The introduction of International Financial Reporting Standards (IFRS) for listed companies in many countries around the world is one of the most significant regulatory changes in accounting history.1 Over 100 countries have recently moved to IFRS reporting or decided to require the use of these standards in the near future and even the U.S. Securities and Exchange Commission (SEC) is considering allowing U.S. firms to prepare their financial statements in accordance with IFRS (SEC ). Regulators expect that the use of IFRS enhances the comparability of financial statements, improves corporate transparency, increases the quality of financial reporting, and hence benefits investors (e.g., EC Regulation No. 1606/2002). From an economic perspective, there are reasons to be skeptical about these expectations and, in particular, the premise that simply mandating IFRS makes corporate reporting more informative or more comparable. Thus, the economic consequences of mandating IFRS reporting are not obvious.
In this paper, we provide early evidence on the capital-market effects around the introduction of mandatory IFRS reporting in 26 countries around the world. Using a treatment sample of over 3,100 firms that are mandated to adopt IFRS, we analyze effects in stock market liquidity, cost of equity capital, and firm value. These market-based constructs should reflect, among other things, changes in the quality of financial reporting and hence should also reflect improvements around the IFRS mandate. We employ four proxies for market liquidity, that is, the proportion of zero returns, the price impact of trades, total trading costs, and bid–ask spreads, four methods to compute the implied cost of equity capital, and use Tobin's q as a proxy for firms' equity valuations.
The primary challenge of our analysis is that the application of IFRS is mandated for all publicly traded firms in a given country from a certain date on. This makes it difficult to find a benchmark against which to evaluate any observed capital-market effects. Our empirical strategy uses three sets of tests to address this issue. First, using firm-year panel data from 2001 to 2005, we benchmark liquidity, cost of capital, and valuation effects around the introduction of IFRS against changes in other countries that do not yet mandate or allow IFRS reporting. We also include firms from IFRS adoption countries that do not yet report under IFRS at the end of our sample period because their fiscal year ends after December 2005, which, except for Singapore, is the date after which our sample firms must use IFRS. Both benchmarks help us to control for contemporaneous capital-market effects that are unrelated to the introduction of IFRS. In addition, we introduce firm-fixed effects to account for unobserved time-invariant firm characteristics.
Second, still using firm-year panel data, we examine whether the estimated capital-market effects exhibit plausible cross-sectional variation with respect to countries' institutional frameworks. As the regulatory change forces many firms to adopt IFRS that would not have done so otherwise, we expect mandatory IFRS reporting to have a smaller effect or no impact in countries with weak legal and enforcement regimes or where firms have poor reporting incentives to begin with. Moreover, assuming that mandatory IFRS reporting is properly enforced, the impact is likely to be smaller in countries that already have high reporting quality or where local generally accepted accounting principles (GAAP) and IFRS are fairly close (e.g., due to a prior convergence strategy).
Third, we exploit that firms begin applying IFRS at different points in time depending on their fiscal year-ends and that, as a result, the adoption pattern in a given country is largely exogenous once the initial date for IFRS adoption is set.2 We relate this pattern to changes in aggregate liquidity in a given country and month. If the introduction of IFRS reporting has indeed discernable effects, we expect changes in aggregate liquidity to be most pronounced in months when many firms report under IFRS for the first time. That is, changes in liquidity should mirror countries' stepwise transition towards the new reporting regime and not simply reflect a time trend or a one-time shock. As this approach has fewer data restrictions, we analyze liquidity effects for 6,500 mandatory adopters, that is, firms that report under IFRS for the first time when it becomes mandatory.
We begin our first set of analyses with a simple difference-in-differences analysis and find that mandatory adopters exhibit a significantly larger increase in market liquidity than a random sample of nonadopting benchmark firms from around the world. In contrast, the changes in Tobin's q for mandatory adopters are insignificant and their cost of capital even increases relative to benchmark firms. While the latter findings may be surprising, they do not yet account for the possibility that markets likely price the IFRS mandate ahead of the actual adoption date.
Next, we run firm-level panel regressions that control for time-varying firm characteristics, marketwide changes in the dependent variable, industry-year-fixed, and firm-fixed effects. We find that market liquidity increases for firms that adopt IFRS reporting when it becomes mandatory. In our main specification, the percentage of days without trades declines by 100 basis points for mandatory adopters, which is close to a 4% liquidity improvement relative to the median level prior to IFRS adoption. Total trading costs and the percentage bid–ask spreads both decline by 12 basis points, indicating liquidity increases of 3% and 6%, respectively, relative to the median level prior to IFRS adoption. The results for price impact are insignificant in the main specification. For parsimony and to reduce measurement error, we aggregate all four liquidity proxies into a single liquidity factor and again find a statistically significant increase in liquidity for mandatory IFRS adopters. We also vary the composition of the benchmark sample using the complete Worldscope population or U.S. firms only. While these variations do not change the tenor of the results, they indicate that benchmarking and the specific choice of the benchmark are important in evaluating the liquidity effects around the IFRS mandate.
The cost of capital and Tobin's q results are mixed. Our base specification indicates an increase in the cost of capital and a decrease of Tobin's q in the year when IFRS reporting becomes mandatory, similar to the difference-in-differences analysis. It is possible, though, that these results stem from transition effects, such as temporary difficulties in forecasting earnings under the new accounting regime, which can affect the implied cost of capital, or changes in the measurement of total assets, which can affect Tobin's q. Another explanation is that markets anticipate the effects of the IFRS mandate, in which case including observations of switching firms before the introduction of IFRS (as our panel approach does) likely works against finding a decrease (increase) in the cost of capital (Tobin's q). Consistent with the existence of anticipation effects, we find that the cost of capital decreases by 26 basis points and Tobin's q increases by 7% when we measure the effect one year before the mandatory adoption date.
While the liquidity and the (anticipation-adjusted) cost of capital and valuation effects for mandatory adopters are economically significant, they are generally smaller than the corresponding capital-market effects of voluntary adopters. That is, the latter group exhibits significant liquidity, valuation, and cost of capital effects around the introduction of mandatory IFRS reporting, despite the fact that these firms have already switched to IFRS prior to the mandate.3 There are several ways to interpret this finding. First, it could reflect comparability benefits that accrue to the voluntary adopters when the other firms in the country have to switch to IFRS. We conduct some tests on the role of comparability effects, but are unable to provide statistical support for this argument. Second, the capital-market effects for voluntary adopters could stem from concurrent changes in the enforcement and governance regimes that (some) countries introduce together with the IFRS mandate. Such changes should affect mandatory and voluntary adopters in a given country and, hence, could explain the capital-market effects. Our cross-sectional results, which we discuss next, are consistent with this interpretation. Finally, as the capital-market effects are particularly pronounced for early voluntary adopters, it is also possible that the mandate increases the commitment associated with IFRS reporting as it eliminates dual reporting practices and the option to reverse back to local GAAP.
Our second set of empirical tests, the cross-sectional analyses, show that the capital-market effects around the introduction of mandatory IFRS reporting are not evenly distributed across countries and firms. We find that the capital-markets effects around mandatory IFRS adoption occur only in countries with relatively strict enforcement regimes and in countries where the institutional environment provides strong incentives to firms to be transparent. These findings are consistent with the view that IFRS implementation is likely to be heterogeneous across countries (e.g., Ball ), and with the idea that firms' reporting incentives, which are shaped by markets and countries' institutional environments, play a crucial role for reporting outcomes (e.g., Ball, Robin, and Wu , Ball and Shivakumar , Burgstahler, Hail, and Leuz ). We also find that the effects for mandatory adopters are smaller in countries that have fewer differences between local GAAP and IFRS and a pre-existing convergence strategy towards IFRS. As expected under the reporting incentives view, these effects are largest for countries with large GAAP differences that also have strong legal regimes. Finally, capital-market effects are stronger in member states of the European Union (EU), possibly reflecting its concurrent efforts to improve governance and enforcement (Hail and Leuz ).
In our last set of analyses, we examine monthly changes in aggregate liquidity as IFRS reporting becomes more widespread, controlling for contemporaneous changes in world market liquidity averaged over 100 random samples, changes in liquidity for the same calendar month in the prior year, lagged levels in liquidity, volatility, market capitalization, and country-fixed effects. We show that increases in IFRS reporting by mandatory adopters are associated with decreases in the percentage of zero returns, in bid–ask spreads and, to a lesser extent, in the price impact of trades. These findings confirm our firm-year analyses but are considerably smaller in magnitude. As the country-month analysis is likely the cleanest test in terms of separating the consequences of the IFRS mandate from other factors (e.g., time trends, unrelated institutional changes), the smaller magnitude of the effects provides further evidence that the documented liquidity improvements in the firm-year analysis cannot be attributed entirely to the IFRS mandate.4
Despite the consistency of our findings across various analyses, we caution the reader to interpret this study carefully. First, as several countries around the world substantially revise their enforcement, auditing, and governance regimes to support the introduction of IFRS reporting, it is likely that our results reflect the joint effects of these efforts and hence cannot solely, or even primarily, be attributed to the switch to IFRS. Second, our analyses are based on a relatively short time period and it is possible that the documented effects are short-lived. But the effects could also increase over time as market participants gain more experience with IFRS or as recent changes to countries' enforcement and governance regimes take further hold. Third, our valuation and cost of capital proxies may exhibit substantial measurement error and, in particular, may be affected by the change in accounting measurement per se, which in turn could bias the magnitude of the estimated effects. For instance, taken at face value, our estimates of the valuation effects seem too large to be solely attributable to the IFRS mandate. Finally, while we attempt to account for anticipation and early pricing of the IFRS mandate as well as first-time IFRS interim reporting, these effects and transitional procedures (see IFRS 1) likely reduce the power of our tests.
With these caveats in mind, our study makes several contributions to the literature. This study is the first to analyze the capital-market effects around the introduction of mandatory IFRS reporting for a large and global sample of firms. The move to mandatory IFRS reporting around the world is one of the most important policy issues in financial accounting. Hence, the findings should be of substantial interest to regulators and policy makers in many countries, including those that have not yet made the decision to move towards IFRS. Our study is also novel in that it examines the economic consequences of a mandatory change of an entire set of accounting standards as well as the heterogeneity in these capital-market effects across many countries and industries. Prior studies either perform analyses across countries with different accounting standards or are based on voluntary adoptions of new accounting standards. Finally, our study illustrates a novel empirical strategy to identify the effects of mandatory accounting regime changes. We exploit that IFRS reporting is phased in based on firms' fiscal year-ends. Thus, our country-month analysis should allow us to disentangle time trends, one-time shocks, and reporting-related effects.
