sample of non-euronext firms and a 6

8 sample of non-segment Euronext firms using firm characteristics that the literature suggests are associated with attractiveness to foreign investors. Our paper makes the following contributions. First, to the best of our knowledge, our study is the first to examine the real investment consequences of the integration of global capital markets. These results are important in light of the ongoing consolidation of global stock exchanges. Integrating the trading platforms of stock exchanges from different countries has the potential to create ever larger pools of liquidity, especially in the presence of a mechanism such as the Euronext named segments that allows firms to commit to more transparency and higher reporting standards than those effectively imposed by their home countries. The enhanced disclosure and corporate governance supported by the segmentation mechanism also has the potential to reduce Home Bias and allow investors to garner more of the benefits of international diversification. Second, our paper sheds more light on the Home Bias puzzle by employing the unique empirical setting of the integration of the Euronext market, which allows us to isolate the transaction costs hypothesis from the information costs hypothesis. In addition, our differencein-differences design allows us to control for behavioral biases posited in prior literature as an alternative explanation for Home Bias (see Beneish and Yohn 2008). Collectively, our empirical evidence suggests that the reduction in Home Bias for Euronext listed firms after the integration is more consistent with the information costs hypothesis. Third, our research provides evidence that the benefits of "bonding" by voluntarily submitting to a more rigorous disclosure and monitoring regime extend to contexts other than cross-listing in the U.S. The bonding hypothesis has been tested extensively for cases in which non-u.s. firms become listed in the U.S. to subject themselves to U.S. securities regulation and 7

9 the SEC to protect domestic minority shareholders against the possibility of being expropriated by powerful insiders. We show that Euronext firms that voluntarily subjected themselves to the enhanced transparency, disclosure, and monitoring became more attractive investments to foreign mutual funds. Showing that firms can "bond" to enhanced disclosure, transparency, and monitoring without incurring the substantial costs of a U.S. listing establishes the generalizability of the bonding hypothesis. Participants in Euronext (securities and exchange officials, firms, and investors) have different incentives and characteristics than U.S. capital market participants and non-u.s. cross-listed firms. Therefore, bonding is not a U.S.-specific concept but extends to other securities markets and firms. Finally, we offer the following interpretation of our results in the context of U.S. policy choices. The NYSE-Euronext merger resulted in the creation of a common holding company, which oversees two exchanges that continue to be largely local. The main concern that led to this lower level of integration was that regulation of any one of the two jurisdictions could supersede that of the other. 2 Our paper suggests that such regulatory concern is unjustified. Despite the high level of integration achieved by the four Euronext exchanges, neither enforcement nor regulation seems to have changed substantially at the jurisdiction level, as demonstrated by the lack of reductions in Home Bias for non-segment Euronext firms. At the same time, the segmentation of the Euronext exchange and the pre-commitment of segment firms to a number of disclosure and reporting requirements resulted in more foreign mutual fund investment for segment firms. Thus, neither the four Euronext exchanges nor the U.S. need worry about unwanted changes in their regulatory environment, because the main tools that can bring about these changes are at the exchange level rather than at the jurisdiction level. 2 SEC Press Release SEC Chairman, Euronext Regulators Meet to Discuss Cooperation in the Event of the NYSE Euronext Combination. 8

10 The rest of this paper is organized as follows. Section 2 describes the merger of the four predecessor exchanges into Euronext and the formation of the two named segments, reviews the literature on the equity Home Bias puzzle, and develops the hypotheses. Section 3 describes the data, empirical design, and results. Section 4 presents diagnostics and extensions, and section 5 concludes with a summary and discussion of implications. 2. Euronext Background, Literature Review, and Hypothesis Development 2.1. Euronext Background 3 The Euronext equity market, Europe's second largest, was formed between 2000 and 2002 by the merger, demutualization, and initial public offering of the exchanges in Amsterdam, Brussels, Paris, and Lisbon. All Euronext equities listed in Amsterdam, Brussels, Paris, and Lisbon are traded through Nouveau System Cotation (NSC) and cleared by Clearnet, which allows all Euronext members, regardless of their location, to access all securities listed on Euronext. The centralized, order-driven trading system, along with the central clearing system, settles transactions on a net basis to guarantee performance (Poser 2001). Euronext and regulators in the individual jurisdictions have harmonized their rules and regulations over the years to produce two Euronext Rulebooks: Rulebook I contains harmonized rules that are contractual agreements among the market participants of Euronext, and the four versions of Rulebook II contain the remaining rules of the individual markets that have not been harmonized. The coverage of Rulebook I has gradually increased from harmonized membership, trading, and enforcement rules in the early periods to a common set of listing qualifications and 3 Much of the descriptive material in Sections 2.1 and 2.2 was gathered from the Euronext NV Annual Reports of 2001 through For additional description of the Euronext market and its two segments, see Euronext Product Information on the NYSE Euronext website at selected Mep=2& idinstrument=15427&isin Code=NL See also Pownall et al. (2011). 9

11 disclosure requirements governing listed companies in later periods. Today, a Euronext-listed company complies with the unified listing requirements as specified in Rulebook I, and after admission, with the ongoing financial reporting requirements of its home member state The NextEconomy and NextPrime Segments Euronext created two market segments NextEconomy and NextPrime for two reasons: (1) to meet the needs of investors seeking greater transparency and liquidity; and (2) to allow companies to increase their visibility and appeal by pre-committing to enhanced governance, disclosure, and financial reporting quality. NextEconomy and NextPrime did not replace any existing regulated market but were in addition to being listed on one of the four national Euronext exchanges. Euronext-listed companies joined the segments voluntarily by signing Commitment Agreements to comply with a series of enhanced and standardized financial reporting, investor relations, and corporate governance practices. 4 Once firms signed the Commitment Agreements, Euronext featured the segment firms on a specific section of the exchange's website including detailed corporate and financial information; initiated advertising campaigns and roadshows; and included coverage of the segments firms in investor guidebooks. At 12/31/01, NextPrime listed 107 companies and NextEconomy listed 93 companies, with market capitalization of billion (Euronext NV Annual Report 2001). Over the years the segments existed (2002 through 2007), Euronext dropped firms from the segments for cause, and added more firms at their petition. 5 Given Euronext's goal of capturing liquidity in EU firms' shares, it is not surprising that few firms were actually dropped for cause. 4 The Commitment Agreements' main requirements were to: (1) publish quarterly financial reports beginning in 2004; (2) adopt international accounting standards beginning in 2004; (3) publish financial documents in English beginning in 2002; (4) schedule meetings for analysts; (5) describe corporate governance policy in the annual report; (6) schedule regular publications and meetings; and (7) publish key financial data on their websites. 5 See archived weekly notices of removals and additions on the NYSE Euronext website. 10

12 Pownall et al. (2011) compare segment firms' reporting and disclosure attributes with those of the non-segment Euronext-listed firms. They find that although compliance with the segment commitments was less than complete, on average segment firms had materially higher incidence of global auditors, English language reporting, IFRS adoption, fully functioning websites, and quarterly reporting than did the non-segment firms. On October 23, 2007, Euronext announced the discontinuation of the NextEconomy and NextPrime segments. 6 The reason given for the elimination of the segments was that the EU Transparency Directive's requirements for enhanced transparency and disclosure for all publiclytraded firms in the EU duplicated most of the requirements of the Commitment Agreements. 7 The Transparency Directive requires more transparency from all EU public firms and eliminates firms ability to distinguish themselves by pre-committing to the higher standards. Therefore, we expect and find that non-segment firms' transparency and disclosure were increased by the Transparency Directive to approach that of the segment firms, and that the segment firms' continued their pre-transparency Directive level of transparency and disclosure (Pownall et al. 2011) Home Bias Literature Review and Hypothesis Development According to the precepts of finance theory, the optimal proportion of domestic assets in an average investor s portfolio should be equal to the share of the domestic market in global market capitalization. For example, in 2009 a US investor should have invested 31.6% of her money in US stocks, because the US equity market during that year represented 31.6% of the 6 See Suppression des segments Nextprime et Nexteconomy" published by NYSE Euronext on 10/23/ See Christensen, Hail, and Leuz (2010) for a description of the Transparency Directive and the cross-country variation in its effects. 11

13 world equity market capitalization. 8 In reality, domestic assets make up a disproportionately large share of the average investor s asset holdings: records from Bank of New York Mellon show that US equities accounted for 81.1% of US investors equity portfolio in This lack of international portfolio diversification is commonly referred to as equity Home Bias, and despite extensive research over the past few decades, it continues to puzzle economists and finance researchers (Amadi 2004). One of the main and earliest hypotheses attempting to explain Home Bias is the transaction costs hypothesis. This hypothesis attributes Home Bias to the existence of market frictions, which make it costly for investors to acquire foreign assets. Several papers document empirical results in support of this hypothesis. Martin and Rey (2004) develop a model in which the demand for foreign assets changes with transaction costs in a non-linear fashion, and conclude that it is possible for a small increase in transaction costs to cause a larger increase in Home Bias. Studying implicit and explicit trading costs, Domowitz et al. (2000) find that in the period both types of costs are substantial, especially for emerging markets. Therefore, these costs have the potential to affect the returns of a globally diversified portfolio. Fidora et al. (2006) document that real exchange rate volatility is an important determinant of bond and equity Home Bias for 40 investor and 120 destination developed and developing markets they show that reducing volatility from its sample mean to zero would result in a decrease in bond Home Bias of up to 60 percentage points and equity Home Bias of up to 20 percentage points. In addition, Balli et al. (2010) find that following the introduction of the Euro 8 World Federation of Exchanges We focus on equity home bias although equity investments are only part of a typical investor's portfolio. Other investment types like debt instruments, real estate, human capital, etc. may be distributed differently than equity investments. Likewise, U.S. investors may take equity stakes in multinational corporations and therefore indirectly in those multinationals' foreign subsidiaries. However, the gap between predicted and actual investments in U.S. equity security portfolios is too large to be explained by varying investments across asset classes or indirect investments through multinational companies' equities. 12

