IFRS and U.S. GAAP vis-à-vis German GAAP?

The International Journal of Accounting 43 (2008) 339 – 386

Analyzing the German accounting triad — “Accounting Premium” for IAS/IFRS and U.S. GAAP vis-à-vis German GAAP?☆ Jürgen Ernstberger ⁎, Oliver Vogler 1 University of Regensburg, Universitätsstraße 31, D-93053 Regensburg, Germany

Abstract This paper critically examines the impact of voluntary adoption of Internationally Accepted Accounting Principles (IAAP, i.e., IAS/IFRS and U.S. GAAP) on the cost of equity capital in Germany. We find that (1) overall cost of equity-capital estimates in the Capital Asset Pricing Model (CAPM) for companies applying IAAP are significantly lower compared to those applying German GAAP, (2) an enhanced multi-factor model which incorporates the accounting-regime differences (called “GM model”) absorbs the cost of equity-capital differences, and (3) changes of the institutional background in Germany and of the accounting standards lead to different cost of equity capital effects for subperiods of the 1998–2004 voluntary-adoption period, while particularly controlling for effects like self-selection, cross-listing, and New Market (Neuer Markt) listing.

☆ A previous version of this paper was presented at the Illinois International Accounting Symposium held at University of Hawai'i at Manoa, HI, in June 2007. We are grateful to the discussant Bill Cready and conference participants for their comments and suggestions. Moreover, we thankfully acknowledge helpful comments by an anonymous reviewer. We are also grateful to the participants of the Annual Congress of the European Accounting Association 2008 in, Rotterdam, especially the discussant Ann Gaeremynck, for further comments and suggestions. Moreover, we thank, the organizing committee of this conference for granting us the Best Paper Award in the category “International Financial Accounting" for this paper. ⁎ Corresponding author. Tel.: +49 941 943 2690; fax: +49 941 943 4497. E-mail addresses: [email protected] (J. Ernstberger), [email protected] (O. Vogler). 1 Tel.: +49 8092 2302929; fax: +49 941 290 3314.

0020-7063/$ - see front matter © 2008 University of Illinois. All rights reserved. doi:10.1016/j.intacc.2008.09.008

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The central thesis advanced in this paper is that changes in the accounting standards and the institutional infrastructure can influence the impact of applying IAAP. Therefore, we suggest incorporating an accounting factor into the cost of equity-capital analysis. © 2008 University of Illinois. All rights reserved. JEL classification: M41; G12 Keywords: Accounting regime adoption; Cost of equity capital; Multi-factor model; IFRS; U.S. GAAP; Germany

1. Introduction This paper critically examines the impact of the voluntary adoption of IAS/IFRS2 and U.S. GAAP (in the following referred to as “Internationally Accepted Accounting Principles” (IAAP)) by German companies. The results suggest that for companies adopting IAAP an “accounting premium” is granted by investors, implying a lower cost of equity capital. Our results specifically hold when controlling for effects like self-selection, cross-listing, and New Market (Neuer Markt) listing. Based on these accounting anomalies we can develop a novel multi-factor model that captures the “accounting premium” and leads to an improvement of the CAPM and Fama–French model. The paper contends that the adoption of IAAP could have direct and indirect effects on the cost of equity capital. The indirect effects via improving earnings quality or disclosure levels as well as lowering information asymmetry have been widely examined. However, the impacts and interrelations of these effects are difficult to separate. To capture direct effects, like additional costs or impact on brand recognition, a study must focus on the entire link between the adoption of IAAP and the cost of equity capital. Our study provides a comprehensive examination of the indirect and direct effects of this link on the cost of equity capital. For the empirical analysis, we use both a portfolio-based and a firm-level analysis. For the portfolio view we apply a Capital Asset Pricing Model (CAPM) and an enhanced multi-factor model to include the information about the type of accounting regime applied as an additional factor. Even though Francis, LaFond, Olsson, and Schipper (2005), Ecker, Francis, Kim, Olsson, and Schipper (2006), and Barth, Konchitchki, and Landsman (2007) have worked with factor-mimicking portfolios and new information risk-related factors, to the authors' knowledge, so far no study has assessed the impact of the type of accounting regime applied to an asset pricing model like the multi-factor analysis based on the Fama–French three factor model (Fama & French, 1993). Our combination of multi-factor regression and accountingregime factors leads to a novel approach to studying the question of whether or not different accounting regimes justify empirically significant differences in excess returns in an asset pricing model. For the firm-level analysis we incorporate a two-stage estimation procedure in which we are able to address explicitly the issue of self-selection. Moreover, we control for cross-listing effects and New Market (Neuer Markt) membership. 2

The International Financial Reporting Standards (IFRS) were initially called International Accounting Standards (IAS). In 2002, the name was changed International Financial Reporting Standards (IFRS). In general, we use a combination of both terms (“IAS/IFRS”). When we refer to a specific time period, we use the term “IAS” for years before (including) 2002 and the term “IFRS” afterwards.

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This study examines the major objectives many German companies have for voluntarily adopting an IAAP. By substituting the domestic accounting regime and, therefore improving the transparency of financial reporting, German companies expect to lower the cost of equity capital. Our study tests this notion by analyzing the cost of equity capital effect of adopting an IAAP. This paper makes several contributions to the existing literature. First, we thoroughly examine the effects of adopting a new accounting regime both theoretically and for our specific setting. In doing so, we respond to Holthausen's (2003) call for research “determining the marginal effects of accounting standards, incentives, ownership structure, institutional features of the capital markets and enforcement on the quality of financial reporting” (p. 273). Second, we develop a new method of comparing cost of equity capital for companies applying IAAP. This method has the advantages of not being biased by the quality of analysts' forecasts (like residual income models) and of mitigating the problem of selfselection of companies adopting new accounting regimes. Consequently, our sample is substantially bigger and less biased compared to other studies (e.g., Leuz & Verrecchia, 2000), since we do not lose (small) companies that are typically not followed by analysts. Third, we are the first to differentiate between subperiods of introducing IAAP in Germany. Our results support the expectation that the institutional changes over time distinctly influence the effect on the cost of equity capital. Fourth, although the classical Fama–French three-factor model has been successfully used in a variety of empirical studies (e.g., Fama & French, 1998; Liew & Vassalou, 2000), the German capital market has been relatively overlooked in such studies. Furthermore, previous studies have examined the Fama–French three-factor model across different countries, but to our knowledge this is the first study to consider different accounting regimes in a homogenous institutional setting. Our findings suggest that the type of accounting regime applied is a priced risk factor in the multi-factor model. Finally, we contribute to the literature comparing IAAP, like IAS/IFRS and U.S.-GAAP, to domestic GAAP. For our setting we find that the adoption of these accounting regimes is associated with reduced cost of equity capital. The remainder of this paper is organized as follows. In Section 2, we summarize related strands of the literature. In Section 3 we describe the institutional background of our study. Our hypotheses are developed in Section 4. In Section 5 we develop the research design. In Section 6 we present the data and descriptive statistics, followed by our econometric results in Section 7. Section 8 concludes with a summary of our results and discusses implications for future research. 2. Related literature 2.1. Information quality and cost of equity capital The link between information quality and cost of equity capital is one of the most fundamental and controversial subjects in recent accounting research. Theory suggests a negative relationship between the quality of (accounting) information, on the one hand, and the estimation risk and information asymmetry for investors, and, hence, the cost of equity capital, on the other hand (Habib, 2006).

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Also, the level of disclosures is considered to be essential for cost of equity capital. Diamond and Verrecchia (1991) argue that voluntary disclosures reduce information asymmetries among informed and uninformed investors and find that higher levels of disclosure reduce estimation risk. Assuming estimation risk as not being completely diversifiable, investors will require a return premium as compensation for additional risk components. This premium is interpreted as a higher cost of equity capital. Botosan (2006) thoroughly reviews the link between disclosure and cost of equity, asserting that “extent theory strongly supports the hypothesis that greater disclosure reduces cost of equity capital” (p. 39). But she also admits that the underlying assumption that public disclosure mitigates information asymmetry is not true for all studies, suggesting the need for additional research in this field. There are other critical voices regarding the assumption that public disclosure mitigates information asymmetry by displacing private information. Verrecchia (2001) misses an underlying theory and attests no unambiguous empirical evidence for a positive association between information quality and cost of equity capital. Kim and Verrecchia (1994) argue that public disclosure might be processed into private information again, also by informed investors. They state that more complex information might improve the quality of private information of informed investors even more than the information quality of public information for less informed investors (Kim & Verrecchia, 1991). As Hail and Leuz (2006) argue, the favorable effects of more disclosure are not predictable as they might be relatively small or (to a large extent) captured by traditional proxies of risk. Several theoretical studies argue that the size of the economy examined might influence the magnitude of these effects (Clarkson & Thompson, 1990; Coles, Loewenstein, & Suay, 1995; Clarkson, Guedes, & Thompson, 1996; Easley & O'Hara, 2004; Hughes, Liu, & Liu, 2007). Easley and O'Hara (2004) find that a higher proportion of private information increases cost of equity capital, whereas cost of equity capital is decreased by higher dispersion of private information and higher precision of private and public information. They see private information as inducing a new form of systematic risk and highlight that investors require compensation for that risk. They attest that “individual firms can influence cost of equity capital by choosing features like accounting treatments” (Easley & O'Hara, 2004, p. 1554). Hughes et al. (2007) work can be seen as an extension of Easley and O'Hara (2004). In contrast to their predecessors, they find that in large economies idiosyncratic risk is not priced. They call the fact that a large number of empirical studies presume information asymmetry is priced, because of having to trade with privately informed investors, “a commonly held misperception” (Hughes et al., 2007, p. 707). This price protection effect, also characterized in Easley and O'Hara (2004), is in fact driven by under-diversification and will disappear in large economies, in their opinion. Nevertheless, while controlling for total information, they show that high information asymmetry does lead to high cost of equity capital. In our study, we investigate the impact of the adoption of IAS/IFRS and U.S. GAAP by German companies from 1998–2004. We argue that the specific institutional setting in Germany and its changes over time call into question the positive impact, implicitly supporting a detailed analysis. Our results provide evidence a lower cost of equity capital for companies applying IAAP in Germany. However, the cost of equity capital effects of applying IAAP is different for the three subperiods examined. Moreover, we document that the type of accounting regime applied is a priced risk factor in our sample.