The remainder of the paper is organized as follows. Section 2 develops our hypotheses and reviews the literature. Section 3 delineates our research design for the firm-year analyses and presents results for the average capital-market effects around the switch to IFRS reporting. In section 4, we examine the heterogeneity in the capital-market effects across countries and industries. Section 5 presents the country-month analyses, and section 6 concludes. In the appendix, we provide additional details on the construction of our key variables.
2. Conceptual Underpinnings and Literature Review
2.1 hypothesis development
In this section, we discuss several hypotheses about the effects of introducing IFRS reporting around the world. There are arguments suggesting significant capital-market effects (in either direction) around the adoption of mandatory IFRS reporting as well as arguments that point towards small or negligible effects. As all of these views have merit, the capital-market effects of mandatory IFRS reporting are ultimately an empirical question.
Arguments suggesting that the adoption of mandatory IFRS reporting yields significant capital-market benefits often start from the premise that IFRS reporting increases transparency and improves the quality of financial reporting (e.g., EC Regulation No. 1606/2002), citing that IFRS are more capital-market oriented and more comprehensive, especially with respect to disclosures, than most local GAAP.5 To the extent that this premise is correct, prior analytical and empirical studies suggest that the introduction of mandatory IFRS reporting should be associated with an increase in market liquidity as well as a decline in firms' cost of capital. That is, higher quality financial reporting and better disclosure should reduce adverse selection problems in share markets and lower estimation risk (e.g., Verrecchia , Lambert, Leuz, and Verrecchia ). Welker , Healy, Hutton, and Palepu  and Leuz and Verrecchia  provide evidence that information asymmetry and liquidity proxies are indeed associated with firms' disclosure and accounting policies. Similarly, Botosan , Botosan and Plumlee , Hail , Francis et al. , and Hail and Leuz  show that more extensive financial disclosures and higher quality reporting are negatively related to firms' (implied) cost of equity capital. Based on this prior work, we expect that market liquidity, cost of capital, and firm value reflect, among other things, firms' reporting quality. Thus, we can use these proxies to evaluate mandatory reporting changes, such as the imposition of IFRS.
A related argument in favor of positive capital-market effects is that IFRS reduce the amount of reporting discretion relative to many local GAAP and, in particular, compel firms towards the bottom of the quality spectrum to improve their financial reporting. Consistent with this argument, Ewert and Wagenhofer  show that tightening the accounting standards can reduce the level of earnings management and improve reporting quality.6
Another argument is that IFRS reporting makes it less costly for investors to compare firms across markets and countries (e.g., Armstrong et al. , Covrig, DeFond, and Hung ). Thus, even if the quality of corporate reporting per se does not improve, it is possible that the financial information provided becomes more useful to investors. For instance, a common set of accounting standards could help investors to differentiate between lower and higher quality firms, which in turn would reduce information asymmetries among investors and/or lower estimation risk. Moreover, if IFRS reporting improves comparisons across firms and reduces estimation risk, the switch to IFRS creates positive externalities on other firms, and mandating IFRS would be one way to capture them (e.g., Coffee , Dye , Lambert, Leuz, and Verrecchia ). Similarly, accounting diversity could be an impediment to cross-border investment (Bradshaw, Bushee, and Miller ). The global movement towards IFRS reporting may facilitate cross-border investment and the integration of capital markets (Covrig, DeFond, and Hung ). Making it easier for foreigners to invest in a country's firms could again improve the liquidity of the capital markets and enlarge firms' investor base, which in turn improves risk sharing and lowers cost of capital (e.g., Merton ).
However, there are also arguments suggesting that the capital-market effects of IFRS adoption could be small or even negligible. In particular, there are reasons to be skeptical about the premise that mandating the use of IFRS alone makes corporate reporting more informative or more comparable. The evidence in several recent studies points to a limited role of accounting standards in determining observed reporting quality and, in contrast, highlights the importance of firms' reporting incentives (e.g., Ball, Kothari, and Robin , Ball, Robin, and Wu , Leuz , Ball and Shivakumar , Burgstahler, Hail, and Leuz ). The argument behind this evidence is that the application of accounting standards involves considerable judgment and the use of private information. As a result, IFRS (like any other set of accounting standards) provide firms with substantial discretion. How firms use this discretion is likely to depend on their reporting incentives, which are shaped by many factors, including countries' legal institutions, various market forces, and firms' operating characteristics.
Thus, the reporting incentives argument questions that changing the standards alone makes the reported numbers more comparable across firms or improves firms' reporting behavior. Firms that oppose the switch to IFRS or towards more transparency are unlikely to make material changes to their reporting policies (e.g., Ball , Daske et al. ). This concern applies not only to recognition and valuation rules, where firms are known to have substantial discretion, but also to footnote disclosures, which firms can make in a more or less informative manner.7 Thus, even if the standards themselves mandate superior accounting practices and require more disclosures, it is not clear whether firms implement these standards in ways that make the reported numbers indeed more informative.8 We note that this is not just a matter of proper enforcement. Even with perfect enforcement, observed reporting behavior is expected to differ across firms as long as accounting standards offer some discretion and firms have different reporting incentives (Leuz ).
That said, enforcement is an important issue for our study because many countries revise and strengthen their enforcement regimes along with the introduction of mandatory IFRS reporting. For instance, the EU made several such efforts in recent years. In 2003, the Committee of European Securities Regulators (CESR) released its Standard No. 1. While it is nonbinding, it is aimed at developing and implementing a common approach to the enforcement of IFRS throughout the EU. Among other things, the standard stipulates that all listed companies are subject to a financial information review and calls for the creation of an independent administrative authority for compliance and enforcement in each member state. In 2004, the EU passed the Transparency Directive, which builds expressively on regulation mandating IFRS reporting and establishes rules for periodic financial reports and other continuing reporting obligations. For instance, it mandates that financial reports include a responsibility statement by corporate insiders and stipulates the creation of enforcement authorities that assume responsibility for IFRS compliance. EU member states had to implement the directive by January 2007. In addition, IFRS implementation and enforcement received widespread attention from auditors and the press,9 and in many countries, the importance of public capital markets increased substantially in recent years.
Such institutional changes can alter firms' reporting incentives and hence lead to higher quality reporting. If these changes take place around the introduction of mandatory IFRS reporting and significantly tighten the enforcement regime compared to one in place under local GAAP reporting, the capital-market effects around IFRS adoption are likely the joint outcome of concurrent reporting and enforcement changes. Similar arguments can be made for recent governance and auditing reforms in many countries (e.g., Enriques and Volpin , Quick, Turley, and Willekens ).
The reporting incentives view predicts that countries' institutional structures and changes therein play an important role in explaining capital-market effects around IFRS adoption. All else equal, countries with stricter enforcement regimes and institutional structures that provide strong reporting incentives are more likely to exhibit discernable capital-market effects around the introduction of IFRS reporting. In these countries, mandatory adopters are less likely to get away with adopting IFRS merely as a label, that is, without materially changing their reporting practices.10 Conversely, one could argue that countries with better reporting practices before the introduction of IFRS should experience smaller capital-market effects.11 This argument, however, rests on the presumption that changing the accounting standards alone improves firms' reporting practices and ignores institutional reasons why firms in these countries have better reporting quality to begin with.
For similar reasons, we expect capital-market effects to be different across voluntary and mandatory adopters. The latter group is essentially forced to adopt IFRS and hence should respond less to the treatment. The former set of firms is more likely to make significant changes to their reporting practices. Some of them may adopt IFRS as part of a broader strategy that increases their commitment to transparency; for example, they may hire higher quality auditors, improve corporate governance, change ownership structures, or seek cross-listings in stricter regimes, along with IFRS adoption. As a result, the capital-market effects around voluntary adoptions are likely to be larger but they cannot be attributed to IFRS alone. That is, the effects might reflect differences in the incentives for credible reporting, the circumstances that lead to IFRS adoption in the first place, and a firm's entire commitment strategy (e.g., Leuz and Verrecchia , Daske et al. ).
At the same time, firms that already voluntarily switch to IFRS prior to the mandate should not exhibit significant capital-market effects when IFRS reporting becomes mandatory unless the latter compels them to increase their commitment to transparency or the mandate creates positive externalities. For example, it is possible that these firms benefit from greater comparability as all the other firms in the country or industry switch to IFRS. Another possibility is that capital-market effects for voluntary adopters around the time IFRS becomes mandatory reflect the concurrent changes in countries' institutional environments, such as improvements in enforcement and governance.
2.2 related empirical studies
In this section, we briefly summarize evidence on the capital-market effects of IFRS adoption. We focus primarily on studies examining the introduction of mandatory IFRS reporting. Studies examining voluntary adoptions do not speak directly to the costs and benefits of an IFRS reporting requirement.12 Studies on the introduction of mandatory IFRS reporting can be classified into two categories: (1) studies that examine the stock market reactions to key events associated with the EU's movement towards mandatory IFRS reporting and (2) a few recent studies that analyze the effects of mandatory IFRS adoption in certain countries based on IFRS financial statements.13 Overall, evidence on the consequences of mandatory IFRS reporting is still in its infancy.