14 in 1999, investors in the Eurozone replaced Home Bias with Eurobias or overweighting of Eurodenominated assets partly due to the elimination of exchange rate risk. Even within the Eurozone itself, investors tend to invest in markets with lower transaction costs. On the other hand, a number of papers raise issues with regard to the validity of transaction costs in explaining Home Bias. Lewis (1999) suggests that an implicit assumption of this hypothesis is that investment costs are larger than the possible diversification benefits, and estimates that in the case of international diversification, investors stand to gain between 20 and 100 percent of their life time consumption depending on their level of risk aversion. Consequently, the costs needed to divert investors from international diversification seem prohibitively high. Ahearne et al. (2004) also suggest that even when trading costs are a determinant of Home Bias, they are only of secondary importance. Further, some authors suggest that some of the market frictions cited by the transaction costs hypothesis have been reduced or have disappeared over time (French and Poterba 1991; Cooper and Kaplanis 1994). For instance, the importance of such barriers to foreign investment as restriction of foreign exchange transactions, withholding taxes, and controls on capital repatriation, has been reduced by international tax treaties, the removal of foreign exchange controls, and the reduction of capital controls in many countries. Given the mixed evidence in the literature about the extent to which the transaction costs hypothesis can explain Home Bias, we conduct empirical analyses in the context of the Euronext merger. We hypothesize that if the transaction costs hypothesis explains Home Bias prior to the Euronext merger, there will be a reduction in Home Bias following the creation of an integrated trading platform. We also hypothesize that such attenuation of Home Bias will be particularly reflected in increased foreign ownership for companies traded on Euronext by investors from the 13

15 other countries with stock exchanges involved in the merger. Therefore, we formulate the following hypotheses: H 01 : Foreign ownership did not increase relative to domestic ownership for Euronext-listed companies at the time of the merger that created Euronext, compared to other companies in the EU. H A1: Foreign ownership increased relative to domestic ownership for Euronext-listed companies at the time of the merger that created Euronext, compared to other companies in the EU, and this increase was more pronounced for foreign investors from the other countries with stock exchanges involved in the merger relative to other foreign investors. The information costs hypothesis provides a main alternative explanation of Home Bias. According to this hypothesis, investors refrain from investing in foreign stocks because they are at an information disadvantage compared to domestic investors. Coval and Moskowitz (1999) show that even within a country, investors prefer geographically proximate firms because they can more easily communicate with their employees, managers, and suppliers, and may obtain information on these firms from local media. Similarly, Kang and Stulz (1997) find that foreign investors in Japan tend to opt for larger companies, with which they are more familiar, even at the cost of 0.60% increase in the volatility of their monthly returns compared to their holding the market portfolio. 9 While information accessibility is a problem in the international environment, additional issues arise for foreign investors from the need to understand and analyze such factors as 9 In addition, Nieuwerburgh and Veldkamp (2009) suggest that small initial information asymmetry is sufficient for investors to prefer acquiring information on local firms and therefore, a larger share of local assets. This, in turn, makes it more cost effective to acquire further information on local assets and less beneficial to acquire information on foreign assets. The result is a further exacerbation of information asymmetry, which the authors term information immobility. 14

16 differences in accounting standards, disclosure requirements, and regulatory environments across countries. For example, Ahearne et al. (2004) find that countries in which a larger percentage of firms cross-listed on US stock exchanges and complied with the stricter disclosure and reporting requirements are more heavily weighted in US investors portfolios than countries in which fewer firms did so. Bradshaw et al. (2004) find that U.S. institutional investors invest more and therefore are less biased against firms whose accounting choices are closer to US GAAP, and that the increase in investment follows the increase in conformity. There is also a growing research stream on foreign investment decisions following voluntary and mandatory adoptions of IFRS. Covrig et al. (2007) examine mutual fund ownership for 25,000 mutual funds in 29 countries, and find that their investment in voluntary IFRS adopters is 45% higher than that in non-adopting firms and that it increases by 35% in the year following IFRS adoptions. The authors attribute this attenuation of Home Bias to more disclosure by adopting firms. In addition, several papers document that cross-border investment increased significantly following mandatory IFRS adoptions in the EU (see Yu 2009 and DeFond et al for mutual fund investment, and Florou and Pope 2009 for institutional investment). DeFond et al. (2011) attribute the findings to increased financial statement comparability as a result of using a uniform set of accounting standards, while Yu (2009) focuses on the effect of accounting distance (i.e. the difference between the local standards of the investor and the local standards of the investee), and suggests that accounting distance mitigates information asymmetry and explains the increase in foreign investment. Despite the overall evidence in support of the information costs hypothesis, the extent to which it can explain Home Bias is not completely clear. Beneish et al. (2009) study mandatory IFRS adoptions and find no evidence of reduction in Home Bias. Beneish and Yohn (2008) 15

17 suggest that the lack of effects on Home Bias from IFRS adoptions might be due to other factors, such as behavioral biases, being more important to explain the phenomenon. To test the information costs hypothesis in the Euronext context, we compare changes in domestic and foreign ownership of Euronext-listed segment companies with similar ownership changes of other Euronext-listed firms (that is, non-segment firms). Euronext established the segments to achieve even more consistent regulation of at least some firms from the markets in each country, and to provide a mechanism by which firms could pre-commit themselves to enhanced financial reporting quality and corporate governance. Pownall et al. (2011) documented increases in accounting quality and liquidity for the segment firms relative to the non-segment firms. This evidence that the ability to pre-commit to enhanced financial reporting and corporate governance was credible leads us to hypothesize that if information costs explain Home Bias, we should observe reduced Home Bias for the segment firms. We formulate the following hypotheses: H 02 : Foreign ownership did not increase relative to domestic ownership for segment-listed companies at the time of the merger that created Euronext, compared to other Euronextlisted companies and to other EU companies. H A2 : Foreign ownership increased relative to domestic ownership for segment-listed companies at the time of the merger that created Euronext, compared to other Euronextlisted companies and to other EU companies. 3. Data, Empirical Design, and Results 3.1. Data 16

18 Our ownership data come from the Ownership Module in Thomson One Banker, which covers 53,000 stocks in more than 70 countries. The data include ownership levels and percentages by year according to investor types and sub-types as well as individual owners. It is reported to Thomson One Banker by investors who own shares over the minimum regulatory threshold of their domestic countries. A discussion with Thomson One representatives indicates two possible shortcomings of the database. First, the information is self-reported by investors, thus certain inaccuracies exist because investors misreport. Second, it is collected at different points in time during the year. Shares that change hands might be double-counted if reported first by the seller and later on by the buyer. We conduct our regression analyses using only mutual fund ownership because mutual fund ownership data are less likely subject to these shortcomings than the overall data provided by various investors, given that mutual funds are required in the U.S. and in Europe to report their holdings. The focus on the mutual fund ownership data is also consistent with prior literature (see, DeFond et al and Yu 2009, for example). The Ownership module covers more than 34,000 global mutual funds. 10 Panel A of table 1 describes our sample selection. We start with an initial sample of 6,081 firms (46,141 firm-years) from the EU with non-missing mutual fund ownership data for the period Of these firms, 1,012 are from the four Euronext countries (and the majority of those are from France), and 5,069 are from the rest of the EU. We require that total ownership 10 It is possible that hedge funds are more affected by transaction costs because they are more likely to use complex trading strategies across national borders. Thus it would be interesting to investigate hedge fund ownership data as an additional test. While Thomson One Banker covers some hedge fund ownership data, the coverage is limited and not comprehensive. For example, we checked the hedge fund ownership data for firms listed on the four exchanges of Euronext. The number of observations of hedge fund data is only 1-2% of that of mutual fund data. In addition, the ownership data only reflect hedge funds from Denmark, Finland, Germany, Italy, Spain, Sweden, Switzerland, UK, and USA, but not other countries. Given this data limitation, we choose to focus on mutual fund ownership data. See for more details on the database. It is also the case that some mutual funds will be less interested in firm-specific information, and will face lower transaction costs than other sorts of investors. To the extent that these characteristics are more typical of mutual funds than other investors, using mutual fund data will bias against rejection of the null hypotheses. 17