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2.2. Determinants and impacts of the adoption of internationally accepted accounting principles Various studies have addressed the determinants and impacts of the adoption of internationally accepted accounting principles (IAAP). One stream of research identifies attributes of companies which voluntarily change to IAAP (e.g., such analyses are included in Leuz & Verrecchia, 2000; Gassen & Sellhorn, 2006). The second stream of research investigates whether the adoption of IAS/IFRS and U.S. GAAP causes significant changes to financial statements. For German companies, Moya and Oliveras (2006) on average find statistically significant increases in equity, but less obvious effects on net income. Several other studies corroborate these results (Küting, Dürr, & Zwirner, 2002; Burger, Fröhlich, & Ulbrich, 2004; Burger, Schäfer, Ulbrich, & Zeimes, 2005; Burger, Feldrappe, & Ulbrich, 2006; Küting & Zwirner, 2007). A third stream of literature examines differences in earnings attributes and accruals between the accounting regimes. Whereas many studies find a higher earnings quality for IAS/IFRS companies compared to German GAAP companies in terms of certain measures, e.g., timeliness, predictability, conservatism, earnings management, value relevance, and analysts' forecast accuracy (Ashbaugh & Pincus, 2001; Barth, Landsman, & Lang, 2007; Bartov, Goldberg, & Kim, 2005), findings of other studies suggest similar results for both accounting regimes (Van Tendeloo & Vanstraelen, 2005; Goncharov, 2005). Some studies are inconclusive (Hung & Subramanyam, 2007) or even provide evidence for a higher earnings quality of German GAAP with reference to certain attributes (Gassen & Sellhorn, 2006). Comparing the earnings attributes of IAS/IFRS and U.S. GAAP, studies find a higher earnings quality for U.S. GAAP (Bartov et al., 2005; Barth, Landsman, Lang, & Williams, 2006; Goncharov & Zimmermann, 2006) or inconclusive results (Van der Meulen, Gaeremynck, & Willekens, 2007). Reasons for the mixed results of these “accounting quality studies” might be that the comparison of earnings attributes across accounting regimes could be biased and that the self-selection of companies applying different accounting regimes could confound the results. Moreover, these studies only focus on certain summary measures, neglecting additional information included in financial statements, e.g., the composition and presentation of assets or net income and the notes as well as other information instruments like cash flow statements. Finally, the implications that can be drawn from certain measures are debatable (e.g., see Holthausen & Watts, 2001, for value-relevance studies). A fourth stream of studies focuses on the capital-market effects of the adoption of international accounting regimes. To determine these effects, these studies rely on various measures, like abnormal returns (Auer, 1996, 1998) stock price volatility (Leuz & Verrecchia, 2000; Cuijpers & Buijink, 2005), bid-ask spreads (Leuz & Verrecchia, 2000; Leuz, 2003; Gassen & Sellhorn, 2006), percentage of trading days (Gassen & Sellhorn, 2006) or analystforecast based cost of equity capital measures (Cuijpers & Buijink, 2005; Daske, 2006; Daske, Hail, Leuz, & Verdi, 2007). No clear conclusions have been drawn from these studies concerning the capital-market impact of the adoption of IAS/IFRS or U.S. GAAP. Prior studies on the capital market effect of the adoption of IAAP in Germany rest upon very specific samples (Leuz & Verrecchia, 2000) or on other time periods (Daske, 2006). Moreover, all of these previous studies on the capital-market effects are conducted on a firm

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basis, do not highlight the specific institutional background of the country they examine, and fail to explore the development of the impact over time. Unlike these studies, we focus on a comprehensive sample of the entire voluntary-adoption period of IAAP in Germany between 1998 and 2004, shed light on the possible effects of the institutional setting and of changes in this setting as well as of accounting principles on the cost of equity capital impact and apply a new methodology of measuring the impact of adoption of IAAP in Germany. 2.3. Measurement of cost of equity capital A firm's cost of equity capital is usually defined as the expected return on a firm's stock (e.g. Lambert, Leuz, & Verrecchia, 2007). In other words, cost of equity capital is the minimum rate of return investors require to provide equity capital to the firm (Botosan, 2006). Researchers have suggested and applied a variety of means to measure cost of equity capital, each with specific advantages and drawbacks. Besides indirect measures or proxies (e.g., stock return volatility), researchers apply direct measures of cost of equity capital like residual income and discounted cash flow models (e.g. Gebhardt, Lee, & Swaminathan, 1999), the Capital Asset Pricing Model (CAPM) (e.g. Fama, & Macbeth, 1973), or multifactor models (e.g. Barth, Landsman et al., 2007). Many studies assume that the CAPM is descriptive and that market beta is a good proxy for non-diversifiable risk. If so, beta does include any estimation risk. However, by using historical data to proxy for expected market risk premiums, the CAPM treats estimated parameters as if they were true, ignoring estimation problems. Therefore, the overriding conclusion in the literature is that the CAPM is not descriptive, and theory suggests that market beta does not capture estimation risk. Investor's uncertainty is not taken into account (Botosan, 2006). The fundamental debate about estimation risk being diversifiable (not priced) or non-diversifiable (priced) is still ongoing, though. For example, one possible counter-argument is that information relevance declines with the degree of diversification in large populations (e.g., Cready & Gurun, 2007). In discounted-cash-flow models, cost of equity capital can be described as the riskadjusted discount rate that investors apply to the expected future cash flows in order to derive the current stock price. Implementations of these models are the Botosan and Plumlee (2002) model, based on the short-horizon form of the classic dividend growth model, as well as the Easton (2004) price-earnings growth-ratio model, based on the abnormal growth in earnings. Daske (2006) directly estimates the expected cost of equity-capital effects through the implied rate of return of a residual income model utilizing financial analysts' consensus earnings forecasts and stock prices. General shortcomings of all the discounted cash-flow models lie in determining the forecast horizon and the terminal value (Easton, 2006). Moreover, the use of analysts' forecasts has further disadvantages. Forecasts for firms that have changed from domestic principles to IAAP may tend to have a different degree of optimism than forecasts for firms that have not changed. These forecasts of the expected rate of return are generally likely to be higher than the real cost of equity capital (Easton, 2006). Generally, in all these models, analyst forecasts serve as proxies for market beliefs. One common criticism, however, is that this implies measurement errors, since analysts cannot perfectly reflect market beliefs. Consequently, these type of models regularly perform

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unsatisfactorily in tests of construct validity (e.g., Easton & Monahan, 2005). In addition, analysts typically only follow companies with a high visibility or market capitalization which might induce a selection bias (Francis, LaFond, Olsson, & Schipper, 2004). In the meanwhile, many researches have tried to extend the classical CAPM. The revolutionary work for today's research practice was the three-factor model suggested by Fama and French (1992, 1993), which rendered the classical CAPM obsolete. Based on factor-mimicking portfolios, they showed that the CAPM beta was not an effective or insightful model in their U.S. market studies. Instead, they introduced new regressors to indicate a value premium that compensates the risk missed by the CAPM: the “smallminus-big” factor (SMB), which represents the firms' size in terms of market capitalization, and the “high-minus-low” factor (HML) which stand for the ratio of book-to-market value. The factor-mimicking portfolio approach introduced by Fama and French was also applied to accounting oriented research. Francis, LaFond et al. (2005) create an accruals quality factor-mimicking portfolio (AQ factor) to estimate asset pricing regressions. Ecker et al. (2006) propose their “e-loadings” concept as returns-based representation of earnings quality. They view earnings quality as a measure of information risk and see information uncertainty as a non-diversifiable (priced) risk factor, gaining theoretical support from Easley and O'Hara (2004), as well as Leuz and Verrecchia (2005). In our study, we use both the CAPM and a factor-mimicking portfolio approach that incorporates the differences between the accounting regimes applied as a new factor in our model. This prevents measurement errors and selection bias which might be present in analyst-based cost-of-equity-capital estimates. To our knowledge, no other study has applied such a model to compare the effects of IAAP. 3. Institutional background 3.1. German financial reporting requirements In Germany, accounting principles and rules are not released by a private standard setter, but are enacted by the legislature and codified in the German Commercial Code (Handelsgesetzbuch, HGB). It is accompanied by standards and norms established by court decisions or by the reporting practice. German GAAP encompasses all codified and noncodified rules, standards, and norms a company has to observe when preparing financial statements (Leuz & Wüstemann, 2004). All German companies are required to provide individual financial statements of the legal entity according to German GAAP. These statements become the basis for determining distributable income, deriving taxable income, and other legal provisions (Haller & Eierle, 2004). In addition, parent companies having one or more subsidiaries are obliged to prepare consolidated financial statements. German GAAP was basically required in these statements until 2005. In the mid 90 s, German multinational companies started to apply IAS and U.S. GAAP3 due to a cross-listing in the United States or due to a perceived need for a more investor3

Thereby, companies adopted different reporting strategies, e.g., a parallel reporting, providing two full sets of financial statements or reconciliations of income and shareholders' equity (Leuz & Verrecchia, 2000).

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oriented reporting (Haller, 2002). Ultimately, this forced the German legislature to enact the Capital Raising Facilitation Act (Kapitalaufnahmeerleichterungsgesetz, KapAEG) which allows publicly listed companies to report consolidated financial statements according to IAAP, consequently substituting for the provisions of German GAAP (§ 292a HGB). However, companies preparing consolidated financial statements under U.S. GAAP in accordance with this option were generally not obliged to comply with the disclosure requirements of the SEC (Wüstemann, 2001) and were not subject to the enforcement of the SEC, unless they were cross-listed in the United States. Since the enactment of the Capital Raising Facilitation Act the number of listed companies in Germany exercising this option to adopt IAAP has increased. Moreover, the listing regulations of the New Market (Neuer Markt), a market segment of the German Stock Exchange for growth firms between 1997 and 2003, required companies to apply IAAP (Glaum & Street, 2003). Since 2005, all publicly traded European companies (including those in Germany) are required to prepare consolidated accounts under IFRS according to the IAS Regulation EC No. 1606/2002 (with a few exceptions).4 Due to the so called “member state options” of the IAS Regulation, the German legislature has allowed companies to provide additional individual accounts under IFRS (besides the individual accounts under German GAAP) for publication purposes and has passed the option to apply IFRS for consolidated accounts to all non-publicly traded companies. 3.2. Accounting standards under investigation Fundamental differences exist between general properties of German GAAP and IAAP. First, IAAP are developed by a private standard-setting body within a specified due process, whereas in Germany the parliament owns the standard-setting authority for accounting rules. Even though in 1998 the German Accounting Standards Board (GASB) was founded, the private-sector standard-setting power of this board is still restricted to developing recommendations for consolidated financial statements and non-compliance with these recommendations is not sanctioned (Sellhorn & Gornik-Tomaszewski, 2006). Second, German GAAP is more strongly principles based and offers more explicit choices (e.g., for the treatment of goodwill) than the IAAP. However, until recently, important areas (e.g., stock options) were not (sufficiently) covered by standards and/or interpretations under IAS/IFRS. Furthermore, some provisions of IAS/IFRS and U.S. GAAP are far more complex in comparison to German GAAP. For example, the revenue recognition according to the percentage of completion method is more complex than the completed-contract method, the treatment of actuarial gains and losses from pension obligations is more complex than the general rule to recognize such adjustments at once, and the impairment test (especially for cash-generating units) is more complex than a simple write-down to the replacement costs. However, it has to be taken into account that in 4

Companies publicly traded both in the European Union and on a regulated third-country market and which are therefore, applying another internationally accepted accounting system (especially U.S. GAAP) in their consolidated accounts are allowed to defer the application of IFRS until 2007. This also holds for companies which only have publicly traded debt securities.