Studies in the first category try to infer whether the adoption of IFRS in the EU has net benefits (or costs) to firms from their stock market reactions to key events that made IFRS reporting more or less likely. The evidence from these papers is mixed. Comprix, Muller, and Standford-Harris  examine abnormal returns of EU firms on four “core” event dates in 2000 that increase the likelihood of mandatory IFRS reporting. They find a weakly significant, but negative, market reaction to the four event dates. However, firms that are audited by a Big 5 auditor, located in countries that are expected to have greater improvements in reporting quality due to IFRS adoption, or subject to higher legal enforcement experience significantly positive returns on some of the event dates they examine. Armstrong et al.  examine the reactions to 16 events between 2002 and 2005 associated with the adoption of IFRS in the EU. They find a positive (negative) reaction to events that increase (decrease) the likelihood of IFRS adoption. They also document that the reaction is more positive for firms from lower quality information environments, with higher preadoption information asymmetry, and firms that are domiciled in common law countries. Christensen, Lee, and Walker [2007a] analyze the market reactions of U.K. firms to announcements of mandatory IFRS reporting in the EU and find that the average U.K. market reaction is small. Using the degree of similarity with German voluntary IFRS and U.S. GAAP adopters as a proxy for U.K. firms' willingness to adopt IFRS, they find that this proxy is positively (negatively) related to the stock price reaction to news events increasing (decreasing) the likelihood of mandatory IFRS reporting. They find a similar association for changes in the implied cost of equity capital.
Studies in the second category analyze the effects of mandated IFRS using data from the annual reports released under the new regime. These studies are closest in spirit to our firm-year analyses. However, they are limited to particular countries and often quite different in their research focus or design. Platikanova  analyzes measures of liquidity and information asymmetry in four European countries. She finds heterogeneous liquidity changes for these countries but documents that the liquidity differences across countries become smaller after the adoption of IFRS. Demaria and Dufour  and Capkun et al.  examine transitional effects and changes in accounting numbers (or ratios) when firms switch from local GAAP to IFRS. Christensen, Lee, and Walker [2007b] analyze whether IFRS/UK GAAP reconciliations around the IFRS introduction convey new information to the markets, and find that reconciliations that are released early do so. Capkun et al.  find that earnings reconciliations of EU firms in the transition year are value relevant.
Finally, several reports examine the implementation and compliance of IFRS in the first year under the new mandate. While a study conducted by the Institute of Chartered Accountants in England and Wales (ICAEW ) on behalf of the European Commission suggests that publicly traded firms in the EU generally comply with IFRS, similar studies by KPMG  and Ernst & Young  conclude that, despite substantial convergence, IFRS financial statements retain a strong national identity, which is consistent with the reporting incentives view.14
3. Firm-Year Analyses of the Capital-Market Effects around the IFRS Mandate
3.1 research design
To conduct our first set of empirical tests, the firm-year panel regressions, we proceed in four steps. First, we define the key variables of interest and divide all IFRS adopters into three categories: mandatory, early voluntary, and late voluntary adopters. We create a binary indicator variable, First-Time Mandatory, that takes on the value of one for fiscal years ending on or after the local IFRS adoption date (in most cases December 31, 2005) for firms that do not report under IFRS until it becomes mandatory. This variable should capture the average capital-market effects around the IFRS mandate for firms that are essentially forced to adopt IFRS. It is the main variable of interest. We introduce separate indicator variables for firm-year observations from firms that report under IFRS ahead of the rule change. We distinguish between Early Voluntary and Late Voluntary adopters, respectively, depending on whether firms switch to IFRS before their home country announces plans to require IFRS reporting or after this announcement, but before IFRS reporting becomes compulsory (see Table 6, panel A, for IFRS announcement and adoption dates).15 We also define two interaction terms, Early Voluntary * Mandatory and Late Voluntary * Mandatory, marking all fiscal years ending on or after the mandated IFRS adoption date for the two respective groups. These terms capture any incremental (period-specific) capital-market effects for early and late voluntary adopters once IFRS reporting is required for all firms in the economy.
Institutional Characteristics of IFRS Adoption Countries and Industries
|Australia||07/04/2002||12/31/2005|| 1.7||(1)||0|| 5.9||(1)||−0.4||(0)||1|
|Austria||06/04/2002||12/31/2005|| 1.8||(1)||1|| −4.9||(0)|| 2.5||(1)||0|
|Belgium||06/04/2002||12/31/2005|| 1.4||(0)||1|| −1.3||(0)|| 1.4||(1)||0|
|Czech Republic||06/04/2002||12/31/2005|| 0.7||(0)||1||n.a.||n.a. || 0.6||(1)||0|
|Denmark||06/04/2002||12/31/2005|| 1.9||(1)||1|| −6.0||(0)|| 0.1||(0)||0|
|Finland||06/04/2002||12/31/2005|| 1.9||(1)||1|| −1.7||(0)|| 4.4||(1)||0|
|France||06/04/2002||12/31/2005|| 1.3||(0)||1|| 4.7||(1)|| 0.4||(1)||0|
|Germany||06/04/2002||12/31/2005|| 1.7||(1)||1|| 1.9||(1)|| 1.5||(1)||0|
|Greece||06/04/2002||12/31/2005|| 0.7||(0)||1|| −9.2||(0)|| 6.1||(1)||0|
|Hong Kong||09/10/2004||12/31/2005|| 1.5||(0)||0|| −8.9||(0)||−1.5||(0)||1|
|Hungary||06/04/2002||12/31/2005|| 0.7||(0)||1||n.a.||n.a. ||−0.3||(0)||0|
|Ireland||06/04/2002||12/31/2005|| 1.6||(1)||1|| 5.7||(1)||−3.3||(0)||0|
|Italy||06/04/2002||12/31/2005|| 0.5||(0)||1|| −6.2||(0)|| 0.7||(1)||0|
|Luxembourg||06/04/2002||12/31/2005|| 1.9||(1)||1||n.a.||n.a. || 6.0||(1)||0|
|The Netherlands||06/04/2002||12/31/2005|| 1.7||(1)||1|| 1.7||(1)||−7.6||(0)||0|
|Norway||06/04/2002||12/31/2005|| 1.9||(1)||0|| 4.2||(1)||−3.8||(0)||0|
|Philippines||10/02/2003||12/31/2005||−0.4||(0)||0|| 12.7||(1)|| 1.1||(1)||1|
|Poland||06/04/2002||12/31/2005|| 0.3||(0)||1||n.a.||n.a. ||−0.9||(0)||0|
|Portugal||06/04/2002||12/31/2005|| 1.1||(0)||1|| −6.0||(0)|| 2.2||(1)||0|
|South Africa||05/20/2003||12/31/2005|| 0.2||(0)||0|| 6.7||(1)||−3.1||(0)||1|
|Spain||06/04/2002||12/31/2005|| 1.1||(0)||1|| 0.2||(0)|| 4.9||(1)||0|
|Sweden||06/04/2002||12/31/2005|| 1.8||(1)||1|| 3.4||(1)||−0.7||(0)||0|
|Switzerland||11/11/2002||12/31/2005|| 2.0||(1)||0|| 1.0||(1)|| 2.2||(1)||0|
|United Kingdom||06/04/2002||12/31/2005|| 1.6||(1)||1|| 3.7||(1)||−3.4||(0)||0|
|01 Petroleum industry|| 8.4||(0)|| 07 Capital goods industry|| 18.3||(1)|
|02 Finance/Real estate industry|| 7.5||(0)|| 08 Transportation industry|| 9.6||(0)|
|03 Consumer durables industry|| 12.6||(1)|| 09 Utilities industry|| 15.6||(1)|
|04 Basic industry|| 10.8||(1)|| 10 Textiles/Trade industry|| 10.4||(0)|
|05 Food/Tobacco industry|| 9.0||(0)|| 11 Service industry|| 10.7||(1)|
|06 Construction industry|| 11.7||(1)|| 12 Leisure industry|| 10.2||(0)|
The second step is to choose dependent variables that capture the potential capital-market effects of the IFRS mandate described in section 2. We use four proxies for market liquidity. Zero Returns is the proportion of trading days with zero daily stock returns out of all potential trading days in a given year. Price Impact is the yearly median of the Amihud  illiquidity measure (i.e., daily absolute stock return divided by US$ trading volume). Total Trading Costs are an estimate of total round trip transaction costs (including bid–ask spreads, commissions, as well as implicit costs from short-sale constraints or taxes) based on a yearly time-series regression of daily stock returns on the aggregate market returns (Lesmond, Ogden, and Trzcinka ). Bid-Ask Spread is the yearly median of daily quoted spreads, measured at the end of each trading day as the difference between the bid and ask price divided by the midpoint. For parsimony, we also aggregate the four liquidity proxies into a single Liquidity Factor employing factor analysis with one oblique rotation, and use it as a dependent variable in the analyses.
In addition, we examine effects on firms' cost of equity capital and equity valuations. We use four different accounting-based valuation models to obtain estimates of the cost of capital implied by the mean I/B/E/S analyst consensus forecasts and stock prices. Following Hail and Leuz (2006, 2008), Cost of Capital is the average of these four estimates. We employ Tobin's q as a proxy for firms' equity valuations and measure it as the market-to-book ratio of the total assets. The appendix describes the theoretical concepts behind our proxies, the data sources, and their empirical measurement in more detail.
We note that all capital-market proxies are related in the sense that liquidity effects could manifest in firms' cost of capital and that decreases in the cost of capital should increase Tobin's q. In addition, Tobin's q could capture effects beyond market liquidity and the cost of capital, for example, real effects on investment or growth from better financial reporting and the costs of IFRS implementation. We also note that cost of capital estimates and Tobin's q are likely to capture capital-market effects of mandatory IFRS reporting early, even before the actual adoption of the standards, because these proxies reflect investors' expectations about the future and, hence, beyond IFRS adoption.16 In addition, both proxies can be affected by the change in the accounting rules. Implied cost of capital estimates could suffer because financial analysts have temporary difficulties forecasting earnings under the new regime and Tobin's q is likely affected by changes in the measurement of total assets due to the new accounting regime.17
The third step is to control for general trends and changes in market liquidity, cost of capital, or firm value that are unrelated to IFRS reporting. To do so, we include a sample of local GAAP benchmark firms from countries that either preclude or do not mandate the use of IFRS. We also include firms that do not yet have to report under IFRS due to their fiscal year-ends, but are from countries that require IFRS reporting. The latter firms are presumably subject to similar economic shocks as switching firms from the same country, which should help us control for contemporaneous effects that are unrelated to the introduction of IFRS. Moreover, we include industry-year-fixed effects, that is, an indicator variable for each year and industry (using the Campbell  industry classification) to capture common effects on our dependent variables in a particular year and industry. Finally, we include a contemporaneously measured Market Benchmark, computed as the yearly mean of the dependent variable from observations in countries that do not mandate IFRS reporting.