19 not exceed 100%, resulting in the loss of 18 firms and 1,939 firm-years. 11 We exclude 406 firms (5,037 firm-years) due to zero domestic mutual fund ownership, 283 firms (2,051 firm-years) due to missing Worldscope data, and 451 firms (5,541 firm-years) for missing control variables. Further, we lose 61 firms (1,141 firm-years) when we exclude the top and bottom half percent of each continuous control variable distribution. Finally, we require a balanced sample with firms appearing both prior and subsequent to the formation of Euronext, which excludes 2,552 firms (8,672 firm-years), leaving a sample of 2,310 firms with 21,760 firm-years. [Insert Table 1 About Here] Panel B of table 1 disaggregates the primary sample into Euronext country firms and other EU firms. Approximately one quarter of the sample are firms from the four Euronext countries, including 169 (376) segment (non-segment) firms. The majority of both segment and non-segment firms are from France (98 segment firms and 276 non-segment firms). In contrast, Portugal contributes only eight segment and 26 non-segment firms. The other EU firms come from 12 countries, with the majority (787 firms) coming from the London Stock Exchange. The second largest concentration of other EU firms is from Germany (400 firms), and only two (one) firms come from Denmark (Luxembourg). Table 2 presents summary statistics for the mutual fund ownership data, broken down in panel A by Euronext vs. other EU firms and by pre- vs. post-merger time periods. There is a statistically significant increase in mutual fund holdings for both Euronext and other EU firms. In addition, all mutual fund ownership categories increase by a statistically significant amount 11 Despite the shortcomings of the Thomson One ownership data, we use them as a proxy for true ownership with the assumption that there is no bias between the segment firms and the non-segment firms and between Euronext listed firms and non-euronext listed firms that would shift the percentage of foreign relative to domestic ownership. Further, assuming that errors and omissions in ownership data occur randomly between foreign owners and domestic owners or are stable over time, we control for changes in domestic ownership when evaluating changes in foreign ownership. 18

20 from the pre- to the post-merger period for the Euronext firms. For the other EU firms, although the undeflated values of all ownership categories increase significantly at the mean and median, when these values are deflated by domestic ownership only mutual fund ownership from Euronext countries increases significantly at the mean and median, and all foreign and EU mutual fund ownership increase significantly at the median. These observations, especially for the foreign ownership categories standardized by domestic ownership to control for secular trends in mutual fund percentage ownership, are strongly consistent with diminishing Home Bias for Euronext firms from before to after the merger. Panel B presents the same statistics broken down by segment vs. non-segment firms and pre- vs. post-merger time periods. Total mutual fund ownership increases from pre- to post-merger for the segment and the non-segment firms. Domestic mutual fund ownership increases for the non-segment firms, but it exhibits little change for the segment firms. While all categories of foreign ownership increase significantly (at the mean and median) for the segment firms, only total foreign and EU ownership increase significantly at the mean and median for non-segment firms when foreign ownership is deflated by domestic ownership. [Insert Table 2 About Here] We also evaluated the differences in Home Bias between the Euronext segment and nonsegment firms both before and after the merger. Before the merger, foreign mutual fund ownership of segment firms was significantly lower (at both the mean and median) than that of non-segment firms. However, after the merger (the integration of the trading platform and the 19

21 segmentation of the stock list) segment firms' foreign mutual fund ownership was not statistically distinguishable from that of non-segment firms Empirical Design Our basic empirical design uses a difference-in-differences approach to control for contemporaneous financial and political factors and events that are common within each country or across the EU. 13 Specifically, we use each firm as its own control by focusing on the differences from before to after the merger in foreign relative to domestic mutual fund ownership measures for EU firms. We regress the ownership measures on indicator variables for: (1) the pre- vs. post-merger periods (to determine whether the integration of the Euronext market was associated with an overall change in Home Bias); (2) the segment firms vs. the non-segment firms (to determine whether choosing to become listed on the named segments was associated with a different level of Home Bias); (3) the interaction between the post-merger period and Euronext non-segment firms (to determine whether the change in Home Bias from before to after the integration was significant for the non-segment firms); and (4) the interaction between the post-merger period and the segment firms (to determine whether the change in Home Bias from before to after the integration was significant for the segment firms). Our basic regression model is: 12 This description focuses on total foreign mutual fund ownership scaled by domestic mutual fund ownership. For other definitions of foreign vs. domestic mutual fund ownership, the description is generally similar. We also compared the segment and non-segment firms before and after the merger, including only the French firms (since French firms dominate both the segment and the non-segment subsamples). The differences between the French firms are mixed and generally not statistically different. 13 A change specification is an alternative research design to control for correlated omitted variables. As discussed in section 4 below, we replicated our analyses using a change specification, which supported exactly the same inferences as the results using the difference-in-differences specification. In addition, because firms chose to join the segments, our empirical design needs to control for endogeneity. See section 4 for a diagnostic on our primary results using a propensity score matched sample as a benchmark. 20

22 Log (1 + MF_for/dom ijt ) = + 1Post + 2Segment + 3NonSegment + 4Post*Segment + 5Post* NonSegment + kcontrols) + it (1) where: MF_for/dom j = relative foreign to domestic mutual fund ownership, where foreign mutual fund ownership is measured as total foreign ownership when j=1, non-euronext EU ownership when j=2, Belgian, Dutch, French, and Portuguese ownership but excluding the firm's home country owners when j=3, and UK ownership when j=4; and domestic ownership is measured as total domestic mutual fund ownership 14 ; Post = an indicator variable equal to 1 for 2002 (for the other EU firms) and the year in which the firm s exchange joined Euronext (for the Euronext firms), and all years thereafter; 15 Segment = an indicator variable equal to 1 for companies included in the NextPrime or NextEconomy segments during any year of the sample period; NonSegment = an indicator variable equal to 1 for companies listed on Euronext but not included in the NextPrime or NextEconomy segments during any year of the sample period; and firm-specific variables are included to control for each firm's appeal to mutual fund investors, along with industry, country, and year fixed effects. 16 All variables included in the regressions are summarized in the appendix. As the appendix shows, we control for leverage, profitability, the number of foreign exchanges on which the firm is traded, size, Big-5 auditor, conformity with US GAAP or IFRS, cross-listing in the US, stock return volatility, liquidity (measured as the number of trading days with zero returns, following Ashbaugh et al. 2005), being included in the 2007 Morgan Stanley Capital International (MSCI) Index, 17 dividend yield, 14 We use the natural log of this variable because the ownership data is non-normal (see table 2) and we chose not to mitigate the problem by truncation to avoid the loss of data truncation would entail. 15 This year is 2002 for Amsterdam, Brussels, and Paris, and 2004 for Lisbon. 16 We use fixed effects to control for the possibility that either transaction costs or information environment may vary between industries or countries and/or may vary from year to year in ways not captured by our difference-indifferences design. Partitioning the sample on any of these three variables leads to unreasonably small sample sizes, particularly for the segment firms. 17 We use the MSCI World Index as a proxy for the company being included in a major market index. It is a freefloat-adjusted market cap weighted index that combines 24 developed and 21 developing country indices, and therefore measures the performance of a broad range of developed and developing economies ( Since the information that MSCI Inc. provided was for the 21

23 earnings-price ratio, analyst following, number of stock indices in which the company is included, and ownership concentration. 18 Panel A of table 3 gives summary statistics for the control variables for the full sample. The table shows that sample firms are quite heterogeneous on most dimensions, thus our empirical design contains a number of controls. By splitting the time period into pre- and postmerger periods, we are using each firm as its own control. By comparing the segment firms and non-segment firms, we are controlling for other financial and political factors in the four countries that may have had an impact on mutual fund ownership patterns. Finally, by including a large sample of other EU firms in our regressions, we are controlling for financial, political, and cultural shifts in the EU, which may have had an impact on ownership patterns. [Insert Table 3 About Here] Panel B of table 3 compares the Euronext and other EU firm subsamples. Before the merger, Euronext firms were significantly more levered, more profitable, larger, more liquid, with more volatile returns, more analyst following, and more concentrated ownership (all differences significant at the mean and median), relative to other EU firms. After the merger, Euronext firms are still significantly more levered, more profitable, and larger, with more analyst composition of the index in the period , and therefore failed to cover the whole sample period, we do not use the year-specific constituents of the index, but instead rely on the 2007 index composition only, given 2007 is the middle point of the data we have available (see also DeFond et al for a similar approach). The MSCI data contained herein are the property of MSCI Inc. (MSCI, its affiliates and any other party involved in, or related to, making or compiling any MSCI data, make no warranties with respect to any such data. The MSCI data contained herein is used under license and may not be further used, distributed or disseminated without the express written consent of MSCI.) 18 These controls are taken from Florou and Pope (2009); Bradshaw et al. (2004), Covrig et al. (2007), Hamberg, Mavruk, Sjogren (2009), and Yu (2010), among others. In untabulated diagnostic analyses, we also included free float (measured as the percentage of closely held shares) and being traded as an ADR either OTC or on a national exchange in the US. Including these variables used in the literature did not change our inferences, and severely reduced our sample size due to lack of free float data. 22