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certain areas, like revenue recognition issues, German GAAP is likely to become complex as well when tax law, court decisions, and particular standards (like GAS, which can be at least factually binding) have to be considered. Third, German GAAP is – in contrast to the other accounting regimes investigated – considerably influenced by tax considerations. Consolidated financial statements following German GAAP are derived from individual financial statements, which are closely tied to the tax accounts and serve as the basis for determining dividend restrictions. Due to the socalled “congruency principle” or “authoritativeness principle” (Maßgeblichkeitsprinzip) the determination of accounting income and taxable income are directly interrelated (Pfaff & Schröer, 1996). Primarily, this principle has an impact on the individual accounts of companies. Until 2002, it was also possible to include tax-induced accounting practices into consolidated accounts. In 2002, however, the Transparency Act abolished this option. Fourth, under IAAP several items of income or expense are recognized directly in equity (e.g., foreign currency translations SFAS 52.13/SFAS 52.20/SFAS 52.46; IAS 21.32/21.37/ 21.39(c)/21.45; cash flow hedges SFAS 133.18(c), IAS 39.95; revaluations of available-for-sale financial assets SFAS 115.13/115.15/115.16, IAS 39.51/39.55/IAS 39.57) and thus two different performance measures are defined (i.e., net income and comprehensive income). Under German GAAP only one item of income or expense (i.e. foreign currency translations) is recognized directly in equity, leading to differences in the adherence to the clean-surplus relation. Finally, U.S. GAAP and IAS/IFRS clearly focus on providing an undistorted picture of the financial position of a company. Yet, German GAAP aims at investor protection and is largely biased by the “principle of prudence.” This divergence in objectives leads to different recognition, measurement, and disclosure provisions. Table 1 summarizes the most important differences. Several changes in the accounting regimes have occurred in the last decade. Table 2 gives an overview of changes in U.S. GAAP, IAS/IFRS, and German GAAP from 1998 until 2005. It is difficult or perhaps impossible to determine the impact of these changes on the quality of financial statements and on the cost of equity capital for individual companies. By focusing on both the quantity and quality of the revision of standards or the issuance of new standards, we determine two crucial points in time where a major change of at least one investigated accounting regime has occurred. First, in 1998 U.S. GAAP introduced a new standard for the disclosure of comprehensive income and under IAS, a revision of IAS 1, and German GAAP new requirements for cash flow statements and segment reports became effective. Second, in 2000, new provisions for derivatives and hedge accounting under U.S. GAAP and IAS 36–IAS 39, as well as a revision of IAS 16, became effective. Third, the new standard for goodwill and six other standards became effective under U.S. GAAP, and under German GAAP the option to include tax-induced accounting practices into consolidated accounts was abolished. Finally, in 2005 the so-called “Improvements Project,” which changed 13 standards, revised two others and introduced four new ones became effective under IFRS, and the Accounting Reform Act brought several revisions and new requirements for German GAAP. 3.3. German corporate governance and enforcement system The corporate governance system in Germany is fundamentally different from the AngloSaxon system. These differences are due to different legal systems or cultural peculiarities.

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U.S. GAAP 1. Recognition Goodwill Self-generated intangible assets Derivatives Lease agreements Deferred tax assets/ liabilities Revenues from (long-term) construction contracts

IFRS

German GAAP

Recognition of goodwill is mandatory (SFAS 141.43; IFRS 3.51) Basically no capitalization of development costs for self-generated intangible assets (SFAS 2, 86, 142, SOP 98-5, 98-1)

Option to recognize it or to offset it directly against retained earnings (§ 309 I S. 3 HGB) Requires the capitalization of development No capitalization of costs for self-generated costs for self-generated intangible assets intangible assets allowed (§ 248 II HGB) when certain prerequisites are fulfilled (IAS 38, SIC-32) Recognition of derivatives (SFAS 133,155; Recognition only when costs of acquisition have IAS 39) occurred Lease agreements are more often classified as Classified with reference to specific tax rules finance leases (SFAS 13, 28, 29, 66, 89 IAS 17, and relatively often classified as operating SIC-15, SIC-2, IFRIC 4). leases (tax law is relevant) Temporary concept is relevant for the recognition Timing concept is relevant for the recognition (SFAS 109; IAS 12) (§ 274 and 306 HGB) Percentage of completion method (ARB 45, SOP 81-1; IAS 11)

Basically no recognition before the goods or services have been finished (§ 252 I No. 4 HGB)

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Table 1 Major differences between U.S. GAAP, IFRS, and German GAAP

2. Measurement Revaluation of Fair value-based measurement of certain financial assets assets is required (SFAS 142, 144)

Pension liabilities

No upward revaluation to fair value above historical cost is allowed (§ 253 I HGB)

A full-cost approach is mandatory (SFAS 151, ARB 43; IAS 2)

Provides an option to use a value between and including direct cost and full cost (§ 255 II HGB) Refers to tax rules and thus uses tax determined measurement methods (interest rate usually 6% and no anticipation of future salary increases); actuarial gains and losses are usually recognized at once

Projected unit credit using a market-based interest rate and consideration of future salary increases; different options for treating actuarial gains and losses from pension obligations (SFAS 86, IAS 19)

3. Presentation and disclosures Presentation of No standardized presentation format, only certain items No standardized presentation format, balance that have to be included (SFAS 6, 109) [Additional only certain items that have to be sheet and requirements by the SEC for companies listed in the included (IAS 1) income United States] statement Notes Numerous explanatory notes

Depreciation charges are often determined with reference to the tax rules. Fixed assets are valued at the lower replacement cost when the write-off is estimated to be permanent (§ 253 II HGB)

Highly standardized presentation formats (§§ 266, 275 HGB)

Rather low level of disclosure

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Depreciation and impairment of fixed assets Inventories

Fair value-based measurement of certain financial assets is required and of fixed as well as intangible assets is allowed (IAS 16, 36, 38, 40) Depreciation charges are determined by the judgement of the management; an impairment test most often based on a reporting unit (cash-generating unit) is required under certain circumstances (SFAS 142, 144; IAS 36).

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Table 2 Major changes in U.S. GAAP, IFRS, and German GAAP 1998–2005 U.S. GAAP

2. Changes effective for fiscal period 1999 SFAS 134 Accounting for Mortgage-Backed Securities Retained after the Securitization of Mortgage Loans Held for Sale by a Mortgage Banking Enterprise Issued: October 1998; Effective date: for the first fiscal quarter beginning after December 15, 1998 SFAS 135 Rescission of FASB Statement No. 75 and Technical Corrections Issued: February 1999; Effective date: for financial statements issued for fiscal years ending after February 15, 1999

3. Changes effective for fiscal period 2000 SFAS 133 Accounting for Derivative Instruments and Hedging Activities Issued: June 1998; Effective date: Deferred to all fiscal quarters of all fiscal years beginning after June 15, 2000 by FAS 137

German GAAP

IAS 1 (rev. 1997) Presentation of Financial Statements Issued: August, 1997; Effective date: for fiscal years beginning on or after July 1, 1998

Law for the Strengthening of Control and Transparency (KonTraG) Issued: April 30, 1998; Effective date: for fiscal years ending after December 31, 1998: additional requirements for the management report (Lagebericht), e.g.: description of major risks (§ 315 I HGB), segment reporting and cash flow statement mandatory in the notes for publicly listed companies (§ 297 I HGB)

IAS 33 (1997) Earnings per Share Issued: February, 1997; Effective date: for fiscal years beginning on or after January 1, 1999 IAS 17 (rev. 1997) Leases Issued: December, 1997; Effective date: for fiscal years beginning on or after January 1, 1999 IAS 19 (rev. 1998) Employee Benefits Issued: February, 1998; Effective date: for fiscal years beginning on or after January 1, 1999

IAS 36 (1998) Impairment of assets Issued: June, 1998; Effective date: for fiscal years beginning on or after July 1, 1999

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1. Changes effective for fiscal period 1998 SFAS 130 Reporting Comprehensive Income Issued: June 1997; Effective date: for fiscal years beginning after December 15, 1997 SFAS 131 Disclosures about Segments of an Enterprise and Related Information Issued: June 1997; Effective date: for fiscal years beginning after December 15, 1997 SFAS 132 Employers' Disclosures about Pensions and Other Postretirement Benefits Issued: February 1998; Effective date: for fiscal years beginning after December 15, 1997

IFRS

4. Changes effective for fiscal period 2001 SFAS 139 Rescission of FASB Statement No. 53 and amendments to FASB Statements No. 63, 89, and 121 Issued: June 2000; Effective date: for financial statements for fiscal years beginning after December 15, 2000 SFAS 140 Accounting for Transfers and Servicing of Financial Assets and Extinguishments of Liabilities — a replacement of FASB Statement 125 Issued: September 2000; Effective date: for transfers and servicing of financial assets and extinguishments of liabilities occurring after March 31, 2001 and for disclosures relating to securitization transactions and collateral for fiscal years after December 15, 2000

IAS 38 (1998) Intangible assets Issued: September, 1998; Effective date: for fiscal years beginning on or after July 1, 1999 J. Ernstberger, O. Vogler / The International Journal of Accounting 43 (2008) 339–386

SFAS 137 Accounting for Derivative Instruments and Hedging Activities — Deferral of the Effective Date of FASB Statement No. 133 Issued: June 1999; Effective date: June 1999 SFAS 138 Accounting for Certain Derivative Instruments and Certain Hedging Activities Issued: June 2000; Effective date: for all fiscal quarters of all fiscal years beginning after June 15, 2000

IAS 39 (rev. 2000) Financial Instruments: Recognition and Measurement Issued: October, 2000; Effective date: for fiscal years beginning on or after January 1, 2000 IAS 16 (rev. 1998) Property, Plant and Equipment Issued: 1998; Effective date: for fiscal years beginning on or after July 1, 1999 IAS 37 (1999) Provisions, Contingent Liabilities and Contingent Assets Issued: July 1, 1999; Effective date: for fiscal years beginning on or after July 1, 1999 IAS 10 (rev. 1999) Events after the Balance Sheet Date Issued: May, 1999; Effective date: for fiscal years beginning on or after January 1, 2000

IAS 40 (2000) Investment Property Issued: April, 2000; Effective date: for fiscal years beginning on or after January 1, 2001

IAS 28 (rev. 1998) Investments in Associates Issued: December, 1998 (revised by IAS 39); Effective date: for fiscal years beginning on or after January 1, 2001 IAS 31 (rev. 1998) Interests in Joint Ventures Issued: December, 1998 (revised by IAS 39); Effective date: for fiscal years beginning on or after January 1, 2001 (continued on next page)

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Table 2 (continued) IFRS

SFAS 141 Business Combinations Issued: June 2001; Effective date: for all business combinations initiated after June 30, 2001

IAS 32 (rev. 1998) Financial Instruments: Presentation Issued: December, 1998 (revised by IAS 39); Effective date: for fiscal years beginning on or after January 1, 2001 IAS 19 (rev. 2000) Employee Benefits Issued: October, 2000; Effective date: for fiscal years beginning on or after January 1, 2001

5. Changes effective for fiscal period 2002 SFAS 142 Goodwill and Other Intangible Assets Issued: June 2001; Effective date: for fiscal years beginning after December 31, 2001; goodwill acquired in business combinations after June 30, 2001, shall not be amortized SFAS 143 Accounting for Asset Retirement Obligations Issued: June 2001; Effective date: for fiscal years beginning after June 15, 2002 SFAS 144 Accounting for the Impairment or Disposal of Long-lived Assets Issued: August 2001; Effective date: for financial statements issued for fiscal years beginning after December 15, 2001 and interim periods within those fiscal years SFAS 145 Rescission of FASB Statements No. 4, 44, and 64, Amendment of FASB Statement No. 13, and Technical Corrections Issued: April 2002; Effective date: for financial statements issued on or after May 15, 2002 SFAS 146 Accounting for Costs Associated with Exit or Disposal Activities Issued: June 2002; Effective date: for exit or disposal activities initiated after December 31, 2002

IAS 19 (rev. 2002) Employee Benefits Issued: May, 2002; Effective date: for fiscal years beginning on or after May 31, 2002

German GAAP

Transparency Act (TransPuG) Issued: July 19, 2002; Effective date: for fiscal years ending after December 31, 2001 (some rules, others for years ending after December 31, 2002): disclosure about the compliance with the Corporate Governance Code (§ 161 AktG), segment reporting, cash flow statement and statement of changes in equity mandatory for publicly traded companies as separate statements (§ 297 I 2 HGB), option to include tax-induced accounting practices into the consolidated accounts is abolished (§ 308 III HGB was deleted)