The fourth step is to include control variables for firm characteristics. In addition to industry-year-fixed effects and the market benchmark mentioned above, our regression models include firm-fixed effects to control for unobserved time-invariant firm characteristics as well as binary indicator variables to control for U.S. GAAP reporting, U.S. cross-listing, trading on a “new market,” and being a member of a major stock index. In the liquidity regressions, we control for firm size, share turnover, and return variability (Chordia, Roll, and Subrahmanyam , Leuz and Verrecchia ).18 For the cost-of-capital specifications, we follow Hail and Leuz  and control for firm size, financial leverage, the risk-free rate, return variability, and forecast bias. The Tobin's q regressions include firm size, financial leverage, asset growth, and the average industry q (e.g., Doidge, Karolyi, and Stulz , Lang, Lins, and Miller ). All control variables are defined as stated in Table 1 (indicator variables) and Table 2 (continuous variables).
Sample Composition by Country and Year
|2001|| 21,050|| 561||2.7|| 2||0.0|| 0|| 0.0|| 228||1.1|| 814||3.9|| 615||2.9|| 7,261||34.5|
|2002|| 21,823|| 532||2.4|| 67||0.3|| 0|| 0.0|| 239||1.1|| 832||3.8|| 697||3.2|| 7,651||35.1|
|2003|| 21,841|| 476||2.2||102||0.5|| 225|| 1.0|| 237||1.1|| 829||3.8|| 690||3.2|| 7,656||35.1|
|2004|| 21,087|| 456||2.2||185||0.9|| 368|| 1.7|| 215||1.0|| 783||3.7|| 685||3.2|| 7,488||35.5|
|2005|| 19,726|| 467||2.4||173||0.9||3,148||16.0|| 134||0.7|| 728||3.7|| 675||3.4|| 7,137||36.2|
Descriptive Statistics for Variables Used in Regression Analyses
|Total Trading Costs|| 94,759||6.5%||7.3%||0.5%||2.1%||3.7%||7.7%||37.4%|
|Bid-Ask Spread|| 65,296||3.3%||4.7%||0.1%||0.7%||1.5%||3.8%||24.0%|
|Cost of Capital|| 24,913||10.2%||3.4%||5.1%||7.9%||9.4%||11.7%||21.9%|
|Tobin's q|| 97,293||1.438||0.957||0.526||0.923||1.116||1.580||5.657|
|Market Value||105,527||601.1||1,704.1 ||0.8||20.9||78.7|| 338.2||9,813.9|
|Total Assets||104,844||1,273.5 ||4,102.0 ||0.4||43.8||163.7|| 626.7||22,195.7|
|Financial Leverage|| 99,823||0.504||0.252||0.014||0.310||0.510||0.691||0.965|
|Forecast Bias|| 44,097||0.009||0.042||-0.076||−0.004||0.000||0.011||0.206|
We combine the variables into the following regression model estimated at the firm-year level:
where EconCon stands for the liquidity, cost of capital, and Tobin's q proxies and Controlsj denotes our set of control variables including the various fixed effects. To estimate this model, we obtain financial data from Worldscope, price and trading volume data from Datastream, and analyst forecasts and share price data for the cost of capital estimation from I/B/E/S.
3.2 sample description
The sample used in the firm-year analyses covers all firms with fiscal years ending on or after January 1, 2001, through December 31, 2005. We start in 2001 to ensure that the sample period before the IFRS mandate is sufficiently long, so that we can also check for the potential anticipation of the pending rule change.19 Due to data availability at the time of our analysis, our sample ends in 2005. Thus, for the majority of countries, the mandatory IFRS observations in our sample stem exclusively from December fiscal year-end firms. Firms that have to adopt IFRS for the first time in fiscal years ending after 2005 are included as control firms, that is, coded as local GAAP firms in 2005. As the firm-year analysis does not cover all firms in a country that have to adopt IFRS for the first time, it should be viewed as providing early evidence on the economic consequences of the IFRS mandate. We overcome this data limitation in the country-month analysis where we cover all mandatory adopters and analyze the effects over the entire initial adoption year.
We begin the sample collection procedure with all firms from countries that require IFRS reporting and for which we have the necessary data to compute the variables used in the firm-year regressions described above. This yields a maximum treatment sample of about 35,000 firm-years from 9,000 unique firms, of which more than 3,100 must adopt IFRS for the first time. Table 1, panel A, provides a break-down of the number of observations, the accounting standards followed, listing status, and stock index membership for the IFRS adopting countries in our sample.20 About 11% of the firm-year observations stem from mandatory IFRS adopters. The group of voluntary adopters is smaller, comprising only 7% (early voluntary) or 2% (late voluntary) of the treatment sample, and the adoption rates vary substantially across countries.
Next, we augment the treatment sample with local GAAP firms from countries that do not require mandatory IFRS reporting. Panel B of Table 1 presents descriptive information on this benchmark sample representing about 17,000 unique firms and 71,000 firm-year observations from 25 countries. To the extent that there are voluntary IFRS adopters in these countries, we exclude them from the sample. We use three different benchmarks: (1) a maximum of 150 randomly selected firms per country (as indicated in column four of the panel), (2) U.S. firms only, and (3) the entire worldwide benchmark sample. While the United States or the Worldscope universe are natural benchmarks, we primarily report results using the random sample as this approach reduces the potentially undue weight of observations from a small set of countries (e.g., Japan) and diversifies contemporaneous changes in benchmark countries. For instance, it is possible that benchmark countries have regulatory changes themselves that coincide with IFRS adoption in treatment countries. Such a concern exists with regard to the United States and the implementation of the Sarbanes-Oxley Act of 2002.
Panel C of Table 1 reports the sample composition by year. Out of the 19,726 observations in 2005, the year IFRS reporting becomes mandatory in all treatment sample countries but Singapore, 2.4% are from early voluntary adopters, 0.9% from late voluntary adopters, and 16.0% from firms that are forced to adopt IFRS for the first time. The remaining firms are from our benchmark sample.
Table 2, panel A, presents descriptive statistics on the dependent variables used in the firm-year analyses. For the average sample firm, 29.2% of daily stock returns are equal to zero, indicating days with no trades or no changes in closing prices. The mean (median) price impact metric is 4.84 (0.22) suggesting that, on average, a US$ 1,000 trade moves stock price by 0.48% (0.02%). The difference between the mean and median illustrates that this variable is highly skewed. The mean total trading costs amount to 6.5% of price, while the mean bid–ask spread equals 3.3%. The mean cost of capital is 10.2% while the mean Tobin's q is 1.4. All these values are in plausible ranges. Panel B reports descriptive statistics on the continuous independent variables. Except for variables with natural lower and upper bounds, we truncate all variables at the first and 99th percentile.
3.3 empirical results
3.3.1. Difference-in-Differences Analyses. We begin our analysis with univariate comparisons of the liquidity, cost of capital, and valuation effects around the introduction of mandatory IFRS reporting using a difference-in-differences design. This is a simple way to account for unobserved differences between treatment and control firms and to adjust observed changes for the treatment firms by concurrent changes that are also experienced by the control firms. Specifically, we compute the difference in our outcome variables between IFRS adopters and nonadopting, local GAAP benchmark firms in the year before and in the year when mandatory IFRS reporting begins and then compare the relative change over time.21 To eliminate the impact of sample composition, we require each firm to have data in both years.
Table 3 reports mean values of the dependent variables across mandatory IFRS adopters and a benchmark sample of non-IFRS adopters for the fiscal years 2004 and 2005. The four liquidity proxies provide a consistent picture. Liquidity is higher in IFRS adoption countries to begin with, but this gap increases in the year of the mandatory change. For instance, based on a sample of 2,696 mandatory IFRS adopters, the mean proportion of zero return days in the preadoption period is 31.2% and decreases to 27.7% in 2005. Over the same period, the 3,987 benchmark firms also experience a decline in zero return days from 35.2% to 33.8%. However, the decrease in zero-return days is significantly larger (by 2.1%) for the IFRS adopters than for the benchmark firms, using t-tests that compare means of yearly firm-level changes across the two groups.
Difference-in-Differences Analysis of the Capital-Market Effects around the IFRS Mandate
The cost of capital increases for IFRS adopters relative to the benchmark firms around the introduction of the mandate (by 35 basis points) and this difference is statistically significant. Tobin's q slightly increases for both mandatory IFRS adopters and benchmark firms, but the difference in the differences is not statistically significant. Thus, the cost of capital and Tobin's q results do not mirror the liquidity analyses, that is, do not produce evidence of significant capital-market benefits. One potential explanation is that both the cost of capital and equity valuations are more susceptible to anticipation effects. We investigate this issue in our regression analyses below.
3.3.2. Analyses of Liquidity Effects. In Table 4, we report regression results. Throughout the firm-year analyses we tabulate ordinary least squares (OLS) coefficient estimates and, in parentheses, t-statistics based on robust standard errors that are clustered by firm.22 Panel A presents results for the four liquidity measures as well as the liquidity factor using the random sample of local GAAP firms from nonadopting countries as a benchmark. Except for the positive but insignificant coefficients on First-Time Mandatory in the price impact regression and on Late Voluntary * Mandatory in the bid–ask spread regression, the coefficients of primary interest (highlighted in bold) are all negative and, in most cases, significant.23 These results indicate that all firms reporting under IFRS experience a significant increase in market liquidity in the year of the IFRS mandate.