24 following and more concentrated ownership (all differences significant at the mean and median), but the significance of the differences in return volatility and liquidity is attenuated. Panel C compares the segment and non-segment firms. Before the merger, firms that eventually signed Commitment Agreements and became listed on the segments were less levered, smaller, and more liquid (all differences significant at the mean and median) than the non-segment firms. After the merger, segment firms were still less levered, smaller, and more liquid, and also had less concentrated ownership than did non-segment firms Regression Results Table 4 shows the results of estimations of eq. (1) using four categories of foreign mutual fund ownership (standardized by domestic mutual fund ownership to control for secular trends in the data) for the full sample of EU firms. Column 1 uses the log of (1 + total foreign mutual fund ownership divided by domestic mutual fund ownership) as the dependent variable, column 2 uses other EU mutual fund ownership as the measure of foreign ownership, column 3 uses only foreign mutual fund owners domiciled in Euronext countries other than the sample firm s domicile as the measure of foreign ownership, and column 4 uses UK mutual fund ownership as the measure of foreign ownership to capture the idea that mutual fund managers in a major EU financial center may have different patterns of buying and selling foreign securities than mutual fund managers not so centrally located. The coefficient on Post is positive and significant only when foreign ownership is measured as total foreign ownership, suggesting an overall decrease in Home Bias from the preto the post-period, but not because EU mutual funds increased their investment in EU firms more than domestic mutual funds did. The coefficients on both Segment and Non-segment are positive and significant except when foreign ownership comes from the other Euronext jurisdictions, 23

25 consistent with less Home Bias on average for the Euronext firms outside the Euronext countries than for the other EU firms. The coefficient on the interaction between Post and Segment is always positive and significant, indicating that the decrease in Home Bias for the segment firms relative to other EU firms is more pronounced no matter how foreign ownership is measured. Finally, the coefficient on the interaction between Post and Non-segment is not significant in any of the four specifications, suggesting that the integration of the trading platforms after the merger did not have a significant effect on Home Bias for the non-segment firms. Tests of hypothesis 1 (that transaction costs fall more significantly for investors from the Euronext countries at the merger, which should be associated with a diminution of Home Bias if Home Bias is driven by transaction costs) in their most straightforward form focus on the column 3 results (other Euronext countries' ownership only), specifically the interactions between Post and Segment or Non-segment. These results support the alternate hypothesis for the segment firms, but fail to reject the null for the non-segment firms. We conclude that there is only limited evidence consistent with the transaction costs hypothesis. [Insert Table 4 About Here] Tests of hypothesis 2 (that the enhancements in accounting, disclosure, transparency, and corporate governance pledged by the segment firms reduced perceived information costs to foreign investors, and were associated with reductions in Home Bias) focus on the coefficients in all four columns on the interaction between Post and Segment. The coefficient is positive and significant in all columns, offering strong support to the information costs hypothesis. Among the control variables, leverage and being included in more stock indices are positively associated with Home Bias; size, return volatility, and analyst following are negatively 24

26 associated with Home Bias; but none of the other control variables are consistently significant. The R - squares of the regressions range from 20% to 34%. 19 Because our hypotheses are framed as tests on changes in Home Bias for Euronext firms at the time of the merger, including other EU firms as controls for EU events and forces may be redundant to including all Euronext firms. Therefore, we present results for only the Euronext firms in table 5. We are unable to estimate the coefficient for NonSegment and for the interaction between Post and NonSegment when we include only Euronext firms in the sample. The coefficient on Post is insignificant, indicating no general reduction in Home Bias for Euronext firms from pre- to post-merger. The coefficient on Segment is negative and significant when foreign ownership stands for either all foreign ownership or other EU ownership, meaning that in general other EU and all foreign mutual funds were less likely to hold segment firms equities. However, the interaction between Post and Segment is positive and significant using all four proxies for foreign mutual fund ownership, indicating a bigger reduction in home bias for the segment firms from pre- to post-merger. This result suggests that the ability to join the segment and credibly pre-commit to enhanced disclosure and transparency decreased information costs for foreign investors (including EU investors) and was associated with increases in their ownership of segment firms but not of non-segment firms. This result is consistent with the information costs hypothesis but not with the transaction costs hypothesis. 20 [Insert Table 5 About Here] 19 In untabulated analyses, we included interaction terms between profitability (Profit) and Post, and between size (Logassets) and Post. The coefficients on the interaction between size and Post were all positive and significant, and the interactions between profitability and Post were negative and significant at 0.05 for total foreign mutual fund ownership and other Euronext country mutual fund ownership. Results of the regressions including these interaction effects support exactly the same inferences as the results in table In untabulated analyses, we also included interaction terms between profitability and Post, and between size and Post. Results of the regressions including these interaction effects in the subsample of firms listed on Euronext also support exactly the same inferences as the results in table 5. 25

27 4. Diagnostics and Extensions 4.1 Propensity Score Matching First, we estimated equation (1) comparing results for the segment firms to a propensity score matched sample of non-segment firms, and separately to a propensity score matched sample of other EU firms. Our descriptive statistics in table 3 show that segment firms differ significantly from non-segment firms and other EU firms on a number of firm characteristics both before and after the formation of Euronext. Among these characteristics are leverage, profitability, number of foreign exchange listings, size, Big-5 auditor, IFRS or US GAAP, US cross-listing, return volatility, and trading liquidity. The accounting literature has demonstrated that these characteristics affect not only the reporting decisions of firms but also their decisions to list or cross-list on a highly-regulated exchange (see Lang et al for an example). Thus, the investment decisions of foreign mutual funds might not be independent of firms' decisions to list on a named segment and our results might be influenced by selection bias. To control for such bias, we match the segment and non-segment firms (other EU firms) on these firm characteristics to obtain a sample of non-segment firms (other EU firms) with similar likelihood of listing on a named segment. Given the large number of independent variables which need to be matched, we expect that a simple matching procedure will result in a large loss of observations. Instead, to minimize this loss, we choose to conduct a propensity score matching (PSM) analysis. The PSM analysis allows us to calculate a propensity score for each observation in our sample, as a single balanced representation of all firm characteristics of interest (Guo and Fraser 2010). We conducted the PSM analysis separately pre- and post-merger. We first present the results from the PSM sample of segment firms and the matched other EU firms in Table 6. Panel 26

28 A reports two logistic regressions used to calculate the propensity scores for the sample of segment firms and the sample of other EU firms. The results of these regressions indicate that both before and after the formation of Euronext, the probability of joining a named segment is positively and significantly related to leverage, profitability, trading liquidity, analyst following, and ownership concentration. The probability of joining a named segment is also negatively and significantly related to the number of stock exchanges and the number of stock indices. Using the calculated propensity scores from these logistic regressions, we perform a nearest neighbor within caliper matching of segment and non-segment firms. We keep in our PSM sample only pairs of segment and other EU firm-years for which the absolute value of the difference in their propensity scores is no larger than In the process of matching we lose segment and other EU firm observations that are not in the common support region of the calculated propensity scores, leaving 1,141 firms and 3,564 firm-years (see panel A of table 1). [Insert Table 6 About Here] To evaluate whether our PSM procedure was successful in eliminating the differences in firm characteristics between the segment and other EU firms, we recalculated the descriptive statistics by period, the difference between means and medians, and the statistical significance of these differences. The comparison in panel B, table 6 indicates that the PSM procedure successfully eliminated the difference in means and medians between the segment and non-eu firms for most firm characteristics. The firms in our PSM sample pre-merger differ significantly only on mean and median ownership concentration and closely held shares percentage and on 27

29 median analyst coverage, and post-merger on mean and median closely held shares percentage and on median analyst following and ownership concentration. 21 Panel C presents results of the PSM Home Bias analyses, and shows inferences very similar to those in table 4. The coefficient on the interaction term for segment firms after the formation of Euronext is positive and significant for all three columns, indicating that the diminution of Home Bias for the segment firms was higher than that for other EU firms using any of the proxies for Home Bias. Similarly, we formed another PSM sample of segment firms and non-segment Euronext firms following the same method. Panel A of table 7 reports two logistic regressions used to calculate the propensity scores. The results of these regressions indicate that both before and after the formation of Euronext, the probability of a Euronext firm joining a named segment is positively and significantly related to being audited by a Big-5 auditor and trading liquidity. The probability of a Euronext firm joining a named segment is also negatively and significantly related to the number of stock exchanges and firm size. We again keep in our PSM sample only pairs of segment and non-segment firm-years for which the absolute value of the difference in their propensity scores is no larger than After this requirement, we are left with 461 firms and 2,882 firm-years (see panel A of table 1). [Insert Table 7 About Here] Panel B of table 7 reports the descriptive statistics of the PSM sample by period, the difference between means and medians, and the statistical significance of these differences. The 21 We did not include the closely held shares variable in the logit regression to calculate the PSM scores as shown in panel a, table 6 (table 7), because the requirement of non-missing closely held shares severely reduces the sample size. So there are significant differences in closely held shares between segment firms and other EU firms (nonsegment firms) in the PSM sample. To increase our confidence in the inferences drawn from the PSM sample, we repeat the regressions by including control variables including closely held shares, and we obtain similar results as those in panel c, table 6 (table 7). 28