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U.S. GAAP

IAS 41 (2000) Agriculture Issued: December, 2000; Effective date: for fiscal years beginning on or after January 1, 2003

(continued on next page)

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6. Changes effective for fiscal period 2003 SFAS 149 Amendment of Statement 133 on Derivative Instruments and Hedging Activities Issued: April 2003; Effective date: for contracts entered into or modified after June 30, 2003; for hedging relationships designated after June 30, 2003; for provisions that relate to Statement 133 Implementation Issues that have been effective for fiscal quarters that began prior to June 15, 2003, apply in accordance with their respective effective dates; for paragraphs 7(a) and 23(a), apply to both existing contracts and new contracts entered into after June 30, 2003 SFAS 150 Accounting for Certain Financial Instruments with Characteristics of both Liabilities and Equity Issued: May 2003; Effective date: for financial instruments entered into or modified after May 31, 2003; otherwise effective at the beginning of the first interim period beginning after June 15, 2003, except for mandatorily redeemable financial instruments of nonpublic entities, which are subject to the provisions of this Statement for the first fiscal period beginning after December 15, 2003

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SFAS 147 Acquisitions of Certain Financial Institutions Issued: October 2002; Effective date: for acqusitions on or after October 1, 2002 SFAS 148 Accounting for Stock-Based Compensation — Transition and Disclosure Issued: December 2002; Effective date: for fiscal years ending after December 15, 2002, for transition guidance and annual disclosure provisions; for financial reports containing financial statements for interim periods beginning after December 15, 2002, for interim disclosure provisions

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U.S. GAAP

IFRS

SFAS 132 (revised 2003) Employers' Disclosures about Pensions and Other Postretirement Benefits Issued: December 2003; Effective date: for domestic plans, for all new provisions except for estimated future-benefit-payments disclosures, effective for fiscal years ending after December 15, 2003; for foreign plans and nonpublic entities, for all new provisions, and for estimated future-benefit-payments disclosures for all entities, effective for fiscal years ending after June 15, 2004; and for interim-period disclosures, effective for quarters beginning after December 15, 2003 7. Changes effective for fiscal period 2004 IFRS 1 (2003) First-time Adoption of International Financial Reporting Standards Issued: June 19, 2003; Effective date: for fiscal years beginning on or after January 1, 2004 IAS 38 (rev. 2004) Impairment of assets Issued: March 31, 2004; Effective date: for fiscal years beginning on or after April 1, 2004

German GAAP

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Table 2 (continued)

IAS 32 (rev. 2003) Financial Instruments: Presentation Issued: December 17, 2003; Effective date: for fiscal years beginning on or after January 1, 2005 Improvements Project: IAS 1, 2, 8, 10, 16, 17, 21, 24, 27, 28, 31, 33, 40, (all rev. 2003) Issued: December 18, 2003; Effective date: for fiscal years beginning on or after January 1, 2005 IFRS 2 (2004) Share-based Payment Issued: March 31, 2004; Effective date: for fiscal years beginning on or after January 1, 2005 IFRS 3 (2004) Business combinations Issued: March 31, 2004; Effective date: for fiscal years beginning on or after January 1, 2005 IFRS 4 (2004) Insurance contracts Issued: March 31, 2004; Effective date: for fiscal years beginning on or after January 1, 2005 IFRS 5 (2004) Non-current Assets Held for Sale and Discontinued Operations Issued: March 31, 2004; Effective date: for fiscal years beginning on or after January 1, 2005 IAS 39 (rev. 2004) Financial Instruments: Recognition and Measurement Issued: 2004; Effective date: for fiscal years beginning on or after January 1, 2005

Accounting Reform Law (BilReG) Issued: December 9, 2004; Effective date: for fiscal years ending after December 31, 2004: disclosures about auditor fees for publicly traded companies (§ 314 No. 9 HGB), additional disclosures for financial instruments (§ 314 No. 10 and 11 HGB), additional requirements for the management report (Lagebericht), e.g. description of major prospects and risks using several performance measures as well as hedging and risk-management strategies (§ 289 I and II HGB), abolition of certain options concerning the consolidation of companies (§ 295 HGB was deleted), cash flow statement and statement of changes in equity mandatory for all companies as separate statements and only voluntary segment reporting (§ 297 I HGB)

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8. Changes effective for fiscal period 2005 SFAS 151 Inventory Costs Issued: November 2004; Effective date: for inventory costs incurred during fiscal years beginning after June 15, 2005 SFAS 152 Accounting for Real Estate Time-Sharing Transactions — an amendment of FASB Statements No. 66 and 67 Issued: December 2004; Effective date: for financial statements for fiscal years beginning after June 15, 2005

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Essentially, Germany has a civil or code law system in contrast to the common law system in the United States (e.g., Haller & Walton, 2003) and it is characterized by a relatively high degree of uncertainty avoidance as well as collectivism in comparison to other countries (Hofstede, 1984). The corporate governance system of a German joint-stock corporation, which is the legal structure of nearly all listed companies,5 is often characterized as being insider-controlled and stakeholder-oriented (Schmidt, 2004). The joint-stock corporations have a two-tier system with a management board (Vorstand) for the executive management of the company and a separate supervisory board (Aufsichtsrat) for the overseeing of the management board.6 As several different stakeholder groups are represented in the supervisory board, the German governance system is often characterized as stakeholder-orientated, where internal control mechanisms play a central role (Franks & Mayer, 1997; Hackethal, Schmidt, & Tyrell, 2005). Until 2005, besides statutory auditors, no external enforcement mechanism for overseeing the compliance of companies with accounting standards had been in place. Auditors published a short audit report to the public and provided a long audit report to the supervisory board. Based on this report, the supervisory board assessed the compliance of the financial statements with the accounting rules and the appropriateness of the accounting policies applied (Naumann, 2000). During the last decade, several legal changes have affected these corporate-governance mechanisms. The monitoring of the management board by the supervisory board has been improved by the Law for the Strengthening of Control and Transparency (Kontroll-und Tansparenzgesetz, KonTraG) (Nietsch, 2005) in 1998 and the Transparency Act (Transparenz-und Publizitätsgesetz, TransPuG) in 2002. The KonTraG included audit reforms changing the objective of the audit as well as the reporting requirements and the legal liability for auditors. These reforms increased the monitoring role of audits in Germany (Gassen & Skaife, 2007). Moreover, due to an amendment of the law for commercial stock companies, the compulsory establishment of risk-management systems was required (§ 91 II of the Stock Corporation Act, Aktiengesetz, AktG). A corporate governance code implemented in 2002 provides standards of best practice (Nietsch, 2005; Noack & Zetzsche, 2005). In § 161 AktG an obligation to “comply-orexplain” was included for listed companies which should facilitate the acceptance of these standards. However, some argue that these reforms have brought no structural change for the governance mechanisms described above (Nietsch, 2005) or that can rather be seen as a “marketing instrument” which should increase the attractiveness of German companies' shares to international investors (Noack & Zetzsche, 2005). Two important reforms have had an impact on the enforcement system. First, the Accounting Reform Law of 2004 (Bilanzrechtsreformgesetz, BilReG) implemented certain measures strengthening the independence of statutory auditors and modified the audit report (§§ 318–322 HGB) and the Auditor Oversight Act (Abschlussprüferaufsichtsgesetz, APAG) established the 5 Some companies are partnerships limited by shares (Kommaditgesellschaft auf Aktien, KGaA) and having at least one personally liable partner, e.g., Henkel KGaA or Merck KGaA. In 2004 the European Company (Societas Europea, SE) was introduced as a legal structure for German companies by the legislature. In 2006 and 2007, respectively, Allianz and Fresenius were the first companies to adopt this legal structure in Germany. 6 The SE provides companies an option to establish a one-tier or two-tier board system (Noack & Zetzsche, 2005).

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Auditor Oversight Commission (Abschlussprüferaufsichtskommission, APAK) for overseeing the statutory auditors (Haller, Ernsberger, & Kraus, 2006). Second, an external enforcement system was established by the Accounting Law Control Act of 2004 (Bilanzkontrollgesetz, BilKoG) which included §§ 342b–342d HGB into the German Commercial Code. This twostep system comprises a privately organized enforcement body called Financial Reporting Enforcement Panel (Deutsche Prüfstelle für Rechnungslegung, DPR) and a state authority called Supervisory Authority for Financial Services Institutions (Bundesanstalt für Finanzdienstleistungsaufsicht, BaFin) (Delvaille et al., 2005; Noack & Zetzsche, 2005). 3.4. Germany's capital market Only a small proportion of German companies is publicly listed on a stock exchange. While there are about one million limited liability companies in Germany, only 15,000 stock corporations are registered, from which approximately 1000 are listed on regulated markets. Most companies in Germany, especially the smaller ones are held privately (Noack & Zetzsche, 2005). Traditionally, the German capital market is often seen as bank-based (Baetge et al., 1995; Haller & Walton, 2003; Vitols, 2005). A major part of debt and also equity financing is provided by a few, dominant, universal banks, the so-called “house banks” (Hausbanken) (Elsas & Krahnen, 2003). Besides being major creditors of companies, banks also hold large stakes of the companies' equity, can increase their influence by acting as proxies for their clients using depositary voting rights, and play a key role in the internal corporate governance of companies (Fohlin, 2005). Moreover, there are many cross-holdings between publicly listed companies (Schilling, 2001), leading to a high ownership concentration in comparison to other countries (Hackethal et al., 2005; Enriques & Volpin, 2007). Being a typical bank-based system, households asset are largely held as bank deposits and not as investments in shares (Vitols, 2005). Since the foundation of the New Market in 1997, the role of equity in the financing of companies has become more important (e.g., the number of IPOs has increased) and the number of stockholders has increased (Vitols, 2005). After the burst of the capital market bubble in 2001, the public interest in stock investments has declined. In particular, since the tax reform in 2000, banks have decreased their large equity stakes in listed companies and, thus, their influence (Vitols, 2005; Hackethal et al., 2005). However, to a certain extent, insurance companies have replaced banks in their role as dominant shareholders of German companies (Vitols, 2005). As a result, raising equity is still less important for the external financing of companies and shareholdings of households are still significantly lower than is the case in other countries. Consequently, the general capital market situation in the German capital market has not changed structurally so far (Hackethal, 2004; Vitols, 2005) and the financial system can still be regarded as bank-based (Hackethal et al., 2005). 4. Hypotheses development 4.1. Overall impact of adopting IAAP We investigate the impact of a voluntary adoption of IFRS or U.S. GAAP by German companies in the entire period examined (1998-2004) as well as in several subperiods. The