Firm-Year Regression Analysis of the Liquidity Effects around the IFRS Mandate
|IFRS adopter types:|
| Early Voluntary||0.07||−10.05||0.75||−5.47||3.31|
| Late Voluntary||−2.95***||−19.84***||−11.84***||−16.92***||−7.81** |
| Early Voluntary * Mandatory||−0.96*||−21.75***||−15.54***||−16.04***||−4.04**|
| Late Voluntary * Mandatory||−0.93||−3.63||−9.31**||0.92||−3.64|
| First-Time Mandatory||−1.00***||1.21||−2.99**||−6.57***|| −4.41***|
| U.S. GAAP||−0.34||0.86||11.24***||−4.94||−0.51|
| U.S. Listing||−0.17||9.11||−0.28||8.07||1.42|
| New Markets||−5.79*||−11.48||−10.39||−5.76||−31.74**|
| Log(Market Valuet−1)||−3.38***||−41.16***||−13.24***||−13.11***||−11.22***|
| Log(Share Turnovert−1)||−2.38***||−33.49***||−10.38***||−13.12***||−8.26***|
| Log(Return Variabilityt−1)||−1.85***||−6.82***||1.12||2.11**||−3.89***|
| Market Benchmark||105.69***||124.30***||60.44***||41.25***||53.58***|
|No. of observations||43,999||42,492||37,611||37,712||31,407|
|No. of unique firms||11,077||10,806||10,173||9,648||8,622|
|No. of countries||51||51||48||38||36|
|Zero Returns as dependent variable:|
| Early Voluntary||0.08||0.38||0.10||0.23||−0.16||−1.90***|
| Late Voluntary||−2.98***||−2.93***||−2.50***||−2.63***||−3.44***||−3.91***|
| Early Voluntary * Mandatory||0.45||−1.87***||−1.89***||−0.94*||−2.28***||−0.17|
| Late Voluntary * Mandatory||0.53||−1.61*||−2.00**||−0.79||−2.04**||0.83|
| First-Time Mandatory||0.36||−1.74***||−1.86***||−0.97***||−2.20***||−0.96***|
|Log(Total Trading Costs) as dependent variable:|
| Early Voluntary||0.01||0.73||1.65||2.53||1.29||−3.29|
| Late Voluntary||−11.65***||−10.06***||−11.13***||−10.23***||−12.83***||−12.60***|
| Early Voluntary * Mandatory||−10.48***||−11.57***||−14.51***||−15.08***||−18.07***||−10.44***|
| Late Voluntary * Mandatory||−4.90||−6.09||−8.13*||−12.05**||−11.09**||−2.89|
| First-Time Mandatory||1.64||1.05||−1.82*||−1.95||−5.15***||−2.80*|
|Liquidity Factor as dependent variable:|
| Early Voluntary||3.21||2.85||2.02||−1.40||1.20||−5.08***|
| Late Voluntary||−7.15**||−7.69**||−6.43*||−11.07***||−8.91**||−10.62***|
| Early Voluntary * Mandatory||0.15||0.57||−3.66*||−1.61||−8.99***||−4.46**|
| Late Voluntary * Mandatory||0.18||0.39||−4.85||0.09||−8.15**||−0.22|
| First-Time Mandatory||−0.23||−0.05||−4.46***||−0.98||−9.27***||−5.29***|
| No. of observations (Zero Returns)||34,673||64,101||105,527||29,410||27,759||43,999|
However, the magnitude of the effects differs across firms. Firms that are forced to adopt IFRS generally experience the smallest increase, while voluntary adopters see larger liquidity effects, either when they switch to IFRS ahead of the mandatory change (late voluntary) or in the year of the mandate (early voluntary). For instance, the coefficient estimate of −2.99 on First-Time Mandatory in column 3 suggests that the total trading costs of mandatory IFRS adopters decrease by 12 basis points, which amounts to a 3% improvement relative to the preadoption median of 4.23% (or 423 basis points). At the same time, trading costs for late voluntary adopters go down by 34 basis points even though they already decrease by 47 basis points when the firm initially switches to IFRS ahead of the mandate. Early voluntary adopters do not experience a significant liquidity change upon IFRS adoption, but experience a reduction in total trading cost by 61 basis points in the year IFRS reporting becomes mandatory.24 The economic magnitudes of the effects for first-time mandatory adopters fall into a similar range for the proportion of zero-return days and the bid–ask spreads. The former (latter) decreases by 100 (12) basis points or about 4% (6%) based on the preadoption median of 27% (1.94%). In the last column of panel A, we find similar results using the Liquidity Factor as a summary measure. The main control variables, that is, firm size, share turnover, volatility, and the market benchmark, are generally highly significant across all columns.25
The sizeable liquidity benefits for late voluntary adopters around their switch to IFRS have to be interpreted carefully as they are likely affected by selection effects. For instance, firms that adopt IFRS ahead of the mandate could signal a strong commitment to transparency and it could be that investors respond to this signal and the commitment to transparency, rather than the adoption of IFRS per se. More interestingly, early voluntary adopters experience liquidity improvements when IFRS reporting becomes mandatory and, in several cases, these increases are even larger than the liquidity changes of the mandatory adopters. Thus, mandatory adopters do not gain in liquidity relative to voluntary adopters. The liquidity effects for the early voluntary adopters cannot be explained with a switch in the reporting standards, as they already report under IFRS. They are also not driven by the fact that we code countries' actual adoption of mandatory IFRS reporting, rather than the earlier announcements of plans to do so. That is, when we additionally include an indicator variable marking the period between the announcement and the adoption dates of IFRS, the results are similar. As discussed in section 2, the liquidity benefits for voluntary adopters when the mandate takes effect could have multiple sources. They could stem from comparability benefits when other firms in the economy are forced to adopt IFRS. Alternatively, they could reflect concurrent changes in the institutional environments of IFRS-adopting countries, which would affect both voluntary and mandatory adopters. Finally, they could result from firms' reporting improvements around the IFRS mandate, as many voluntary adopters, especially the early ones, initially do not provide full IFRS reports and often start with dual reporting strategies (e.g., Daske et al. ). We investigate these issues in the cross-sectional analyses.
In Panel B of Table 4, we assess the sensitivity of the liquidity results to various research design choices. We report these analyses for the proportion of zero-return days, total trading costs, and the Liquidity Factor.26 In model 1, we limit the sample to IFRS adoption countries. Hence, the liquidity effects for mandatory IFRS adopters are evaluated relative to firms that have not yet switched to IFRS by the end of 2005. In this case, the coefficients for our variables of interest are largely insignificant. We then vary our benchmark sample using either U.S. firms only (model 2) or the entire Worldscope population (model 3). While the choice of benchmark sample does not seem to matter for the zero-returns metric, the results for the total trading costs and the liquidity factor are weaker than in panel A, in particular when using U.S. firms as a benchmark. The variability of the results in model 1 through model 3 and panel A illustrates the importance of benchmarking and the choice of the benchmark. In model 4, we control for changes in sample composition over time by holding the sample constant. The effects become slightly weaker, and for the liquidity factor, all three mandatory coefficients are even insignificant. As this specification tilts the sample towards larger, more stable firms, some attenuation is expected. In model 5, we limit the treatment sample to firms that have already switched to IFRS, that is, we exclude firms from IFRS-adopting countries that have not yet adopted the new standards. Including these firms could work against finding significant effects if markets (partially) anticipate the liquidity consequences of the IFRS mandate. Moreover, if there are concurrent changes in the institutional framework that benefit the liquidity of all firms in the economy, removing these firms should increase the effects. Consistent with these two explanations, the results for model 5 are substantially stronger, both in terms of significance and magnitude, than in panel A. However, the rank order of the coefficients and our inferences remain the same. Finally, in model 6, we replace the firm-fixed effects by country-fixed effects, and, again, find similar results.
3.3.3. Analyses of Cost of Capital and Valuation Effects. Table 5 presents the results of the cost of capital and valuation analyses. We start with the base specification (model 1) and, consistent with the difference-in-differences results, find a significant increase in cost of capital for firms that are forced to adopt IFRS. However, our estimation of the implied cost of capital might suffer from anticipation effects. For instance, if IFRS in fact lower the cost of capital and, as a result, investors use a lower rate to discount expected future cash flows that occur after IFRS adoption, the valuation models produce a lower implied cost of capital estimate even for pre-IFRS years because they assume a constant cost of capital. Thus, anticipation effects may make it harder to detect a reduction in the cost of capital. We address this issue by (1) excluding firm-year observations immediately before mandatory IFRS adoption (model 2) and (2) by moving the mandatory IFRS indicator variables by one year, that is, we start coding them as one in the year before the official IFRS adoption date (model 3). These tests also mitigate concerns about first-time IFRS interim reporting, press releases, and other disclosures ahead of the accounting change, all of which would likely accelerate the capital-market effects (if IFRS reporting has an effect).
Firm-Year Regression Analysis of the Cost of Capital and Valuation Effects around the IFRS Mandate
|IFRS adopter types:|
| Early Voluntary||0.49||0.74||0.84||5.43||3.09||−1.35|
| Late Voluntary||−0.16||0.13||0.34||12.26**||9.59||11.06*|
| Early Voluntary * Mandatory||0.37||−0.21||−0.66***||−4.31||4.75||14.31***|
| Late Voluntary * Mandatory||0.41||−0.31||−0.90**||−13.60**||−4.87||2.67|
| First-Time Mandatory||0.67***||0.23||−0.26**||−4.57**||3.31||8.05***|
| U.S. GAAP||0.05||−0.35||−0.02||7.36||5.65||8.14|
| U.S. Listing||−0.28||−0.20||−0.32||6.48||3.19||6.29|
| New Markets||0.57*||0.65||0.30||34.51||26.29||36.09|
| Log(Total Assets)||0.59***||0.62***||0.49***||−41.96***||−42.33***||−41.58***|
| Financial Leverage||1.35**||0.69||1.35**||37.49***||40.83***||37.52***|
| Risk-Free Rate||28.64***||18.08***||24.06***||–||–||–|
|(6.18)||(3.26)||(5.09)|| || || |
| Return Variability||2.15||1.89||2.09||–||–||–|
|(1.62)||(1.24)||(1.58)|| || || |
| Forecast Bias||7.53***||7.30***||7.68***||–||–||–|
|(4.95)||(3.97)||(5.05)|| || || |
| Asset Growth||–||–||–||16.23***||17.67***||15.76***|
| Industry q||–||–||–||10.90||14.97||11.67|
| Market Benchmark||252.98*||270.80||237.60*||431.77***||407.74***||409.14***|
|No. of observations||9,946||8,129||9,946||40,769||34,206||40,769|
|No. of unique firms||3,584||3,391||3,584||10,959||10,854||10,959|
|No. of countries||36||36||36||47||47||47|
Consistent with the existence of anticipation effects, we find that the coefficients on the mandatory IFRS variables move in the expected direction in model 2 and become significantly negative in model 3.27 Even the relative magnitude of the effects for the three adopter types around the mandate is now consistent with the liquidity results. Mandatory adopters experience a decrease in the cost of capital by 26 basis points, which is a 2.5% decline relative to the preadoption median cost of capital. Early voluntary adopters and late voluntary adopters experience declines in their cost of capital by 66 and 90 basis points, respectively.28
In the second half of Table 5, we report the Tobin's q results across three similar specifications. Tobin's q captures not only changes in firms' cost of capital but also real effects on investment and growth and IFRS-related costs (e.g., implementation costs). In addition, it does not rely on analyst forecasts, which could be substantially affected by the switch to a different accounting regime. We find that, in the year of the regulatory change, mandatory adopters and late voluntary adopters exhibit a significant decrease in firm value compared to the random sample of benchmark firms. Similar to the cost of capital results, the coefficients on the three mandatory IFRS variables increase as we move from model 1 through model 3, and they become significantly positive for mandatory and early voluntary adopters in model 3. For mandatory adopters, the effect amounts to 7.1% of the pre-IFRS median q, which is surprisingly large. One reason for this finding could be that the measurement of Tobin's q is mechanically affected by the change in accounting rules around the switch to IFRS. Hung and Subramanyam  provide evidence for German firms that the switch to IFRS, on average, increases the book value of equity and total assets, which leads to a downward bias in the q effects. However, these transition effects likely differ across firms and countries and could also go in the other direction (e.g., Capkun et al. ).29 Hence, the magnitude of the Tobin's q effects should be interpreted cautiously.