30 comparison indicates that the PSM procedure successfully eliminated the difference in means and medians between the segment and non-segment firms for most firm characteristics. Finally, panel C presents results of the PSM Home Bias analyses, and shows inferences very similar to those in table 5. The coefficient on the interaction term for segment firms after the formation of Euronext is positive and significant for columns (1), (2), and (4), indicating that the diminution of Home Bias for the segment firms was higher than that for non-segment firms using most proxies for Home Bias. 4.2 The Effect of Segment Firms Inclusion in Equity Indices in Europe Next, we investigate an alternative explanation of the results regarding the segment firms. It is possible that segment firms are more likely to be included in equity indices of the pan-eu market after the formation of Euronext, and mutual funds around the world tend to invest in equity indices rather than individual stocks. In addition, mutual funds may have faced less "fiduciary liability risk" associated with investments in segment firms, because it was more straight forward to explain to investors that the funds chose to invest in segment firms because of their commitment to higher reporting standards. To parse out these effects, we employ the segment compliance data we collected in Pownall et al. (2011), table 1. Specifically, we use a compliance score to measure segment firms compliance with the enhanced financial reporting requirements. Compliance is the sum of five dimensions including global auditors, IFRS, English language reporting, functioning website, and quarterly reporting. We also construct another compliance score (Compliance 4) by adding the requirements on global auditors, IFRS, English reporting, and functioning website because these four requirements are more likely to reduce the information accessing and processing costs for foreign investors while quarterly reporting makes information more available to both domestic and foreign investors. For each dimension of 29

31 compliance requirements, we define an indicator variable equal to one if the segment firm s average compliance in the post-merger period is above the median average, and zero otherwise. We then add the five indicators together to get the summary compliance score. If the reduction of equity Home Bias we report in our paper is due to equity index inclusion and/or fiduciary liability risk as opposed to increased information quality of segment firms, we would not observe cross-sectional variation in the reduction of equity Home Bias among segment firms in the postmerger period. We run a regression of Log (1+ MF_for/dom) that includes Compliance, Post, an interaction term of Compliance and Post, and the other control variables included in tables 4 and 5. The results are reported in table 8. Column (1) reports the regression results on Compliance, and column (2) reports that on Compliance 4. While we do not find a significant coefficient on the interaction term in column (1), we document a positive and statistically significant coefficient on the interaction term in column (2) when compliance is measured using the four dimensions of compliance that are more likely to increase accounting information comparability of segment firms. 22 This result does not support inclusion in an index and/or fiduciary liability risk as an alternative explanation of our segment results. On the other hand, it provides further support that enhanced financial reporting requirements, in particular those making accounting information more available and more comparable, have the potential to reduce Home Bias. [Insert Table 8 About Here] 4.3 Other Robustness Checks We performed several replications of our primary analyses to assess the sensitivity of our results to including various sample definitions and combinations of control variables. 22 We also run regressions using other forms of foreign mutual fund ownership relative to domestic mutual fund ownership as the dependent variables, and we obtain qualitatively similar results as those in table 8. 30

32 Specifically, we reran the regressions in equation (1) including only the test variables and no control variables, and including the Closelyheldsharespct variable, which results in the loss of almost 4,000 observations. The results of these re-estimations support the same inferences as those reported in tables 4 and 5. We also replicated our primary analyses using a change specification, in which the change in foreign relative to domestic ownership is the dependent variable, and the independent variables are all expressed as changes from before to after the merger. Not surprisingly, our inferences from the change specification are exactly the same as the inferences from the difference-in-differences specification in tables 4 and 5. Finally, we reran the analyses after excluding these Euronext-listed firms that are also cross-listed on U.S. stock exchanges. These tend to be the largest firms in the sample and all but one of them are non-segment firms. These firms would have little incentive to pre-commit to enhanced transparency and disclosure by signing the Commitment Agreements, because their U.S. exchange listing already allows them to "bond" to high quality financial reporting. The results of this replication support the same inferences as those in table Summary and Conclusions In this paper, we examined the ability of global stock exchange mechanisms to reduce equity Home Bias the phenomenon that investors everywhere tend to over-invest in domestic securities and under-invest in foreign securities relative to optimal global portfolio diversification. Our research setting is the formation in 2000 and 2002 of the Euronext stock market from the merger, demutualization, and IPO of the Amsterdam, Brussels, Paris, and 31

33 Lisbon stock exchanges. The two mechanisms we investigate are the integration of trading platforms across the four national predecessor exchanges, and the creation of named segments of the exchange on which firms could voluntarily list by pre-committing to enhanced disclosure and transparency. Our empirical approach is to compare changes in foreign and domestic ownership of Euronext-traded firms with concurrent changes in foreign and domestic ownership of other EU companies to isolate the effects of the integration of the Euronext market from the effects of other developments in the EU financial and political regime taking place concurrently. We find that the integration of the Euronext market was associated with a reduction in Home Bias for firms listed on the named segments of the Euronext exchange, but not for the non-segment Euronext firms. Our results suggest that the reduction in transaction costs from the integration of the trading platform did not make the non-segment Euronext firms more attractive to the specific investors for whom the transaction costs were reduced. While there is no diminution of Home Bias measured as the holding of other Euronext countries relative to domestic holdings for nonsegment firms, we document significant increases in all categories of foreign holdings relative to domestic holdings for Euronext segment firms. We interpret this as suggesting that the decrease in information costs due to the pre-commitments to enhanced transparency made the segment firms more attractive to all categories of foreign investors, consistent with the information costs hypothesis. These results are important in light of the ongoing consolidation of global stock exchanges. Integrating the trading platforms of stock exchanges from different countries has the potential to create ever larger pools of liquidity, especially in the presence of a mechanism such as the Euronext named segments that allows firms to commit to higher standards than those 32

34 effectively imposed by their home countries. Our results suggest that the enhanced financial reporting and corporate governance supported by the segmentation mechanism have the potential to reduce Home Bias for firms listed on the integrated global capital markets and allow investors to garner more of the benefits of international diversification. 33

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38 Appendix: Variable definitions and data sources Variable Definition Source MF_total = percentage of total mutual fund ownership for firm i ThomsonOwnership MF_domestic = percentage mutual fund ownership from firm i s home country ThomsonOwnership MF_foreign = percentage mutual fund ownership from all countries other than firm i's home country ThomsonOwnership MF_eu MF_euronext = percentage mutual fund ownership from other European countries not including Euronext countries and firm i's home country = percentage mutual fund ownership from Euronext countries excluding firm i's home country if firm i is part of Euronext ThomsonOwnership ThomsonOwnership MF_uk = percentage mutual fund ownership from UK only (this measure is not available for UK firms) ThomsonOwnership MF_for/dom = MF_foreign divided by MF_domestic ThomsonOwnership MF_eu/dom = MF_eu divided by MF_domestic ThomsonOwnership MF_euronext/do m = MF_euronext divided by MF_domestic ThomsonOwnership MF_uk/dom = MF_uk divided by MF_domestic (this measure is not available for UK firms) ThomsonOwnership Lev = total liabilities/total assets WorldScope Profit = net income/total assets WorldScope FExchange = number of foreign exchanges on which firm i is listed WorldScope Logassets = natural logarithm of total assets WorldScope (continues on next page) 37

39 Appendix (continues from previous page) Variable Definition Source Auditor = an indicator variable equal to one if firm i is audited by a Big-5 auditor and zero otherwise WorldScope IFRS_USGAAP = an indicator variable equal to one if firm i uses IFRS or US GAAP and zero otherwise WorldScope USCrosslisted = an indicator variable equal to one if the firm i is listed on the OTC market or the Pink Sheets, two if it is listed on the NYSE, NASDAQ or AMEX, and zero otherwise ADR database of Bank of NY Mellon ret_std = standard deviation of daily returns in year t Datastream perc_zeroret = percentage of zero returns for firm i in year t Datastream MSCI = an indicator variable equal to one if the firm i is included in the Morgan Stanley Capital International (MSCI) Index and zero otherwise MSCI Inc. div_yield = dividends per share/year-end market price WorldScope PERatio = year-end market price/earnings per share WorldScope AF = total number of analysts following firm i in year t IBES Detailed History file StockIndex = number of stock indices in which firm i is included WorldScope Herfindahl = the sum of the squares of the ownership of the three largest shareholders in firm i ThomsonOwnership Closelyheldshare spct = percentage of closely held shares of firm i in year t WorldScope Post = indicator variable equal to one for 2002 (for the non-euronext EU firms) or the year the exchange joined Euronext and all the years thereafter (for the Euronext firms for Paris, Brussels, and Amsterdam, and 2004 for Lisbon) n/a (continues on next page) 38

40 Appendix (continues from previous page) Variable Definition Source Segment = indicator variable equal to one for companies in the NextPrime or NextEconomy segments Euronext Cash marketduring any year of the sample period and zero otherwise Monthly statistics ( Non-segment = an indicator variable equal to one for Euronext companies not included in the NextPrime or NextEconomy segments during any year of the sample period and zero otherwise Euronext Cash market- Monthly statistics 39