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relation between applying a specific accounting regime and the cost of equity capital is complex and influenced by several factors. Lambert et al. (2007) argue that information provided to investors might have both an indirect and a direct effect on the cost of equity capital. Using similar reasoning, the adoption of IAAP could have an indirect impact on the cost of equity capital by improving the quality and quantity of information, lowering information asymmetry (Gassen & Sellhorn 2006), improving the liquidity of a company's shares by enlarging the investor base (Merton, 1987; Covrig, Defond, & Hung, 2006), and, finally, lowering the compensation required by uninformed investors in terms of returns, which means a lower cost of equity capital (Easley & O'Hara, 2004). In addition, adopting IAAP may impact cash flows directly. On the one hand, those could be negative due to the costs of adoption as well as the application of the more complex IAAP. On the other hand, positive impacts could emerge because the adoption of IAAP could improve brand recognition which might lead to the recruiting of international employees, enhance international co-operations or acquisitions, and foster the implementation of value-based management systems (Weißenberger, Stahl, & Vorstius, 2004). Three major problems impede evaluating these effects separately. First, influential factors, like the incentives of managers and auditors (Ball et al., 2003; Ewert & Wagenhofer, 2005; Gassen & Sellhorn, 2006), the expertise and capabilities of managers, auditors and users of financial statements, other institutional settings like corporate governance or enforcement and the importance or the integration of the capital market (Hail & Leuz, 2006) could have diverse impacts on the (indirect and direct) relations between standards and the cost of equity capital for the different accounting regimes. Especially, the low rate of listed companies in Germany in comparison to other countries and the small percentage of people holding shares might decrease opportunities for diversification and, therefore, influences the pricing of estimation risk. This is particularly true when a strong home-bias towards domestic stocks prevails. For Germany, such a bias is found in several studies (e.g., Tesar & Werner, 1995; Kilka & Weber, 2000). In contrast, the integration of the stock markets in Europe and even worldwide could mitigate this effect (Harvey, 1991). Second, these effects might interact with each other (Gietzmann & Trombetta, 2003) and with the factors explained, which hampers their exploration. For example, the interaction of accounting standards and of accounting practice is difficult or rather impossible to disentangle (Schipper, 2005; Sellhorn & Gornik-Tomaszewski, 2006). Third, concepts like earnings quality, disclosure level, or information asymmetry are unobservable and thus have to be measured by proxies (e.g., earnings quality by persistence, predictability, conservatism, timeliness, discretionary accruals, or value relevance). Moreover, these concepts disregard the direct cash-flow impacts of adopting IAAP. This makes it difficult to unambiguously investigate the overall impact of adopting IAAP. We assume that the cost of equity capital is an important objective for adopting IAAP. Several survey studies document this motive for adopting IAAP (Pellens & Tomaszewski, 1999; Weißenberger et al., 2004). Also, the EU and standard-setters like the FASB aim at lowering the cost of equity capital when deciding what accounting standards should be applied. Moreover, only the cost of equity capital is able to capture indirect and direct effects which pertain to the adoption of IAAP. Thus, we investigate the overall link between the adoption of IFRS or U.S. GAAP and the cost of equity capital.

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The impact of adopting IAAP in Germany is difficult to predict. The application of IAAP should reflect the performance of a company in a more timely way and with a lower degree of conservatism. The higher extent of value-relevant items recognized especially under IAS/IFRS (e.g., intangible assets) and the more timely and less conservative measurement of items under IAS/IFRS and U.S. GAAP (e.g. at fair value) should improve the ability of investors to predict future cash flows and thus lower estimation risk. The higher extent of explanatory notes and of additional disclosures required by IAS/IFRS and U.S. GAAP in comparison to German GAAP should, all else being equal, lower the degree of information asymmetry. A company's decision to switch to IAAP could be regarded as a strong commitment to increased disclosure because it is very costly to reverse (Leuz & Verrecchia, 2000; Daske, 2006). As IAS/IFRS and U.S. GAAP are explicitly directed at investors, the adoption of these accounting regimes in Germany should cause an exchange of private information granted to certain stakeholders represented on the supervisory board for public information (Daske, 2006). This should have a decreasing effect on the degree of asymmetric information. However, Francis, Khurana, and Pereira (2005) argue that the need for public information in bank-based financial systems like Germany is lower than in market-based systems and that weak investor protection might impair the credibility of the information provided by IAS/IFRS or U.S. GAAP. Therefore, these effects are expected to be favorable for IAAP, but could turn out to be relatively low in Germany. In contrast, previous studies find that principles-based accounting standards lead to a higher earnings quality (Webster & Thornton, 2005), which suggests, all else equal, a higher earnings quality of German GAAP and partly IAS/IFRS in comparison to U.S. GAAP. The reduced comparability of financial statements under IAS/IFRS and U.S. GAAP due to missing specification of a reporting format for the income statement and balance sheet could deteriorate the quality of information about German companies available to investors. In addition, the adoption of IAS/IFRS or U.S. GAAP might only represent the use of a label without resulting in a material change of the transparency of financial statements (Daske, Hail, Leuz, & Verdi, 2007). This might hold particularly in countries like Germany where individual shareholders have less influence on the governance of the management and managers have more room for pursuing their interests (Ball, 2006). Furthermore, the enforcement of IAS/IFRS or U.S. GAAP financial statements might have been more difficult for the statutory auditors and the supervisory boards especially in the first years of adoption because they lacked sufficient expertise in the new accounting regimes. Glaum and Street (2003) provide empirical evidence on this topic. Following the arguments of Kim and Verrecchia (1994), the adoption of the more complex accounting regimes, IFRS and U.S. GAAP, could even have increased the information asymmetry because informed investors are able to gain more insights than less-informed investors. This argument might hold particularly for non-institutional investors in Germany, as they rather neglect additional disclosures and focus only on the balance sheet and income sheet (Deutsches Aktieninstitut, 2005). However, even analysts might have difficulties in using IFRS or U.S. GAAP for earnings forecasts (Daske, 2005). A further disadvantage of the adoption of IAAP could be that the previous domestic standards have more effectively addressed the specific needs of financial statement users or have accommodated the particular legal or economic system of a country (Armstrong, Barth, Jagolinzer, & Riedl, 2007).

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Based on this discussion, it ultimately remains an empirical question whether over the examined period the adoption of IAS/IFRS and U.S. GAAP by German companies has decreased their cost of equity capital. We therefore state the following hypothesis: H1. In Germany, the cost of equity capital is higher for companies applying German GAAP than for companies applying IAS/IFRS or U.S. GAAP. 4.2. Impact of adopting IAAP in subperiods In a second analysis, we examine whether and how changes in accounting standards, as well as in the German capital market, corporate-governance system, and enforcement system could have an effect on the impact of applying IAAP on the cost of equity capital. As the description of the institutional background in the previous section shows, several changes have taken place during the sample period. It is likely that these changes will result in different effects of adopting IAS/IFRS or U.S. GAAP. As explained above, in the years 1998, 2000, 2002, and 2005 major revisions of accounting standards or new standards became effective. Moreover, the years 1998, 2002, and 2005 brought reforms for the corporate governance and enforcement systems in Germany. Consequently, we identify three relatively stable subperiods in the investigated period: (1) 1998–1999, (2) 2000–2001 and (3) 2002–2004. At the beginning of the first subperiod (1998–1999), several new disclosures and presentation requirements under IAS and U.S. GAAP became effective which might have a positive impact on the cost-of-equity capital effect of applying these two accounting regimes. Moreover, the capital markets in Germany became more popular especially to noninstitutional investors, which might have increased investor's demands for more useful decision information. This would ceteris paribus also imply a positive impact of an adoption of IAAP, because German GAAP was regarded as being less suitable for those purposes. In contrast, the IAS provided many options and had no standards for several important issues in this time period. Moreover, the compliance level of companies applying IAAP in Germany, especially in the first years of application, was low, since companies and auditors were not used to the new accounting regimes. In addition, many analysts and investors in Germany might not have been able to cope with the more complex provisions. In 2000, which is the beginning of the second subperiod, several new standards as well as revisions of key standards became effective under IAS, which restricted options or filled important gaps of missing rules. Furthermore, companies, investors, financial analysts, and auditors had become used to the provisions of IAAP and thus were able to exploit the higher degree of transparency. Various IPOs took place in this time period and the interest in investor-oriented accounting standards increased. In the third subperiod (2002–2004), the Transparency Act became effective, abolishing the option which was allowed under German GAAP to include tax-induced accounting practices into consolidated accounts. Under U.S. GAAP, the new provisions for measuring goodwill are rather complex and provide a considerable degree of discretion to managers. Concerning IFRS, a revised standard for employee benefits (IAS 19 (rev. 2002)) might have an impact on transparency and thus on the cost of equity capital. The credibility in

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accounting numbers, and especially in those under IAAP, was damaged due to some accounting scandals of companies listed in Germany's New Market that have adopted either IAS/IFRS or U.S. GAAP. Concerning the cost of equity capital effects of applying IAAP in the three subperiods presented, we state the following hypothesis: H2. The cost-of-equity-capital impact of applying IAS/IFRS or U.S. GAAP in comparison to that of applying German GAAP is different in the time periods 1998–1999, 2000–2001, and 2002–2004. 5. Research design 5.1. Portfolio analyses The starting point for our analysis is the classical CAPM. This model describes how the market return above risk free rate explains a stock or portfolio of stocks. The CAPM model is described by the following equation:   rit
rtrf = ai + bi rmt
rtrf + eit ð1Þ where rit represents the individual stock (or portfolio) i at time t, rtrf indicates the risk-free interest rate, and rmt the market return at time t. The Greek letters stand for the intercept αi, the slope parameter βi, and the residuum εit of each individual stock (or portfolio) i. However, for voluntary changes in the disclosure level, the CAPM results could be affected by self-selection bias. When variables like company characteristics explaining the decision of managers to change the disclosure level are omitted in the analysis and are correlated to certain priced risk factors, the results are biased (Hail, 2002). One way to mitigate this problem of self-selection is to include known risk factors like market capitalization and market-to-book value into the analysis (Berk, 1995). Therefore, we use an enhanced multi-factor model, gaining theoretical support from Francis et al. (2004) as well as Francis, LaFond et al. (2005), and control for these known priced risk factors. One assumption of the model is that there is an information factor in our sample which could not be diversified away and thus is priced. A second alternative for controlling a possible selfselection bias is a two-step regression approach, which is applied in the firm-level analyses in the next section. The multi-factor model we use in the following is based on the original Fama and French model, with the following multi-factor equation:   rit
rtrf = ai + bi1 rmt
rtrf + bi2 SMBt + bi3 HMLt + eit

ð2Þ

The first part of Eq. (2) is similar to Eq. (1), with the only difference being that the Greek slope parameters βij are now numbered from j = 1, 2, 3 for the three regressors (rmt − rtrf), SMBt, and HMLt. SMBt (“Small minus Big”) represents the size of the companies. HMLt stands for a factor based on the book-to-market ratio. Firms with high (low) book-to-market values are regarded as “value stocks” (“growth stocks”) (Fama & French, 1993).

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Similar to Fama and French (1993) we split our sample of companies into six portfolios: S-H S-M S-L B-H B-M B-L

“Small-High” (small size, high book-to-market ratio) “Small-Medium” (small size, medium book-to-market ratio) “Small-Low” (small size, low book-to-market ratio) “Big-High” (big size, high book-to-market ratio) “Big-Medium” (big size, medium book-to-market ratio) “Big-Low” (big size, low book-to-market ratio)

We rank our sample first according to the size in terms of market value. The median discriminates between the “small” and “big” firms. Then we rank our sample according to the book-to-market ratios and separate the highest 30% as “high”, the lowest 30% as “low,” and the resulting 40% as “medium.” The companies remain in one of the six portfolios for one year (starting in July, ending in June of the next calendar year). Reference date for the re-alignment of the portfolios is the end of June of the previous period for the size, and the end of December of the previous period for the book-to-market ratio. The monthly returns of the companies are averaged – weighted with their market value – in each of the six portfolios. We obtain: rS-H, rS-M, rS-L, rB-H, rB-M, rB-L. Eventually, we compute SMBt, and HMLt as follows:

SMBt = rS
H + rS
M + rS
L =3
rB
H + rB
M + rB
L =3

ð3Þ



HMLt = rS
H + rB
H =2
rS
L + rB
L =2

ð4Þ

With these factors, the influence of size on book-to-market is reduced and vice-versa. Based on the Fama and French approach (2), our new model (that we call “GM model”), is augmented with two new factors that represent the impact of the different accounting regimes:   rit
rtrf = ai + bi1 rmt
rtrf + bi2 SMBt + bi3 HMLt + bi4 GMIt + bi5 GMUt + eit ð5Þ The factors GMIt and GMUt explain the accounting regime impact. GMIt is the return difference between the portfolios of companies using German GAAP and IAS/IFRS (“German GAAP minus IAS/IFRS”) and GMUt is the return difference between German GAAP and U.S. GAAP (“German GAAP minus U.S. GAAP”) portfolios. For this analysis, we calculate the market value weighted means for each of the three accounting-regime groups and compute the difference for the “GM” factors as described for each month in the sample. As illustrated in Table 3, extending the number of portfolios according to the accounting regime results in: 18 factor-mimicking portfolios. We assign a company to a fiscal year as follows: The reporting date determined the June– July year used. Each June–July year is assigned to the accounting regime used in the reporting date of that year. For example, if a company's reporting date is December 31, 2000, the accounting regime applied at that point is assigned to the July 2000-to-June 2001 year.