Overall, the results for the cost of capital and Tobin's q are consistent with each other, and they are in line with our liquidity findings once we control for anticipation.
4. Heterogeneity in the Capital-Market Effects Around the IFRS Mandate
4.1 research design
In our second set of empirical tests, still employing the firm-year design, we examine the cross-sectional variation in the capital-market effects around IFRS reporting. We sequentially partition IFRS firm-year observations by countries' institutional frameworks using the following country-level factors. (1) The rule of law in the year 2005, drawn from Kaufmann, Kraay, and Mastruzzi . Higher values represent countries with stricter enforcement regimes. (2) We distinguish between EU member states and the remaining IFRS adoption countries. Apart from providing descriptive evidence for the largest economic bloc of countries requiring IFRS reporting, this partition also identifies a set of countries for which enforcement regimes are significantly revised around the adoption of mandatory IFRS reporting (see section 2 and also Hail and Leuz ). (3) We use the opaqueness of financial reporting practices in a country, measured by the earnings management score from Leuz, Nanda, and Wysocki , as a proxy for the strength of firms' reporting incentives in a given country (and hence the likely responsiveness to the IFRS mandate). We multiply this measure by minus one so that higher values indicate less opaque earnings. (4) To capture the degree to which the accounting rules change with the switch to IFRS, we use the Bae, Tan, and Welker  summary score of how local GAAP differ from IAS on 21 key accounting dimensions. Higher scores represent more differences. (5) We single out countries with an official convergence strategy toward IFRS reporting before it becomes mandatory.30
One concern about country-level institutional variables is that they are all highly correlated and that some of them are outcomes of more fundamental qualities of countries' institutional frameworks. To address this concern, we orthogonalize earnings opaqueness and accounting discrepancies with respect to more fundamental country characteristics. That is, we first regress the raw values of proxies (3) and (4) on countries' legal origin (La Porta et al. ) and the log transformed average gross domestic product (GDP) per capita (World Bank) and then use the residuals from those regressions to form partitions in the cross-sectional analyses.31
Finally, we use the proportion of firms voluntarily reporting under IFRS in a given industry to split the sample. The idea behind this partition is that voluntary adopters may experience comparability effects when other firms in the same industry must switch to IFRS. However, if many industry peers already report under IFRS, the comparability benefits (or externalities) from mandatory adopters are likely to be smaller. Thus, we analyze whether the effects for voluntary adopters in the year of IFRS mandate are inversely related to the number of global industry peers already reporting under IFRS.
For our cross-sectional analyses, we combine the early and late voluntary adopters into a single group to ensure that we have a sufficient number of observations in each of the bins created by our partitions. That is, we create two indicator variables, Voluntary and Voluntary * Mandatory, marking the entire series of IFRS observations and the fiscal years ending on or after the mandated change, respectively. We then transform the continuous institutional factors into binary variables splitting by the country or industry medians of the treatment sample. We interact these binary Conditional Variables with each of the IFRS indicators leading to the following empirical model:
Relative to equation (1), the interpretation of the coefficients on Voluntary, Voluntary * Mandatory, and First-Time Mandatory does not change except that it applies only to IFRS adopters where the conditional variable is below the median. The interaction terms represent the incremental capital-market effects for firms from countries and industries where the conditional variable is above the median. To gauge the total effects for those latter firms, we must sum the two corresponding coefficients. The control variables and fixed effects are the same as before.
4.2 empirical results
Table 6, panel A, presents descriptive information on the institutional variables by country (together with the official IFRS announcement and adoption dates). Next to the raw values or, in the case of the earnings management and accounting differences scores, the residuals from a first-stage regression on legal origin and GDP, we present (in parentheses) the binary indicator values used to partition the treatment sample countries. In panel B, we report the percentages of firms voluntarily reporting under IFRS in a given industry in the year before the mandate.
The sample used in the cross-sectional analyses is the same as in section 3. For brevity, we tabulate and discuss only the results estimating equation (2) for the Liquidity Factor. However, the results using the four individual liquidity proxies are very similar to those reported below and the inferences are essentially the same. The cross-sectional results for the cost of capital (using model 3) are weaker in the sense that the differential effects are not always significant, but they point in the same direction. The cross-sectional results for Tobin's q are in line with liquidity for the rule of law, earnings opaqueness, and convergence strategy partitions, but insignificant or opposite otherwise.
In Table 7, we report the coefficient estimates for the IFRS adopter type variables. In addition, we indicate the statistical significance of the joint coefficients (p-values from Wald tests). As model 1 shows, the liquidity benefits around the introduction of IFRS occur only in countries with a relatively strong rule of law.32 That is, the interaction terms for voluntary and mandatory adopters are negative and highly significant for the strong enforcement group, but no such effects exist in countries with weak legal enforcement. This result is plausible as the IFRS mandate is unlikely to have much of an effect if legal enforcement is weak. Partitioning by EU member states yields similar results (model 2). Aside from relatively strong legal systems in EU countries, this result may reflect recent efforts to tighten enforcement, corporate governance, and auditor oversight. We also examine the differential effects for firms that already face a strict enforcement regime because they are cross-listed on a U.S. exchange and subject to SEC oversight (results not tabulated). Although the sample of exchange-traded American Depository Receipts in IFRS-adopting countries is small, the results are as expected if enforcement changes play an important role. We find that, compared to the remaining IFRS firms, IFRS adopters that are cross-listed on U.S. exchanges experience lower, if any, liquidity benefits.
Cross-Sectional Analysis of the Liquidity Effects around the IFRS Mandate
|IFRS adopter types:|
| (1) Voluntary||−3.04||11.72||−2.11||10.45||−6.05**||−4.17|
| (2) Voluntary * Conditional Variable||−2.85||−18.08**||−2.95||−15.77*||31.30***||0.27|
| Test of (1) + (2) = 0 [p-value]||[0.09]||[0.01]||[0.10]||[0.04]||[0.00]||[0.27]|
| (3) Voluntary * Mandatory||2.64||−4.05 ||−0.74 ||3.11|| −5.39***|| −4.90*|
|(1.11)||(−1.18) ||(−0.32) ||(0.60)||(−3.25) ||(−1.68)|
| (4) Voluntary * Mandatory *Conditional Variable|| −8.66***||−0.67 || −8.70***||−8.33 ||5.54|| 2.20|
|(−3.10) ||(−0.19) ||(−3.13) ||(−1.58) ||(0.69)|| (0.61)|
| Test of (3) + (4) = 0 [p-value]||[0.00]||[0.01]||[0.00]||[0.00]||[0.98]|| [0.20]|
| (5) First-Time Mandatory||1.54|| 3.20*||0.02||−2.69* ||−11.11***|| −7.35***|
|(0.99)||(1.91)||(0.01)||(−1.81) ||(−7.71) ||(−4.13)|
| (6) First-Time Mandatory *Conditional Variable|| −9.40***||−13.90***|| −9.59***||−4.75**|| 16.66***|| 5.48**|
|(−5.12) ||(−7.09) ||(−4.96) ||(−2.54) ||(8.21)|| (2.21)|
| Test of (5) + (6) = 0 [p-value]||[0.00]||[0.00]||[0.00]||[0.00]||[0.00]|| [0.28]|
|Control variables, firm-fixed and industry-year-fixed effects||Included||Included||Included||Included||Included||Included|
|No. of observations||31,407||31,407||30,933||31,407||31,407||31,407|
|No. of unique firms|| 8,622|| 8,622|| 8,494|| 8,622|| 8,622|| 8,622|
|No. of countries||36||36||34||36||36||36|
Next, we partition the sample by firms' reporting incentives. The idea is that firms operating in institutional environments that provide strong reporting incentives are likely to be more responsive to the IFRS mandate. In model 3, we use the transparency of firms' earnings (prior to IFRS adoption) as a proxy for these reporting incentives. We find that the liquidity effects around IFRS adoption are larger and in fact only present in countries where earnings are relatively transparent in the first place. A split of IFRS adoption countries by ownership concentration, an alternative incentives proxy, yields similar results (not tabulated). These findings indicate that the documented capital-market benefits require more than the adoption of high-quality accounting standards; they need supporting institutions. Concurrent changes in these institutions could also explain why voluntary adopters experience larger effects around the IFRS mandate than firms that are forced to adopt IFRS.