41 Table 1: Sample selection and composition Panel A. Sample selection procedure Firm-year Firm All EU firms with non-missing ownership data 46,141 6,081 Euronext 8,785 1,012 Other EU 37,356 5,069 Less total ownership > 100% 44,202 6,063 Less zero domestic ownership 39,165 5,657 Merge with WorldScope data 37,114 5,374 After requiring non-missing control variables 31,573 4,923 After truncating control variables at 0.5% and 99.5% 30,432 4,862 After requiring balanced sample 21,760 2,310 After PSM matching Euronext Segment and Non-Segment 2, After PSM matching Euronext Segment and Other EU 3,564 1,141 (continues on next page) 40

42 Table 1 (continues from previous page) Panel B. Final sample distribution Euronext/non-Euronext; segment/non-segment Firm-Years Firm Balanced sample 21,760 2,310 including: Euronext Segment Amsterdam (1.44%) (1.26%) Brussels (1.69%) (1.47%) Lisbon 84 8 (0.39%) (0.35%) Paris 1, (4.71%) (4.24%) Non-segment Amsterdam (2.22%) (1.86%) Brussels (1.50%) (1.34%) Lisbon (1.19%) (1.13%) Paris 2, (12.38%) (11.95%) (continues on next page) 41

43 Panel B. (continues from previous page) Non-Euronext Table 1 Budapest (0.49%) (0.48%) Copenhagen 5 2 (0.02%) (0.09%) Dublin (0.18%) (0.52%) Frankfurt 3, (15.41%) (17.32%) Helsinki (2.92%) (3.42%) London 7, (35.40%) (34.07%) Luxembourg 3 1 (0.01%) (0.04%) Madrid 1, (5.16%) (4.42%) Milan 1, (6.51%) (6.75%) Stockholm 1, (5.48%) (6.49%) Vienna (1.58%) (1.39%) Warsaw (1.31%) (1.43%) The table includes the sample selection procedures and detailed composition of the sample used in the paper. Panel A describes the formation of the sample. Panel B shows the break-down of firm-years and firms included in the final sample by exchanges participating in the Euronext merger and exchanges that did not participate in the merger, and by segment/non-segment for the Euronext exchanges. It also shows what percentage of the total sample each of the groups represents. 42

44 Table 2: Summary statistics on mutual fund ownership Panel A: By Euronext/non-Euronext and by pre-/post-merger Euronext Pre-merger Post-merger N Mean Median StDev N Mean Median StDev MF_total 1, , *** *** MF_domestic 1, , *** *** MF_foreign 1, , *** *** MF_eu 1, , *** *** MF_euronext 1, , *** *** MF_uk 1, , ** *** MF_for/dom 1, , *** *** MF_eu/dom 1, , *** *** MF_euronext/dom 1, , *** *** MF_uk/dom 1, , ** ** Non-Euronext Pre-merger Post-merger N Mean Median StDev N Mean Median StDev MF_total 5, , *** *** MF_domestic 5, , *** *** MF_foreign 5, , *** *** MF_eu 5, , *** *** MF_euronext 5, , *** *** MF_uk 2, , MF_for/dom 5, , * *** MF_eu/dom 5, , ** *** MF_euronext/dom 5, , * *** MF_uk/dom 2, , *** (continues on next page) 43

45 Table 2 (continues from previous page) Panel B: By segment/non-segment and by pre-/post-merger Segment Pre-merger Post-merger N Mean Median StDev N Mean Median StDev MF_total , *** *** MF_domestic , * MF_foreign , *** *** MF_eu , *** *** MF_euronext , *** *** MF_uk , ** *** MF_for/dom , *** *** MF_eu/dom , ** *** MF_euronext/dom , *** *** MF_uk/dom , * *** Non-segment Pre-merger Post-merger N Mean Median StDev N Mean Median StDev MF_total 1, , *** *** MF_domestic 1, , *** *** MF_foreign 1, , *** *** MF_eu 1, , *** *** MF_euronext 1, , *** *** MF_uk 1, , MF_for/dom 1, , *** ** MF_eu/dom 1, , *** MF_euronext/dom 1, , *** MF_uk/dom 1, , * (continues on next page) 44

46 Table 2 (continues from previous page) Panel A of the table includes the number of firm-years, mean, median and standard deviation of various ownership measures for firm-years belonging and not belonging to Euronext before and following the Euronext merger. Panel B of the table compares various ownership measures for firm-years that belong and do not belong to the NextPrime and NextEconomy segments before and following the Euronext merger. In both panels, MF_dom is defined as the percentage mutual fund ownership from the same country as the sample firm, MF_for is the percentage mutual fund ownership from all countries other than the sample firm's home country, MF_eu is the percentage mutual fund ownership from other European countries not including Euronext countries and the sample firm's home country, MF_euronext is the percentage mutual fund ownership from Euronext countries not including the sample firm's home country, MF_uk is the percentage mutual fund ownership from UK only (this measure is not available for UK firms), MF_for/dom is a ratio of MF_for to MF_dom, MF_eu/dom is a ratio of MF_eu to MF_dom, MF_euronext/dom is defined as a ratio of MF_euronext to MF_dom. Finally, MF_uk/dom is a ratio of MF_uk to MF_dom (this measure is not available for UK firms). The comparison of means in both panels is based on a two-sided t-test with unequal differences. The comparison of medians is based on a Wilcoxon rank-sum test. ***, **, and * indicate significance at 0.01, 0.05, and 0.10 levels, respectively. 45

47 Table 3: Summary statistics Panel A: Summary statistics full sample Variable N Q1 Median Q3 Mean Std Dev Lev 21, Profit 21, FExchange 21, Logassets 21, Auditor 21, IFRS_USGAAP 21, USCrosslisted 21, ret_std 21, perc_zeroret 21, MSCI 21, div_yield 21, PERatio 21, AF 21, StockIndex 21, Herfindahl 21, Closelyheldsharespct 18, (continues on next page) 46

48 Table 3 (continues from previous page) Panel B: Summary statistics on control variables by Euronext membership pre- and post-merger Pre-merger Euronext Non-Euronext N Mean Median StDev N Mean Median StDev Lev 1, , *** *** Profit 1, , *** *** FExchange 1, , * ** Logassets 1, , *** *** Auditor 1, , *** *** IFRS_USGAAP 1, , *** *** USCrosslisted 1, , * ret_std 1, , ** *** perc_zeroret 1, , *** *** MSCI 1, , div_yield 1, , *** PERatio 1, , AF 1, , *** *** StockIndex 1, , Herfindahl 1, , *** *** Closelyheldsharespct 1, , *** *** (continues on next page) 47

49 Panel B (continues from previous page) Table 3 Post-merger Euronext Non-Euronext N Mean Median StDev N Mean Median StDev Lev 3, , *** *** Profit 3, , *** *** FExchange 3, , *** Logassets 3, , *** *** Auditor 3, , *** *** IFRS_USGAAP 3, , USCrosslisted 3, , ** ret_std 3, , perc_zeroret 3, , *** MSCI 3, , ** ** div_yield 3, , ** PERatio 3, , AF 3, , *** *** StockIndex 3, , *** Herfindahl 3, , *** *** Closelyheldsharespct 3, , *** *** (continues on next page) 48

50 Table 3 Panel C: Summary statistics on control variables for the Euronext sample for segment/non-segment firms and pre/postmerger Pre-merger Segment Non-Segment N Mean Median StDev N Mean Median StDev Lev , *** *** Profit , *** FExchange , *** *** Logassets , *** *** Auditor , *** *** IFRS_USGAAP , USCrosslisted , *** *** ret_std , perc_zeroret , *** * MSCI , div_yield , PERatio , AF , *** StockIndex , *** Herfindahl , * Closelyheldsharespct , *** *** (continues on next page) 49

51 Panel C (continues from previous page) Table 3 Post-merger Segment Non-Segment N Mean Median StDev N Mean Median StDev Lev 1, , *** *** Profit 1, , *** FExchange 1, , *** *** Logassets 1, , *** *** Auditor 1, , *** *** IFRS_USGAAP 1, , ** ** USCrosslisted 1, , *** *** ret_std 1, , perc_zeroret 1, , *** *** MSCI 1, , *** *** div_yield 1, , PERatio 1, , AF 1, , *** *** StockIndex 1, , *** *** Herfindahl 1, , *** *** Closelyheldsharespct 1, , *** *** The table shows descriptive statistics for the companies in our sample. Panel A includes descriptive statistics for the entire sample. Panel B includes number of firm-years, means, medians, and standard deviations for the Euronext and non-euronext firms in our sample separately for the pre- and post-euronext merger period. Panel C includes the number of firm-years, means, medians, and standard deviations for the segment and non-segment Euronext firms separately for the periods pre- and post the Euronext merger. In all three panels, Lev equals the ratio of total liabilities to total assets, Profit is the ratio of net income to total assets, FExchange is the total number of foreign exchanges on which the company is listed, Logassets is the natural logarithm of total assets, Auditor is a dummy variable equal to one if the company is audited by a Big-5 auditor and zero (continues on next page) 50