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Table 3 18 analyzed portfolios Book-to-market

German GAAP Size

IAS/IFRS Size

U.S. GAAP Size

Low

Medium

High

Small Big

S-L-G B-L-G

S-M-G B-M-G

S-H-G B-H-G

Small Big

S-L-I B-L-I

S-M-I B-M-I

S-H-I B-H-I

Small Big

S-L-U B-L-U

S-M-U B-M-U

S-H-U B-H-U

The 18 portfolios are built upon three criteria: (1) accounting regime applied (G: German GAAP; I: IAS/IFRS; U: U.S. GAAP), where companies are assigned into the July-to-June year by the accounting regime they used at the reporting date within that period; (2) book-to-market value (L / M / H: Low / Medium / High), where the book value of equity is divided by the market value of equity; we rank our sample according to the book-to-market ratios and separate the highest 30% as “high.” the lowest 30% as “low,” and the resulting 40% as “medium”; (3) size in terms of market value of equity (S / B: Small / Big), where the median discriminates between the “small” and “big” firms. The companies remain in one of the 18 portfolios for one year (starting in July, ending in June of the next calendar year). Reference date for the re-alignment of the portfolios is the end of June of the previous period for the size, and the end of December of the previous period for the book-to-market ratio.

Within these 18 portfolios, we again construct the market value weighted average of the monthly returns. Ultimately, we compute our new factors as follows:
GMIt = rS
H
G + rS
M
G + rS
L
G + rB
H
G + rB
M
G + rB
L
G =6

rS
H
I + rS
M
I + rS
L
I + rB
H
I + rB
M
I + rB
L
I =6

ð6Þ


GMUt = rS
H
G + rS
M
G + rS
L
G + rB
H
G + rB
M
G + rB
L
G =6

rS
H
U + rS
M
U + rS
L
U + rB
H
U + rB
M
U + rB
L
U =6

ð7Þ

The idea of these factors is that, provided that these differences are significant, they help us to explain the performance of the returns of our 18 portfolios and account for an information factor related to the different disclosure levels among the three accounting regimes examined. Knowing the accounting regime of a portfolio should lead to greater explanatory power within the model. We apply the seemingly unrelated regression (SUR) technique. Since it would be unrealistic to expect the equation errors to be uncorrelated, this method explicitly allows us to analyze a system of multiple equations with cross-equation parameter restrictions and correlated error terms.7 7

As a robustness check, we also apply the ordinary least squares (OLS) method. The inference is even higher for OLS, but for the technical reasons mentioned we nevertheless incorporate the SUR method.

364

Mean

StdDev

Obs

N

Mean

StdDev

Obs

N

Mean

StdDev

Obs

N

G S B

L S-L-G B-L-G

−0.0072 0.0069

0.0672 0.0604

96 96

17.4 29.1

M S-M-G B-M-G

0.0045 0.0147

0.0423 0.0630

96 96

36.4 43.1

H S-H-G B-H-G

0.0095 0.0146†

0.0510 0.0561

96 96

43.0 22.6

I S B

L S-L-I B-L-I

−0.0180 0.0101

0.1275 0.0572

72 96

19.0 21.5

M S-M-I B-M-I

− 0.0042 0.0121

0.1204 0.0635

84 96

22.0 24.0

H S-H-I B-H-I

0.0136 0.0028†

0.1105 0.0773

84 84

21.7 17.6

U S B

L S-L-U B-L-U

−0.0205 0.0122†

0.1601 0.1227

84 96

12.3 11.4

M S-M-U B-M-U

0.0014 − 0.0039

0.1184 0.0684

72 96

14.0 11.8

H S-H-U B-H-U

0.0260 0.0119

0.0982 0.0979

60 72

10.0 6.5

The portfolio construction is described in Table 1. Table 2 shows the descriptive results of our analysis for the 18 portfolios. We have monthly return observations from July 1997 until June 2005. Mean: The monthly returns of the portfolios are averaged by using market value of equity weights of the companies. StdDev: standard deviation of the mean returns; Obs: observations = number of months data are available for the specific portfolio; N: average number of firms included for computing the mean/standard deviation per observed month. † “Value premium” exceptions: these values do not reflect the Fama and French “value premium”; Fama and French found in 1992 that returns grow with higher book-tomarket values.

J. Ernstberger, O. Vogler / The International Journal of Accounting 43 (2008) 339–386

Table 4 Descriptive analysis for all 18 portfolios: returns (July 1997–June 2005, max. 96 observations)

J. Ernstberger, O. Vogler / The International Journal of Accounting 43 (2008) 339–386

365

5.2. Firm-level analyses As already mentioned, dealing with voluntary adoption of IAAP can lead to a potential self-selection bias between the different accounting regime portfolios. One way of tackling this issue is to include an information-quality factor in the factor-mimicking portfolio analysis. But researchers have also successfully implemented another means of addressing the self-selection problem at the firm-level. By applying the two-stage procedure, proposed by Heckman (1978), we can control for self-selection by incorporating the Inverse Mills Ratio (see e.g. Leuz & Verrecchia, 2000; Gassen & Sellhorn, 2006; Hung & Subramanyam, 2007). In the first stage, we estimate a probit model to analyze the firms' probability of adopting IAS/IFRS or U.S. GAAP, given a variety of explaining factors: IAAPit = Probitðd0 + d1 logðMEit Þ + d2 ROAit + d3 CAPINTit + d4 MANUFit

ð8Þ

+ d5 NEWMARKETit + d6 USUKit + eit Þ where for firm i and time t IAAPit is a dummy variable for applying IAAP (i.e. IAS/IFRS or U.S.-GAAP), log(MEit) is the natural logarithm of the market equity, ROAit is the return on assets, CAPINTit is the capital intensity (long-term assets divided by total assets), MANUFit is a dummy variable indicating if the company is a manufacturing company (SIC b 4000), NEWMARKETit is a dummy for being included in the New Market (Neuer

Fig. 1. Excess returns of the German GAAP portfolio, the IAS/IFRS portfolio, and the U.S. GAAP portfolio from July 1997 to June 2005 (market value weighted).

366

J. Ernstberger, O. Vogler / The International Journal of Accounting 43 (2008) 339–386

Markt) segment of the Frankfurt stock exchange, and USUKit is a dummy indicating whether the company is cross-listed on the U.S. or U.K. market. Using this first stage probit estimation, we can compute the Inverse Mills Ratio (λit) to account for self-selection in the second stage. In the second stage, we analyze whether the adoption of IAAP (indicated by the variable IAAPit) significantly influences the cost of equity capital estimates which we derive based upon our factor-mimicking models. The important difference here is that we simultaneously control for self-selection bias and other effects included in the first-stage regression (e.g., cross-listing or New Market membership). Therefore, we estimate in a second-stage regression: CoECm it = u0 + u1 IAAPit + u2 logðMEit Þ + u3 kit + eit

ð9Þ

where for firm i and time t CoECitm is the cost of equity capital calculated by method m (CAPM or GM-Model), IAAPit is a dummy variable for applying IAS/IFRS or U.S.GAAP, log(MEit) is the natural logarithm of the market equity, and λit is the Inverse Mills Ratio. Even though we have already specified the cost of equity capital estimation model in the factor-mimicking section already, this procedure can be meaningful as a ceteris paribus analysis for cost of equity capital versus the adoption of IAAP.

Table 5 Descriptive statistics of yearly data (1998–2004), 494 firms Panel A: Continuous variables

log(ME) ROA CAPINT CoEC (CAPM) CoEC (GM Model) Inverse Mills Ratio

Observations

Mean

Median

Maximum

Minimum

Std. Dev.

2910 2924 2883 3394 3088 2468

11.965 0.016 0.346 0.060 0.050 0.496

11.756 0.047 0.334 0.062 0.072 0.563

19.193 0.839 0.951 2.997 3.976 0.667

6.674 −4.310 0.000 −1.588 −1.903 0.288

2.053 0.209 0.195 0.271 0.406 0.122

Panel B: Discrete variables

IAAP MANUF NEWMARKET USUK

Observations

Obs with Dep = 0

Obs with Dep = 1

3319 3952 3952 3952

1777 2688 2736 3672

1542 1264 1216 280

Variable definitions: log(ME) is the natural logarithm of the market value of equity, ROA is the return on assets, CAPINT is the capital intensity (long-term assets divided by total assets), CoEC (CAPM) is the cost of equity capital calculated by the CAPM, CoEC (GM Model) is the cost of equity capital calculated by the GM model, InvMillsRatio is the Inverse Mills Ratio calculated based on the first-stage regression in Table 12. IAAP is a dummy variable for applying internationally accepted accounting principles (IAS/IFRS or U.S.-GAAP), MANUF is a dummy variable indicating if the company is a manufacturing company (SIC b 4000), NEWMARKET is a dummy for being included in the New Market (Neuer Markt) segment of the Frankfurt stock exchange, and USUK is a dummy for indicating whether the company is cross-listed in the U.S. or U.K market.

αi

βi [rmt − rrf]

Adj. R2

αi

βi [rmt − rrf]

Adj. R2

αi

βi [rmt − rrf]

Adj. R2

G S B

S-L-G B-L-G

L − 0.0127⁎⁎ − 0.0027

0.3065⁎⁎⁎ 0.7091⁎⁎⁎

0.0951 0.5713

S-M-G B-M-G

M − 0.0002 0.0042

0.2945⁎⁎⁎ 0.8387⁎⁎⁎

0.2068 0.7181

S-H-G B-H-G

H 0.0033 0.0077⁎

0.4219⁎⁎⁎ 0.5079⁎⁎⁎

0.2911 0.3340

I S B

S-L-I B-L-I

L − 0.0372⁎⁎⁎ 0.0022

1.3864⁎⁎⁎ 0.6355†⁎⁎⁎

0.5126 0.5066

S-M-I B-M-I

M − 0.0155 0.0022

1.0132⁎⁎⁎ 0.7675†⁎⁎⁎

0.3167 0.5951

S-H-I B-H-I

H 0.0015 − 0.0046

0.9368⁎⁎⁎ 0.5948⁎⁎⁎

0.3078 0.2488

U S B

S-L-U B-L-U

L − 0.0401⁎⁎⁎ − 0.0067

1.6401⁎⁎⁎ 1.4431⁎⁎⁎

0.4758 0.5747

S-M-U B-M-U

M − 0.0167⁎ − 0.0121⁎⁎

1.2004⁎⁎⁎ 0.6439†⁎⁎⁎

0.4686 0.3531

S-H-U B-H-U

H 0.0132 0.0014

0.5944⁎⁎⁎ 1.0962⁎⁎⁎

0.1684 0.4783

The portfolio construction is described in Table 1. The Capital Asset Pricing Model (CAPM) describes how the market excess return (rmt − rrf) explains the excess return of a portfolio (rit − rrf). The excess return means the difference between the actual returns and the risk-free market interest rate. We apply the seemingly unrelated regression (SUR) technique. Abbreviations: αi: intercept; βi [rmt − rrf]: estimated parameter βi for market excess return rmt − rrf; Adj. R2: adjusted R2 (Goodness-of-Fit). ⁎, ⁎⁎, and ⁎⁎⁎ means that these values are significant at the 10%, 5%, and 1% level. †Exceptions to the thesis that the CAPM betas of the German GAAP portfolios are smaller than their equivalents in the IAS/IFRS and U.S. GAAP portfolios.