The next two models examine the role of accounting differences between local GAAP and IFRS. The results for model 4 and model 5 are fairly similar. Both models suggest that mandatory and voluntary adopters exhibit larger increases in liquidity around the IFRS mandate if the differences between local GAAP and IFRS (or IAS) are larger. At face value, these results suggest that the differences in the accounting standards matter. However, accounting differences are likely to matter most if the rules are properly enforced and firms do not have countervailing incentives (e.g., Hope , Burgstahler, Hail, and Leuz ). To explore this issue, we further single out the group of IFRS adoption countries where not only local GAAP differ substantially from IFRS but also enforcement regimes or reporting incentives are strong. That is, we compare the effects in IFRS adopting countries with stronger rule of law (or more transparent earnings) and larger accounting discrepancies against the effects for the rest of the IFRS sample. Results (not tabulated) show that, as expected, liquidity increases the most for the former group, suggesting that change in the standards could play a role, but only when coupled with proper reporting incentives and legal enforcement.
Finally, in model 6, we attempt to analyze potential comparability and global convergence benefits from IFRS reporting. Consistent with the notion that voluntary adopters should experience fewer positive externalities from the IFRS mandate when more of their peers already report under IFRS, we find a positive coefficient on the interaction between Voluntary * Mandatory and the partitioning variable for industries with a higher fraction of IFRS firms. However, as the coefficient is statistically insignificant, we are unable to provide evidence supporting the notion of comparability effects. The interpretation of the significantly positive incremental coefficient for mandatory adopters in industries with high voluntary adoption rates is less straightforward because there is no clear prediction. From a comparability standpoint, one would expect mandatory adopters to benefit more when there are already many peers reporting under IFRS, yielding an opposite prediction, that is, a negative interaction. On the other hand, it is possible that the switch to IFRS is rewarded less in the market place when there are already many industry peers reporting under IFRS, suggesting weaker effects and hence a positive interaction.
Taken together, the cross-sectional analyses show that the capital-market effects are heterogeneous across countries in a way that is consistent with the reporting incentives view in the international accounting literature.
5. Country-Month Analyses of the Liquidity Effects around the IFRS Mandate
5.1 research design
Our third and final set of tests pursues a different empirical strategy to identify the capital-market effects around the introduction of mandatory IFRS reporting. Instead of comparing firm-year observations across IFRS and non-IFRS adopting firms, we use the fact that the IFRS mandate applies to firms at different points in time depending on their fiscal year-end. In other words, this approach exploits the gradual adoption of the new reporting regime during the initial year of mandatory IFRS reporting, as given by countries' fiscal year-end pattern. This adoption pattern is largely exogenous once a country sets the initial date for IFRS adoption. If there are discernable capital-market effects from the IFRS mandate, we expect changes in market liquidity to mirror countries' adoption patterns. That is, liquidity should decrease as more firms start reporting under IFRS. As this approach is tied to the phase-in of IFRS and the availability of IFRS financial reports, it should not pick up one-time shocks or time trends in liquidity in IFRS adoption countries, which could affect the firm-year analyses. It is also more likely to capture the effects of mandatory IFRS reporting and less likely to reflect other institutional changes (e.g., in the governance or enforcement regimes) that take place in the year of the IFRS mandate. Of course, if these supporting changes in the countries' infrastructure operate primarily through firms' financial reports, for example, in making IFRS reports more credible, then the capital-market effects of the other institutional changes should exhibit a similar time pattern as the IFRS effects and hence cannot be separated from each other.
We analyze liquidity changes at the aggregate, that is, country-month level. The reasoning behind this design choice is that, by aggregating firms in a country and examining aggregate liquidity changes, we are more likely to also capture any externalities that adopting firms confer on firms that have already adopted or will soon adopt IFRS (e.g., due to better comparability). The country-month approach also addresses concerns about potential cross-sectional correlation in the error terms due to the fact that all firms in a given country are subject to the same mandate.
We relate aggregate changes in market liquidity to a variable tracking countries' IFRS adoption patterns at the monthly level. We define ΔIFRS Adoption Rate as the month-to-month change in the cumulative proportion of firms in a given country that are required to follow IFRS for the first time. We assign firms to a given month in two ways. Our first approach relies on firms' fiscal year-ends (FYE) and assumes that financial statement information becomes available with a three-month time lag.33 That is, in computing the cumulative adoption rate, we assign the value of one to each month starting in month m=FYE+3, and zero otherwise. For instance, if a firm's fiscal year ends on April 30th, we assume the financial information is made available in July. We then sum all the firm-level values in a given country and month, and divide it by the total number of firms switching to IFRS over the entire year. We label this variable ΔIFRS Adoption RateFYE.
Our second approach accounts for the fact that many adopting countries require some form of IFRS interim reporting in the year leading up to the first annual IFRS financial statements (see IFRS 1 and IAS 34). Interim reporting likely weakens the power of tests that are centered on the release of annual information. Moreover, it is likely that a uniform three-month time lag does not accurately reflect firms' reporting practices. We therefore apply the following two refinements to create ΔIFRS Adoption RateInterim. (1) We use the actual announcement dates for interim and annual earnings as reported in I/B/E/S to assign firms to a given month. If these data are missing, we first determine whether local rules require quarterly or semiannual reporting (using LexisNexis and Websites of national stock exchanges), and then, based on this classification, allow two months for interim information to be distributed. We again apply the FYE+3 convention for the annual report without I/B/E/S announcement dates. (2) Once the dates of interim and annual reporting are defined, we account for interim reporting by adding 0.25 (0.5) to the adoption rate in the release month of each quarterly (semiannual) report, and again assign a value of one for the annual report. Thus, the cumulative adoption rate for a single firm that reports quarterly takes on the values of 0.25, 0.5, 0.75, and 2 after the disclosure of the first, second, third, fourth quarter, and annual reports, respectively. This coding assumes that firms jointly release fourth quarter (or second half-year) financial information and the annual report. That is, a firm reporting semiannual earnings in September and annual earnings in March of the following year is coded as zero until September, 0.5 from October to March, and two starting in April.
The difference between our two coding approaches is that the FYE adoption rate relies purely on firms' fiscal year-ends whereas the Interim adoption rate relies on actual announcement dates from I/B/E/S, whenever available, and it accounts for the early release of IFRS information through interim reports.34 These two refinements are potentially important as the country-month analysis relies heavily on identifying when the new IFRS financial statements are released to the market.
We use Zero Returns, Price Impact and the Bid-Ask Spread as dependent variables, each computed at the firm level in a given month (see the appendix).35 We aggregate the liquidity measures in a country and month by taking medians and then compute monthly changes. Similar to the firm-year analyses, we control for general (and worldwide) trends in the market proxies by including the contemporaneous change (Δ) in a Market Benchmark, based on monthly median changes in liquidity for a random sample of up to 150 firms per non-IFRS adopting country. As we cannot include firm-level controls in this analysis and, hence, the idiosyncrasies of a single random draw are larger, we repeat this procedure 100 times and use the resulting average values in the analyses. In addition, we include lagged changes of the dependent variable from the same calendar month one year ago to account for seasonal patterns. For instance, aggregate market liquidity could be higher (lower) just after (before) most firms in a country report their annual reports. Because liquidity is likely to be correlated over time, we also include the lagged level of the dependent variable. Apart from country-fixed effects, we further control for firm size and return variability at the market level (as defined in the notes to Table 8). We measure all lagged variables as of month m−12. This design leads to the following empirical model, where ΔEconCon refers to the three liquidity measures:
Country-Month Analysis of the Liquidity Effects around the IFRS Mandate
|ΔIFRS Adoption RateFYE||360||6.7%||20.6%||0.0%||0.0%||0.2%||1.9%||97.0%|
|ΔIFRS Adoption RateInterim||656||7.4%||18.7%||0.0%||0.0%||1.1%||4.6%||108.8%|
|Market Value||360|| 151.3|| 161.8|| 17.3|| 47.1|| 96.4|| 164.6|| 789.3|
|ΔMarket BenchmarkZero Returns||360||−0.31%||4.94%||−9.86%||−3.81%||1.14%||2.61%||8.23%|
|ΔMarket BenchmarkPrice Impact||360||0.000||0.019||−0.024||−0.010||−0.006||0.010||0.049|
|ΔMarket BenchmarkBid-Ask Spread||360||−0.01%||0.07%||−0.17%||−0.04%||−0.01%||0.02%||0.16%|
|ΔIFRS Adoption RateFYE, m||−1.07 ||−4.22* || ||−0.09 ||−6.76 || ||−0.02 ||−0.19* || |
|(−0.74) ||(−1.68) || ||(−0.02) ||(−1.12) || ||(−0.41) ||(−1.73) || |
|ΔIFRS Adoption RateInterim, m|| || ||−4.18**|| || ||−6.13|| || ||−0.16*|
|(−2.43) || || ||(−1.12)|| || ||(−1.65)|
| Log(Market Valuem−12)||−3.77||−17.79||3.21||5.28||35.06||12.79||0.11||−1.35||0.82*|
| Return Variabilitym−12||66.50||458.93**||3.59||−280.35||−660.50||−147.11||−4.16||2.31||2.79|
| Dependent Variablem−12||3.27||26.56||14.42||−1.77||1.38||1.41||13.36||15.31||21.78|
| ΔDependent Variablem−12||12.06||21.34||1.78||16.10*||9.62||23.57*||10.77||17.48||6.96|
| ΔMarket Benchmarkm||−3.78||6.45||14.86||238.37***||382.70**||370.32***||201.68***||198.03***||231.95***|
|No. of observations||360||96||169||330||86||155||315||84||150|
5.2 sample description and empirical results
The sample window for the country-month analysis covers a 15-month period (i.e., January 2006 to March 2007, except for Singapore where we use January 2004 to March 2005) during which the first mandatory IFRS annual reports are released. When we account for interim reporting, we include the 12 months leading up to the first full set of financial statements under IFRS and extend the window to 27 months (i.e., January 2005 to March 2007). To capture the effects around the IFRS mandate on firms that are forced to adopt IFRS, we include only firms that report under IFRS for the first time, that is, we exclude voluntary adopters from the aggregate liquidity measures. The sample using annual reporting consists of 360 country-month observations with data available from 26 countries. Allowing for interim reporting, the sample increases to a maximum of 656 country-month observations.
Table 8, panel A, reports descriptive statistics for the dependent and independent variables. The mean and median monthly changes in the liquidity metrics are either zero or slightly negative, suggesting that there is no clear trend in liquidity. The same is true for the market benchmarks, which are based on 100 random samples of non-IFRS adopting countries.36 The average change in IFRS Adoption RateFYE equals 6.7%. However, the distribution is highly skewed, and there are many months with only very small changes in the cumulative adoption rate. The reason is that, in many countries, fiscal year-ends cluster in December, March, June, or September. The distribution is slightly less skewed when we compute the changes in IFRS Adoption RateInterim because this variable accounts for variation in firms' earnings announcement dates.