52 Table 3 (continues from previous page) otherwise, IFRS_USGAAP is a dummy variable equal to one if the company uses IFRS or US GAAP and zero otherwise. USCrosslisted is a dummy variable equal to 1 if the company is listed on the OTC market or the pink sheets, 2 if the company is listed on NYSE, NASDAQ or AMEX, and zero otherwise. Ret_std is the standard deviation of daily returns for company i in year t, per_zeroret is the percentage of zero-return days for company i in year t. MSCI is a dummy variable equal to one if the company is part of the MSCI index and zero otherwise, div_yield is the ratio of annual dividend to year-end share price, PERatio is the ratio of closing annual price to earnings per share. AF is the total number of analysts following company i in year t. StockIndex is the total number of indices of which company i is a member during a given year. Herfindahl is the sum of squares of ownership percentages for the three largest shareholders in a given company. Closelyheldsharespct is the percentage of closely held shares in a given company. The comparison of means in Panels B and C is based on a two-sided t-test with unequal differences. The comparison of medians in the two panels is based on a Wilcoxon rank-sum test. ***, **, * indicate statistical significance at the 0.01, 0.05, and 0.10 levels, respectively. 51

53 Table 4. Regression results for the EU sample -- all sample firms (1) (2) (3) (4) Log Log (1+MF_eu/dom) (1+MF_euronext/dom) Log (1+MF_for/dom) Log (1+MF_uk/dom) Post 0.192** (2.57) (1.08) (0.84) (0.87) Segment 0.299*** 0.569*** *** 0.466*** (2.73) (6.92) (12.03) (6.16) NonSegment 0.552*** 0.720*** *** 0.520*** (5.13) (9.37) (11.76) (7.34) Post*Segment 0.254*** 0.219*** 0.072*** 0.164*** (4.14) (4.26) (2.72) (3.43) Post*NonSegment (0.51) (1.20) (1.45) (1.53) Lev *** *** *** *** (4.13) (4.36) (2.95) (3.28) Profit *** *** (0.27) (2.60) (1.37) (6.46) FExchange * (1.51) (0.30) (0.82) (1.79) Logassets 0.096*** 0.041*** 0.011*** 0.055*** (8.63) (5.66) (3.62) (6.36) Auditor * (1.02) (1.51) (1.83) (0.17) IFRS_USGAAP 0.054* * (1.88) (1.23) (1.83) (0.44) USCrosslisted 0.120*** ** (3.76) (1.33) (2.04) (0.39) ret_std 2.957*** 2.251*** 0.777*** 3.260*** (3.96) (3.87) (3.08) (4.84) perczeroret *** *** *** (2.98) (4.03) (0.84) (4.95) MSCI 0.106*** *** (3.28) (1.29) (3.77) (1.35) div_yield (0.93) (0.92) (0.89) (1.64) PERatio (0.69) (0.65) (1.40) (1.20) AF 0.010*** 0.011*** 0.006*** 0.008*** (4.42) (6.77) (5.88) (4.13) StockIndex *** *** *** ** (2.70) (4.14) (2.83) (2.35) Herfindahl (0.05) (0.48) (0.67) (0.75) Constant 0.394*** * 0.627*** ** (5.19) (1.91) (23.85) (2.44) Observations 21,760 21,760 21,760 14,058 Adjusted R-squared (continues on next page) 52

54 Table 4 (continues from previous page) The table includes results from regression Log (1+ MF_for/dom) = α + β 1 Post + β 2 Segment + β 3 NonSegment + β 4 Post*Segment+ β 5 Post* NonSegment + β i Controls + ε for column (1) Log (1+ MF_eu/dom) = α + β 1 Post + β 2 Segment + β 3 NonSegment + β 4 Post*Segment+ β 5 Post* NonSegment + β i Controls + ε for column (2) Log (1+ MF_euronext/dom) = α + β 1 Post + β 2 Segment + β 3 NonSegment + β 4 Post*Segment+ β 5 Post*NonSegment + β i Controls + ε for column (3) and Log (1+ MF_uk/dom) = α + β 1 Post + β 2 Segment + β 3 NonSegment + β 4 Post*Segment+ β 5 Post*NonSegment + β i Controls + ε for column (4) The variables in the models and their sources are as defined in the appendix. ***, **, and * indicate statistical significance at 0.01, 0.05, and 0.10 levels, respectively. 53

55 Table 5. Regression results for the Euronext sample (1) (2) (3) (4) Log Log (1+MF_eu/dom) (1+MF_euronext/dom) Log (1+MF_for/dom) Log (1+MF_uk/dom) Post (1.44) (0.22) (0.57) (1.29) Segment *** *** * (4.02) (3.16) (1.13) (1.65) Post*Segment 0.226*** 0.168*** 0.049* 0.111** (3.15) (2.80) (1.78) (2.34) Lev *** *** *** (4.04) (3.99) (1.25) (4.80) Profit * *** (0.79) (1.88) (0.80) (3.08) FExchange (0.11) (1.29) (1.48) (0.97) Logassets 0.143*** 0.075*** 0.014** 0.058*** (5.29) (4.25) (2.31) (4.77) Auditor (0.32) (0.46) (1.35) (0.71) IFRS_USGAAP * (1.41) (1.76) (0.00) (0.76) USCrosslisted (0.94) (0.07) (1.04) (0.47) ret_std 5.680*** 5.034*** 1.398*** 4.230*** (2.78) (2.99) (2.91) (4.48) perczeroret ** ** ** *** (2.12) (2.32) (2.57) (5.35) MSCI (0.98) (0.55) (1.45) (0.83) div_yield (0.94) (0.84) (0.69) (1.41) PERatio (0.62) (0.15) (1.15) (0.88) AF (0.29) (1.36) (0.38) (1.43) StockIndex (0.91) (0.87) (0.27) (0.91) Herfindahl ** ** (2.24) (2.17) (0.84) (0.41) Constant 0.913*** 0.502*** 0.211*** 0.452*** (4.51) (3.71) (4.11) (4.70) Observations 5,556 5,556 5,556 5,556 Adjusted R- squared (continues on next page) 54

56 The table includes results from regression: Table 5 (continues from previous page) Log (1+ MF_for/dom) = α + β 1 Post + β 2 Segment + β 3 Post * Segment + β i Controls + ε in column (1) Log (1+ MF_eu/dom) = α + β 1 Post + β 2 Segment + β 3 Post * Segment + β i Controls + ε in column (2) Log (1+ MF_euronext/dom) = α + β 1 ppost + β 2 Segment + β 3 Post*Segment + β i Controls + ε in column (3) Log (1+ MF_uk/dom) = α + β 1 Post + β 2 Segment + β 3 Post * Segment + β i Controls + ε in column (4) The variables in the models and their sources are as defined in the appendix. ***, **, and * indicate statistical significance at 0.01, 0.05, and 0.10 levels, respectively. 55

57 Table 6: PSM matching Euronext Segment and Other EU Panel A: Logistic regression pre- and post-merger (1) (2) Pre-merger Post-merger Lev 1.054*** 1.293*** (4.92) (8.15) Profit 1.939*** 1.112*** (4.72) (3.85) FExchange *** *** (5.29) (8.83) Logassets ** *** (2.28) (4.98) Auditor *** (0.50) (3.94) IFRS_USGAAP *** (3.66) (1.60) USCrosslisted (1.07) (0.34) ret_std *** (0.63) (4.93) perczeroret *** *** (2.81) (15.20) div_yield ** 0.013** (2.07) (1.96) PERatio (0.11) (0.96) AF 0.047*** 0.028*** (6.00) (3.84) StockIndex *** *** (5.44) (5.49) Herfinadahl 0.311*** 1.499*** (2.84) (7.97) MSCI *** (3.50) Constant *** (2.75) (0.84) Observations 5,805 12,190 Pseudo-R (continues on next page) 56

58 Table 6 (continues from previous page) Panel B: Summary statistics between Euronext segment firms and matched other EU firms pre- and post-merger Pre-merger Euronext Segment Other EU (Non-Euronext) N Mean Median StDev N Mean Median StDev Lev Profit FExchange * Logassets Auditor IFRS_USGAAP USCrosslisted ret_std perc_zeroret MSCI div_yield PERatio AF * StockIndex Herfindahl *** *** Closelyheldsharespct *** *** (continues on next page) 57

59 Panel B (continues from previous page) Table 6 Post-merger Euronext Segment Other EU (Non-Euronext) N Mean Median StDev N Mean Median StDev Lev 1, , Profit 1, , FExchange 1, , Logassets 1, , Auditor 1, , IFRS_USGAAP 1, , USCrosslisted 1, , ret_std 1, , perc_zeroret 1, , MSCI 1, , div_yield 1, , PERatio 1, , AF 1, , *** StockIndex 1, , * Herfindahl 1, , *** Closelyheldsharespct 1, , *** *** (continues on next page) 58