J. Ernstberger, O. Vogler / The International Journal of Accounting 43 (2008) 339–386

Table 6 Capital Asset Pricing Model: rit − rtrf = αi + βi(rmt − rtrf) + εit

367

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6. Sample selection and descriptive statistics 6.1. Portfolio analyses We use data concerning the German stock market, sampled monthly from July 1997 to June 2005 (96 months). This ensures that only companies fully applying IFRS or U.S. GAAP regulations are included as IAAP companies. Prior to that period, data quality was poor — e.g., reconciliations only. Our source for the stock returns (adjusted prices), number of common shares outstanding, and book values is the Datastream Advance database. For the risk-free rate we apply the 3-month interest rate of the German Bundesbank (July 1997–December 1998), and after that the EURIBOR 3-month rate as the money market reference rate for the Euro. The type of accounting regime applied and the fiscal year-end data are hand collected from company annual reports, since we found several missing or mistaken entries in the Worldscope database (see Daske et al., 2007, for details). In accordance with former studies (e.g., Ziegler et al., 2007), we exclude companies with a negative book value. Also, finance and insurance companies are excluded from the sample based on the Standard Industrial Classification (SIC 6000 to 6999) since their book values of equity are fundamentally different from those of nonfinancial companies. No company is allowed to have an interrupted time series. This leaves us with a final sample of 548 companies that we assign into 18 portfolios. To minimize the bias through outliers, we winsorize the return data at the 1% and 99% levels respectively. Table 4 shows the descriptive results of our analysis for the 18 portfolios, with the mean excess returns, their standard deviations, the number of monthly observations, and the average number of firms per month. Fig. 1 illustrates the timely development of the pooled portfolios into the three main classifications German GAAP, IAS/IFRS, and U.S. GAAP. The first interesting finding is that our data contain the Fama and French (1992) value premium in the means. We find a positive impact of a high book-to-market ratio on the (excess) returns. In our sample, the mean of the 18 portfolios increases with a rising bookTable 7 Beta values — comparison between the German GAAP firms, the IAS/IFRS firms, and the U.S. GAAP firms Average beta value

G I U

0.5131 0.8890 1.1030

Average beta value, weighted with number of observations

CAPM regression of the three combined portfolios

0.5298 0.8644 1.1380

0.6232 0.7255 0.9723

CAPM with new endogenous variables: (with t-statistic) I–G U–G

0.1089⁎⁎ 0.3750⁎⁎⁎

(1.8415) (4.5772)

Abbreviations: G: German GAAP; I: IAS/IFRS; U: U.S. GAAP; CAPM: Capital Asset Pricing Model; I-G: IAS/ IFRS portfolio minus German GAAP portfolio, regressed in CAPM model; U-G: U.S. GAAP portfolio minus German GAAP portfolio, regressed in CAPM model; ⁎, ⁎⁎, and ⁎⁎⁎ means that these values are significant at the 10%, 5%, and 1% level (here one-sided test).

G S B I S B U S B

L S-L-G B-L-G S-L-I B-L-I S-L-U B-L-U

αi − 0.0037 0.0012 L − 0.0138⁎⁎ 0.0020 L − 0.0170⁎ 0.0022

M rf

βi1 [rmt − r ] 0.3707⁎⁎⁎ 0.6645⁎⁎⁎

βi2 [SMB] 0.6780⁎⁎⁎ 0.0164

βi3 [HML] − 0.1170 − 0.1895⁎⁎

Adj. R 0.3388 0.5900

S-M-G B-M-G

1.2600⁎⁎⁎ 0.6441⁎⁎⁎

1.1443⁎⁎⁎ 0.0113

− 0.5012⁎⁎⁎ 0.0168

0.8111 0.4961

S-M-I B-M-I

1.5782⁎⁎⁎ 1.2869⁎⁎⁎

1.1327⁎⁎⁎ − 0.0531

− 0.6889⁎⁎⁎ − 0.5409⁎⁎⁎

0.7185 0.6142

S-M-U B-M-U

2

αi −0.0017 0.0026 M −0.0068 −0.0046 M −0.0015 −0.0157⁎⁎⁎

βi1 [rmt − rrf] 0.4787⁎⁎⁎ 0.7786⁎⁎⁎ 1.1703⁎⁎⁎ 0.8881⁎⁎⁎ 1.2080⁎⁎⁎ 0.6867⁎⁎⁎

The portfolio construction is described in Table 1. The Fama and French model describes how the market excess return (rmt − rrf), the small-minus-big factor (SMB), and the high-minus-low factor (HML) explain the excess return of a portfolio (rit − rrf). The excess return means the difference between the actual returns and the risk-free market interest rate. SMB represents the size of the companies, HML stands for a factor based on the book-to-market ratio, analogously to Fama and French (1997). We apply the seemingly unrelated regression (SUR) technique. Abbreviations: αi: intercept; βi [rmt − rrf]: estimated parameter βi1 for market excess return rmt − rrf; βi2 [SMB]: estimated parameter βi2 for factor SMB; βi3 [HML]: estimated parameter βi3 for factor HML; Adj. R2: adjusted R2 (Goodness-of-Fit). ⁎, ⁎⁎, and ⁎⁎⁎ means that these values are significant at the 10%, 5%, and 1% level.

J. Ernstberger, O. Vogler / The International Journal of Accounting 43 (2008) 339–386

Table 8 Fama and French model: rit−rtrf = αi + βi1(rmt − rtrf) + βi2SMBt + βi3HMLt + εit

369

370

G S B I S B S B

M βi2 [SMB] 0.5920⁎⁎⁎ − 0.3024⁎⁎⁎ M 1.1583⁎⁎⁎ 0.0589 M 1.0780⁎⁎⁎ − 0.0372

H βi3 [HML] 0.3913⁎⁎⁎ − 0.0630

Adj. R 0.5393 0.7528

S-H-G B-H-G

0.0902 0.4270⁎⁎⁎

0.5061 0.6944

S-H-I B-H-I

− 0.1569 0.1731

0.6901 0.3601

S-H-U B-H-U

2

αi 0.0021 0.0005 H 0.0044 − 0.0107 H 0.0132 − 0.0174⁎⁎

βi1 [rmt − rrf] 0.6426⁎⁎⁎ 0.6219⁎⁎⁎

βi2 [SMB] 0.7294⁎⁎⁎ 0.0101

βi3 [HML] 0.4508⁎⁎⁎ 0.4292⁎⁎⁎

Adj. R2 0.6289 0.4677

1.1808⁎⁎⁎ 0.7871⁎⁎⁎

1.3118⁎⁎⁎ 0.4139⁎⁎⁎

0.4043⁎⁎ 0.5388⁎⁎⁎

0.5421 0.3535

0.6859⁎⁎⁎ 1.3953⁎⁎⁎

1.3744⁎⁎⁎ 0.0442

0.6034⁎⁎⁎ 0.8813⁎⁎⁎

0.5060 0.6865

J. Ernstberger, O. Vogler / The International Journal of Accounting 43 (2008) 339–386

Table 8 Fama and French model: rit−rtrf = αi + βi1(rmt − rtrf) + βi2SMBt + βi3HMLt + εit

J. Ernstberger, O. Vogler / The International Journal of Accounting 43 (2008) 339–386

371

Table 9 Correlations of exogenous factors rmt − r SMBt HMLt GMIt GMUt Gt It Ut

rf

rmt − rrf

SMBt

HMLt

GMIt

GMUt

Gt

It

Ut

1.0000 −0.1553 −0.3298 −0.4365 −0.5044 0.8911 0.8293 0.8410

1.0000 −0.3771 −0.3470 −0.2321 −0.2844 −0.1810 −0.0402

1.0000 0.3326 0.4188 −0.3030 −0.0947 −0.3654

1.0000 0.7484 −0.1921 −0.3737 −0.5355

1.0000 − 0.2757 − 0.3047 − 0.6900

1.0000 0.7635 0.6923

1.0000 0.6432

1.0000

Abbreviations: rmt − rrf: Market excess return at time t; SMBt: Small-minus-Big (market value) factor at time t; HMLt: High-minus-Low (book-to-market value) factor at time t; GMIt: German GAAP-minus-IAS/IFRS factor at time t; GMUt: German GAAP-minus-U.S. GAAP factor at time t; Gt: Average returns of the German GAAP companies at time t; It: Average returns of the IAS/IFRS companies at time t; Ut: Average returns of the U.S. GAAP companies at time t.

to-market ratio, with only three exceptions (marked with † in Table 4), meaning that the value premium is empirically visible. Ziegler, Schröder, Schulz, and Stehle (2007) discover the same pattern for Germany as well. However, the second effect, discovered by Fama and French, of a positive impact of a small size (i.e. small market values have higher returns) cannot be confirmed in our data, more to the contrary. This is in accordance with Schrimpf, Schröder, Stehle (2006) and Ziegler et al. (2007). In our opinion this can be seen as a typical German phenomenon. In Germany, the tendency toward public offerings is substantially smaller compared to the United States or the United Kingdom where considerably more small companies are publicly traded. 6.2. Firm-level analyses For the two-stage approach, we use yearly financial-statement data gained from the DAFNE database8 for the period 1998 until 2004.9 To have these data available for all firms, however, we had to reduce the sample from the 548 companies referenced above to 494. The cross-listing data are taken from a study of the “Deutsches Aktieninstitut”10 (Glaum, Thomaschewski, & Weber, 2006). The accounting-regime data are, again, the hand-collected data from the portfolio analysis. We estimate the monthly cost of equity capital for each firm based on the CAPM and GM models using the risk factors from above ((rmt −rtrf), SMBt, HMLt, GMIt, and GMUt). Afterwards, we average the monthly data for every year and multiply them by 12

8 DAFNE is a database of detailed financial information for 140 000 German and Austrian companies, hosted by Bureau van Dijk Electronic Publishing (BvDEP), one of Europe's leading electronic publishers of business information. 9 Before 1998, data quality regarding international accounting standards is very low, since only a few companies published IAS or U.S.-GAAP financial statements. After 2004, the adoption of IFRS was mandatory for most of the publicly listed companies. 10 Deutsches Aktieninstitut e.V. (DAI) is the association of German exchange-listed stock corporations and other companies and institutions with an interest in the capital market. The DAI is an independent, nonprofit institution.