Panel B of Table 8 presents coefficients from estimating equation (3) together with robust t-statistics. For each dependent variable, we report results for the ΔIFRS Adoption Rate variable based on the FYE+3 convention (model 1 and model 2) and the combination of interim and annual reporting (model 3). In addition, in model 2 and model 3, we limit the sample to the months with the biggest changes in the IFRS adoption rate to increase the power of our tests. That is, we use only observations that belong to the top quartile of the adoption rate changes. The sample restriction is meant to address the issue that in many months the change in the adoption rate is very small or zero, while liquidity continues to fluctuate, which likely attenuates our coefficients.
The results across the three dependent variables are consistent in that all the coefficients on the ΔIFRS Adoption Rate variable are negative, suggesting a reduction in zero-return days, the price impact of trades, and bid–ask spreads. However, the coefficients are often not statistically significant. For zero returns and spreads, the results are significant at the 10% level or better for model 2 and model 3, that is, for the samples that are restricted to the biggest changes. For price impact, model 2 and model 3 point in the right direction but the p-values are only around 26%. Overall, these results suggest an increase in market liquidity around the phase-in of IFRS reporting.
In terms of economic magnitude, the results in the first three columns suggest that a 10% change in the adoption rate translates into a reduction between 7 and 31 basis points in the level of aggregate zero returns. This corresponds to a reduction of up to 1.1% from the mean aggregate zero-return days of 28%. Aggregate price impact decreases up to 0.007% per US$1,000 trade and bid–ask spreads decrease up to two basis points, representing a reduction of 1.8% and 1.2% compared with the mean aggregate level of 0.39 and 1.7%, respectively.
While the significance levels and the magnitude of the effects are modest, also compared to the results in the firm-year analysis, it is important to recognize that, despite its conceptual appeal from an identification perspective, the country-month analysis is conservative and low in power for at least two reasons. First, the identification of the IFRS effects relies on a relatively small number of observations. Firms' fiscal year-ends are heavily clustered in time, which reduces the variation that can be used to identify the IFRS adoption effects. Consistent with this argument, the magnitude of the coefficient estimates on ΔIFRS Adoption Rate is substantially larger when we limit the sample to the months with the biggest changes. Second, as we conduct the analysis in changes, it is important to correctly identify when the IFRS financial information is released to the market. This is not a trivial task considering that many firms provide IFRS interim reports and IFRS-related guidance ahead of their first IFRS annual report.
We conduct an array of robustness checks. First, we perform three tests to reduce the concern that our results still capture general liquidity effects unrelated to IFRS reporting. (1) We replicate our country-month analysis two years before the introduction of mandatory IFRS reporting. In these “placebo” analyses, our adoption rate variables are never significant. (2) We drop observations from March 2006 to address the concern that the results are driven by a single calendar month, that is, the month when all the December 2005 fiscal year-end firms likely report, and still obtain similar results. (3) We compute aggregate liquidity only for firms in treatment countries that do not yet adopt IFRS in a given country and month to see whether the aggregate liquidity increase documented earlier is also present in these firms. We find little evidence that these firms experience liquidity increases, suggesting that our results stem primarily from firms switching to IFRS in the respective country and month.
Second, we use two- and three-month changes in liquidity and the adoption rate to address the difficulty of correctly identifying when the new IFRS financial information is released. Longer intervals reduce the likelihood of misclassifying the reporting month but also reduce the number of observations. The results using two- and three-month changes are generally consistent with (and in some cases stronger than) the findings in Table 8. That is, we find significant decreases in zero returns and spreads around the IFRS phase-in, but the results for price impact continue to be weak.
Finally, we perform several sensitivity tests regarding our empirical specification. We use a log-linear specification for the dependent variables (which is particularly important for price impact as this variable is highly skewed, even at the country-median level), introduce additional control variables (e.g., lagged turnover, changes in firm size and volatility), and vary the variable measurement (e.g., use means instead of medians or use the midpoint of a month as cutoff for assigning earnings announcement dates to a particular month instead of the month end). These sensitivity checks produce similar results to those presented in Table 8, although the magnitude of the effects depends on the exact specification.
Taken together, the results for the country-month analysis corroborate the liquidity increase around the IFRS mandate as suggested by the firm-year analysis. However, liquidity effects in the country-month analysis are weaker in magnitude. As the latter analysis is more likely to identify pure IFRS reporting effects, the smaller magnitude is expected if the firm-year results are in part driven by concurrent changes in countries' institutional frameworks. Alternatively, the weaker results could simply reflect the difficulty of identifying the information release, which hurts the country-month analysis.
6. Conclusion and Suggestions for Future Research
The recent move towards IFRS in over 100 countries is one of the most significant regulatory changes in accounting history. The lessons and merits of this change are still debated and are a major policy issue. This paper contributes to this debate by providing early evidence on the capital-market effects of introducing mandatory IFRS reporting in 26 countries around the world. Aside from its policy relevance, the study provides rare evidence on the economic consequences of forcing firms to change an entire set of accounting and disclosure standards.
We analyze effects in stock market liquidity, cost of equity capital, and equity valuations using firm-year panel regressions with firm-fixed effects. We also perform country-month liquidity analyses that exploit countries' phase-in pattern during the initial year of IFRS adoption. Our results can be summarized as follows. We find that mandatory adopters experience statistically significant increases in market liquidity after IFRS reporting becomes mandatory. In our firm-year analyses, the effects range in magnitude from 3% to 6% for market liquidity relative to the levels prior to IFRS adoption. Consistent with the liquidity improvements, we also document a decrease in firms' cost of capital and a corresponding increase in Tobin's q, but only if we account for the possibility that these effects occur prior to the official IFRS adoption date. The latter suggests that the market anticipates the economic consequences of the mandate.
In interpreting these results, three additional sets of findings are worth noting. First, while the results are robust to numerous sensitivity checks, the magnitude and statistical significance of the documented effects vary substantially depending on the benchmark sample, the length of our sample period, and whether we include firms from IFRS-adopting countries with fiscal year-ends other than December that have not yet switched to IFRS as a benchmark. The variation in the effects illustrates the difficulties of benchmarking the economic consequences of a regulatory change that simultaneously affects all firms in an economy.
Second, the aforementioned capital-market effects of mandatory (or forced) adopters are relative to local GAAP benchmark firms that are not required to adopt IFRS or have not yet switched. Firms that already switch to IFRS voluntarily prior to the mandate are an alternative group against which one could benchmark the effects. We find that late voluntary adopters, that is, firms that switch to IFRS reporting shortly before doing so becomes mandatory, experience positive liquidity and valuation effects. These effects are likely the result of self-selection and therefore not representative for the population of firms that is forced to adopt IFRS. More to the point, we document capital-market benefits for (early and late) voluntary adopters in the year of the mandated switch to IFRS. The magnitude of these benefits often exceeds the corresponding effects for mandatory adopters, indicating that mandatory adopters do not gain in market liquidity or market value relative to voluntary adopters around the introduction of the IFRS mandate. As the latter group already reports under IFRS, one potential explanation for these capital-market effects is that mandatory adopters confer positive externalities on voluntary adopters by increasing the set of comparable firms, which in turn could lead to improved risk sharing across a larger set of investors. We conduct tests for this explanation but obtain insignificant results. Another explanation for the effects of voluntary adopters around the IFRS mandate is concurrent changes in the institutional environment, for example, with respect to enforcement, governance, or auditing, which apply to all firms in the economy, including the voluntary adopters. Besides, voluntary adopters likely have better reporting incentives to begin with and, hence, should be more responsive to such institutional changes, which could explain stronger treatment effects. This explanation clearly questions the extent to which the capital-market effects for mandatory adopters can be attributed solely or even primarily to IFRS.
Third, and related to the last point, we analyze the cross-sectional variation in the effects for mandatory and voluntary adopters in an attempt to shed light on the factors driving the capital-market reactions. For both groups, we find that the capital-market benefits occur only in countries with relatively strict enforcement regimes and in countries where the institutional environment provides strong incentives to firms to be transparent. In the other IFRS adoption countries, market liquidity and firm value remain largely unchanged around the mandate. We also show that the effects around mandatory adoption are stronger in countries that have larger differences between local GAAP and IFRS and in countries without a prior convergence strategy towards IFRS. Although these latter results are consistent with the notion that differences in the accounting standards matter as well, further analysis shows that the effects are strongest for countries with large local GAAP/IFRS differences coupled with strong enforcement (or reporting incentives). Thus, the strength of enforcement regimes and firms' reporting incentives play a major role for our results.
Taken as a whole, our evidence suggests modest but economically significant capital-market benefits around the introduction of mandatory IFRS reporting. However, as with many empirical studies that explore uncharted terrain, our study has a number of results that call for further investigation. For instance, while it seems clear that the documented capital-market effects cannot be attributed solely to the new reporting standards per se, it is still an open question which other factors do play a role. As several countries around the world revise their enforcement and governance regimes to support the introduction of IFRS, we suggest that our results likely reflect the joint effects of these institutional changes and the IFRS mandate. Investigating this conjecture and the role of countries' enforcement regimes, which still differ considerably across IFRS countries, is an interesting avenue for future research.
Similarly, we point to comparability effects as a potential source for the capital-market effects, but are unable to provide statistical support for this explanation. Future research could explore this issue and the existence of positive externalities in more detail as well as examine whether IFRS are in fact implemented in ways that improve international comparisons. Furthermore, we suggest that the market's anticipation of economic consequences of the IFRS mandate as well as transitional effects around the first-time adoption could play a role in our results. For instance, we offer these effects as an explanation that reconciles the cost of capital and Tobin's q results with the liquidity findings. While it seems plausible that liquidity proxies are less subject to anticipation and transitional effects, this issue warrants further investigation. Finally, we note that our study is limited to a relatively short time period after the introduction of mandatory IFRS reporting. Thus, it remains to be seen whether the effects are sustained in the long run.