60 Table 6 (continues from previous page) Panel C: Regression using the PSM sample: industry, exchange and year dummies are included in all regressions (1) (2) (3) Log(1+MF_for/dom) Log(1+MF_eu/dom) Log(1+MF_euronext/dom) Post 0.255* (1.89) (1.48) (1.57) Euronext Segment *** *** ** (6.33) (5.58) (2.46) Post*Euronext Segment 0.266*** 0.238*** 0.070** (3.56) (3.91) (2.20) Constant 4.574*** 3.687*** 1.308*** (8.70) (6.93) (3.08) Observations Adjusted R-squared The table includes results from the analysis using a propensity score matched sample of Euronext segment firms and non-euronext EU firms. Panel A includes results from the logistic models used to arrive at the PSM samples for the period pre- and post- the Euronext merger. In it, Lev equals the ratio of total liabilities to total assets, Profit is the ratio of net income to total assets, FExchange is the total number of foreign exchanges on which the company is listed, Logassets is the natural logarithm of total assets, Auditor is a dummy variable equal to one if the company is audited by a Big-5 auditor and zero otherwise, IFRS_USGAAP is a dummy variable equal to one if the company uses IFRS or US GAAP and zero otherwise. USCrosslisted is a dummy variable equal to 1 if the company is listed on the OTC market or the pink sheets, 2 if the company is listed on NYSE, NASDAQ or AMEX, and zero otherwise. Ret_std is the standard deviation of daily returns for company i in year t, per_zeroret is the percentage of zero-return days for company i in year t. MSCI is a dummy variable equal to one if the company is part of the MSCI index and zero otherwise, div_yield is the ratio of annual dividend to year-end share price, PERatio is the ratio of closing annual price to earnings per share. AF is the total number of analysts following company i in year t. StockIndex is the total number of indices of which company i is a member during a given year. Herfindahl is the sum of squares of ownership percentages for the three largest shareholders in a given company. Closelyheldsharespct is the percentage of closely held shares in a given company. Both logistic models also include fixed industry effects. (continues on next page) 59

61 Table 6 (continues from previous page) Panel B includes number of firm-years, means, medians, and standard deviations for the firm characteristics on which the companies are matched, separately pre- and post the Euronext merger. The comparison of means is based on a two-sided t-test with unequal differences. The comparison of medians is based on a Wilcoxon rank-sum test. Panel C includes results of the following logistic regressions: Log (1+ MF_for/dom) = α + β 1 Post + β 2 Euronext + β 3 Post*Euronext + ε in column (1) Log (1+ MF_eu/dom) = α + β 1 Post + β 2 Euronext + β 3 Post*Euronext + ε in column (2) Log (1+ MF_euronext/dom) = α + β 1 Post + β 2 Euronext + β 3 Post*Euronext + ε in column (3) The variables in the models and their sources are as defined in the appendix. In all three panels ***, **, and * indicate statistical significance at 0.01, 0.05, and 0.10 levels, respectively. 60

62 Table 7: PSM matching Euronext segment and Euronext non-segment firms Panel A: Logistic regression pre- and post-merger (1) (2) Pre-merger Post-merger Lev ** (2.26) (0.53) Profit * (1.34) (1.67) FExchange *** *** (4.23) (4.39) Logassets *** *** (6.37) (9.60) Auditor 0.799*** 0.513*** (5.90) (5.26) IFRS_USGAAP (0.45) (0.43) USCrosslisted (0.16) (0.07) ret_std *** (3.55) (1.60) perczeroret *** *** (11.21) (16.03) div_yield (0.56) (0.42) PERatio (0.47) (0.80) AF ** (2.54) (0.64) StockIndex * (0.61) (1.95) Herfindahl (0.81) (1.36) MSCI *** (2.85) Constant 2.999*** 2.173*** (4.14) (3.83) Observations 1,973 3,583 Pseudo R (continues on next page) 61

63 Table 7 (continues from previous page) Panel B: summary statistics between Euronext segment firms and matched Euronext non-segment firms pre- and post-merger Pre-merger Segment Non-segment N Mean Median StDev N Mean Median StDev Lev Profit FExchange Logassets Auditor IFRS_USGAAP USCrosslisted ret_std perc_zeroret MSCI div_yield PERatio AF StockIndex *** Herfindahl Closelyheldsharespct (continues on next page) 62

64 Panel B (continues from previous page) Table 7 Post-merger Segment Non-segment N Mean Median StDev N Mean Median StDev Lev Profit FExchange Logassets Auditor IFRS_USGAAP USCrosslisted ret_std * perc_zeroret MSCI div_yield PERatio AF *** StockIndex *** Herfindahl Closelyheldsharespct * * (continues on next page) 63

65 Table 7 (continues from previous page) Panel C: regression using the PSM sample: industry, exchange and year dummies are included in all regressions (1) (2) (3) (4) Log(1+MF_for/dom) Log(1+MF_eu/dom) Log(1+MF_euronext/dom) Log(1+MF_uk/dom) Post 0.240* ** (1.81) (1.38) (0.08) (2.45) Segment *** *** (3.47) (2.66) (0.87) (1.20) Post*Segment 0.238*** 0.163** ** (2.66) (2.35) (1.24) (2.02) Constant 1.510*** 0.876*** 0.250*** 0.753*** (11.92) (10.16) (6.51) (9.93) Observations 2,882 2,882 2,882 2,882 Adjusted R-squared The table includes results from the analysis using a propensity score matched sample of Euronext segment and non-segment firms. Panel A includes results from the logistic models used to arrive at the PSM samples for the period pre- and post the Euronext merger. In it, Lev equals the ratio of total liabilities to total assets, Profit is the ratio of net income to total assets, FExchange is the total number of foreign exchanges on which the company is listed, Logassets is the natural logarithm of total assets, Auditor is a dummy variable equal to one if the company is audited by a Big-5 auditor and zero otherwise, IFRS_USGAAP is a dummy variable equal to one if the company uses IFRS or US GAAP and zero otherwise. USCrosslisted is a dummy variable equal to 1 if the company is listed on the OTC market or the pink sheets, 2 if the company is listed on NYSE, NASDAQ or AMEX, and zero otherwise. Ret_std is the standard deviation of daily returns for company i in year t, per_zeroret is the percentage of zero-return days for company i in year t. MSCI is a dummy variable equal to one if the company is part of the MSCI index and zero otherwise, div_yield is the ratio of annual dividend to year-end share price, PERatio is the ratio of closing annual price to earnings per share. AF is the total number of analysts following company i in year t. StockIndex is the total number of indices of which company i is a member during a given year. Herfindahl is the sum of squares of ownership percentages for the three largest shareholders in a given company. Closelyheldsharespct is the percentage of closely held shares in a given company. The model also includes industry and exchange fixed effects. (continues on next page) 64

66 Table 7 (continues from previous page) Panel B includes number of firm-years, means, medians, and standard deviations for the firm characteristics on which the segment and nonsegment companies are matched, separately pre- and post the Euronext merger. The comparison of means is based on a two-sided t-test with unequal differences. The comparison of medians is based on a Wilcoxon rank-sum test. Panel C includes results of the following logistic regressions: Log (1+ MF_for/dom) =α + β 1 Post + β 2 Segment + β 3 Post*Segment + ε in column (1) Log (1+ MF_eu/dom) =α + β 1 Post + β 2 Segment + β 3 Post*Segment + ε in column (2) Log (1+ MF_euronext/dom) = α + β 1 Post + β 2 Segment + β 3 Post*Segment + ε in column (3) Log (1+ MF_uk/dom) =α + β 1 Post + β 2 Segment + β 3 Post*Segment + ε in column (4) The variables in the models and their sources are as defined in the appendix. In all three panels ***, **, and * indicate statistical significance at 0.01, 0.05, and 0.10 levels, respectively. 65

67 Table 8. Regression Results for the Euronext Segment Firms the Effect of Segment Compliance (1) Log (1+MF_for/dom) (2) Log (1+MF_for/dom) Compliance Score (0.36) Post 0.586** 0.440** (3.26) (2.73) Post*Compliance (0.76) Compliance (0.96) Post*Compliance * (2.05) Lev (1.72) (1.67) Profit (1.57) (1.54) FExchange 0.275*** 0.276*** (4.20) (4.03) Logassets 0.203*** 0.202*** (3.89) (3.64) Auditor (0.36) (0.39) IFRS_USGAAP 0.212* 0.206* (2.09) (2.07) USCrosslisted y (0.11) (0.14) ret_std *** *** (4.42) (4.58) perczeroret (0.65) (0.58) MSCI (0.11) (0.03) div_yield (0.13) (0.14) PERatio (0.35) (0.29) AF 0.024*** 0.025*** (4.68) (4.89) StockIndex * * (2.03) (2.03) Herfindahl ** ** (2.84) (2.83) Constant ** ** (3.09) (2.37) Observations Adjusted R-squared

68 Table 8 (continues from previous page) The table includes results from regression Log (1+ MF_for/dom) = α + β 1 Post + β 2 Compliance + β 4 Post*Compliance + β i Controls + ε Compliance (Compliance 4) represents the sum of five (four) dimensions of segment firms compliance to the enhanced financial reporting requirements: global auditors, IFRS, English reporting, functioning website, and quarterly reporting (global auditors, IFRS, English reporting, and functioning website). For each dimension of compliance requirements, we define an indicator variable equal to one if the segment firm s average compliance in the post-merger period is above the median average, and zero otherwise. We then add the five (four) indicators together to get the summary compliance score. Other variables in the models and their sources are as defined in the appendix. ***, **, and * indicate statistical significance at 0.01, 0.05, and 0.10 levels, respectively. 67