372

G S B

L S-L-G B-L-G

I z S B

αi −0.0086⁎ 0.0004

βi2 [SMB] 0.9540⁎⁎⁎ 0.0736

βi3 [HML] − 0.1425 − 0.2350⁎⁎⁎

βi4 [GMI] 0.4761⁎⁎⁎ −0.0498

βi5 [GMI] 0.2861⁎⁎ 0.2744⁎⁎⁎

Adj. R2 0.4853 0.6167

βi1 [rmt − rrf] 0.8940⁎⁎⁎ 0.6329⁎⁎⁎

βi2 [SMB] 0.8612⁎⁎⁎ − 0.0901

βi3 [HML] − 0.5180⁎⁎⁎ − 0.0560

βi4 [GMI] −0.5190⁎⁎ −0.5479⁎⁎⁎

βi5 [GMI] −0.1296 0.3844⁎⁎⁎

Adj. R2 0.8181 0.5348

βi1 [rmt − rrf] 0.8501⁎⁎⁎ 0.9189⁎⁎⁎

βi2 [SMB] 0.6697⁎⁎⁎ − 0.2243

βi3 [HML] − 0.5093⁎⁎⁎ − 0.2840⁎⁎

βi4 [GMI] −0.3401 0.3827⁎

βi5 [GMI] −1.1204⁎⁎⁎ −1.2483⁎⁎⁎

Adj. R2 0.8121 0.7410

βi1 [rmt − rrf] 0.5823⁎⁎⁎ 0.8222⁎⁎⁎

βi2 [SMB] 0.6758⁎⁎⁎ − 0.2244⁎⁎⁎

βi3 [HML] 0.3591⁎⁎⁎ − 0.0368

βi4 [GMI] 0.0264 0.2921⁎⁎

βi5 [GMU] 0.2455⁎⁎⁎ −0.1153

Adj. R2 0.6068 0.7604

βi1 [rmt − rrf] 0.6746⁎⁎⁎ 0.8664⁎⁎⁎

βi2 [SMB] 0.5824⁎⁎⁎ − 0.0123

βi3 [HML] 0.0019 0.3787⁎⁎⁎

βi4 [GMI] −1.9735⁎⁎⁎ −0.3461⁎⁎⁎

βi5 [GMU] 0.6027⁎⁎⁎ 0.2303⁎⁎

Adj. R2 0.6656 0.6981

L S-L-I B-L-I

αi −0.0103⁎ 0.0040

U

L

S B

αi −0.0116 0.0024

S-L-U B-L-U

G S B

βi1 [rmt − rrf] 0.5973⁎⁎⁎ 0.7416⁎⁎⁎

M S-M-G B-M-G

αi −0.0035 0.0016

I

M

S B

αi −0.0018 −0.0026

S-M-I B-M-I

J. Ernstberger, O. Vogler / The International Journal of Accounting 43 (2008) 339–386

Table 10 GM model: rit − rtrf = αi + βi1(rmt − rtrf) + βi2SMBt + βi3HMLt + βi4GMIt + βi5GMUt + εit

U S-M-U B-M-U

αi 0.0067 −0.0154⁎⁎⁎

G

H

S B

αi 0.0006 −0.0008

S-H-G B-H-G

I S B

βi2 [SMB] 0.7324⁎⁎⁎ − 0.0907

βi3 [HML] − 0.1231 0.2146⁎

βi4 [GMI] − 0.1098 0.0337

βi5 [GMU] − 1.0650⁎⁎⁎ − 0.2182

Adj. R2 0.7797 0.3589

βi1 [rmt − rrf] 0.7481⁎⁎⁎ 0.7050⁎⁎⁎

βi2 [SMB] 0.8251⁎⁎⁎ 0.1207

βi3 [HML] 0.4142⁎⁎⁎ 0.4326⁎⁎⁎

βi4 [GMI] 0.1023 0.2476⁎

βi5 [GMU] 0.2105⁎⁎ 0.0537

Adj. R2 0.6750 0.4907

βi1 [rmt − rrf] 0.6990⁎⁎⁎ 0.4841⁎⁎⁎

βi2 [SMB]βi2 [SMB] 0.8039⁎⁎⁎ 0.1867

βi3 [HML]βi3 [HML] 0.3815⁎⁎⁎ 0.5511⁎⁎⁎

βi4 [GMI]βi4 [GMI] − 1.4755⁎⁎⁎ − 0.4707⁎

βi5 [GMU]βi5 [GMU] 0.2136 − 0.1128

Adj. R2 0.6755 0.3539

βi1 [rmt − rrf] 1.2680⁎⁎⁎ 0.2493

βi2 [SMB]βi2 [SMB] 0.1017 1.3003⁎⁎⁎

βi3 [HML]βi3 [HML] 0.9047⁎⁎⁎ 0.7412⁎⁎⁎

βi4 [GMI]βi4 [GMI] 0.4246 0.9536⁎⁎⁎

βi5 [GMU]βi5 [GMU] − 0.3934⁎⁎ − 1.3604⁎⁎⁎

Adj. R2 0.6960 0.6635

H S-H-I B-H-I

U S B

βi1 [rmt − rrf] 0.4105⁎⁎⁎ 0.5913⁎⁎⁎

αi 0.0082 −0.0082 H

S-H-U B-H-U

αi −0.0162⁎⁎ 0.0114

The portfolio construction is described in Table 1. The GM model describes how the market excess return (rmt − rrf ), the small-minus-big factor (SMB), the high-minus-low factor (HML), the GMI factor, and the GMU factor explain the excess return of a portfolio (rit − rrf). The excess return means the difference between the actual returns and the risk-free market interest rate. SMB represents the size of the companies, HML stands for a factor based on the book-to-market ratio, analogously to Fama and French (1997). The factors GMI and GMU explain the accounting regime impact. GMI is the return difference between portfolios of companies using German GAAP and IAS/IFRS (“German GAAP minus IAS/IFRS”) and GMU is the return difference between German GAAP and U.S. GAAP (“German GAAP minus U.S. GAAP”) portfolios. We apply the seemingly unrelated regression (SUR) technique. Abbreviations: αi: intercept; βi [rmt − rrf]: estimated parameter βi1 for market excess return rmt − rrf; βi2 [SMB]: estimated parameter βi2 for factor SMB; βi3 [HML]: estimated parameter βi3 for factor HML; βi4 [GMI]: estimated parameter βi4 for factor GMI; βi5 [GMU]: estimated parameter βi5 for factor GMU; Adj. R2: adjusted R2 (goodness-of-fit). ⁎, ⁎⁎, and ⁎⁎⁎ means that these values are significant at the 10%, 5%, and 1% level.

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S B

M

373

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Table 11 Improvements of adjusted R2: Fama and French model versus CAPM and GM model versus Fama and French model G

L

S B

FF vs. CAPM + 0.2437 + 0.0187

S-L-G B-L-G

I

L

S B

FF vs. CAPM + 0.2985 - 0.0105

S-L-I B-L-I

U

L

S B

FF vs. CAPM + 0.2427 + 0.0395

S-L-U B-L-U

M GM vs. FF +0.1465 +0.0267

S-M-G B-M-G

FF vs. CAPM +0.3325 +0.0347

H GM vs. FF + 0.0675 + 0.0077

S-H-G B-H-G

M GM vs. FF +0.0070 +0.0387

S-M-I B-M-I

FF vs. CAPM +0.1893 +0.0993

+0.0936 +0.1268

S-M-U B-M-U

FF vs. CAPM +0.2291 +0.0070

GM vs. FF +0.0461 +0.0230

H GM vs. FF + 0.1595 + 0.0037

S-H-I B-H-I

M GM vs. FF

FF vs. CAPM + 0.3378 + 0.1338

FF vs. CAPM + 0.2344 + 0.1048

GM vs. FF +0.1334 +0.0004

H GM vs. FF + 0.0896 − 0.0012

S-H-U B-H-U

FF vs. CAPM + 0.3377 + 0.2083

GM vs. FF +0.1900 −0.0231

The portfolio construction is described in Table 1. The table shows absolute improvements (+) / deteriorations (−) of adjusted R2. Abbreviations: G: German GAAP; I: IAS/IFRS; U: U.S. GAAP; L / M / H: Low / Medium / High book-to-market value; S / B: Small / Big market value (=size); CAPM: Capital Asset Pricing model; FF: Fama and French model; GM: “German GAAP-minus” model.

(see, e.g., Fama & French, 1997) to obtain yearly firm-level cost of equity capital estimates.11 Descriptive statistics of all data are provided in Table 5. Most important to note is that the sample consists almost equally of IAAP (1542) and non-IAAP (1777) companies. 7. Regression results 7.1. Portfolio analyses Regarding the classical CAPM results in Table 6, we can testify to a fairly high explanatory power. All slope parameters are significant at a level of 1%. The goodness-offit is notably high, resulting in an adjusted R2 of 40.08%, on average, and an adjusted R2 of 54.78% applying to the market value weighted average. Looking at the parameter estimates, we find that nine of 12 comparisons between the German GAAP betas and IAS/IFRS and U.S. GAAP betas indicate differences for market beta (the other three are indicated with an † in Table 6). Computing the average of the three accounting regime groups, we can also state that German GAAP portfolios have lower betas. Their average is 0.5131, compared to 0.8890

11 We also estimate the firm-level models for the monthly cost-of-equity-capital data. For the inference, however, there is no difference between the monthly average and the monthly average multiplied by 12, since this is only linear transformation.

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Table 12 Forecasts of Cost of Equity Capital (based on CAPM and GM model) G

L

S B

CAPM − 0.377% 1.009%

S-L-G B-L-G

I S B

M GM −0.349% 0.993%

S-M-G B-M-G

L S-L-I B-L-I

H GM 0.651% 1.637%

S-H-G B-H-G

M

CAPM − 2.065% 1.335%

U

L

S B

CAPM − 2.228% 1.403%

S-L-U B-L-U

CAPM 0.562% 1.759%

GM −1.804% 1.501%

S-M-I B-M-I

CAPM − 0.389% 1.553%

S-M-U B-M-U

CAPM − 0.197% − 0.045%

GM 1.240% 1.996%

H GM − 0.221% 1.722%

S-H-I B-H-I

CAPM 1.127% 0.451%

S-H-U B-H-U

CAPM 1.880% 1.563%

M GM −1.546% 1.152%

CAPM 1.090% 2.013%

GM 1.230% 0.584%

H GM 0.470% − 0.119%

GM 2.774% 1.812%

The portfolio construction is described in Table 1. The table shows the forecasts for cost of equity capital, based on the CAPM and GM model. Cost of equity is the portfolio's excess-return plus the risk-free rate. Abbreviations: G: German GAAP; I: IAS/IFRS; U: U.S. GAAP; L / M / H: Low / Medium / High book-to-market value; S / B: Small / Big market value (=size); CAPM: Capital Asset Pricing model; GM: “German GAAP-minus” model.

(IAS/IFRS) and 1.1030 (U.S. GAAP). Weighted with the number of observations in each portfolio, we see the same pattern: the German GAAP average is 0.5298 versus 0.8644 (IAS/IFRS) and 1.1380 (U.S. GAAP). Finally, we also regress the return differences between IAS/IFRS and German GAAP, as well as between U.S. GAAP and German GAAP, on the market excess return in the CAPM. Testing the one-sided hypothesis that the resulting betas are smaller than zero can be rejected at a 5%-level for the “IAS/IFRS minus German GAAP” regressand, and at a 1%-level for “U.S. GAAP minus German GAAP” Table 13 Cost of Equity Capital Comparison (monthly weighted averages) G CAPM GM I CAPM GM U CAPM GM

Weighted by observations

Weighted by market value

1.094% 1.115%

1.496% 1.426%

Weighted by observations

Weighted by market value

0.506% 0.670%

1.267% 1.429%

Weighted by observations

Weighted by market value

0.158% 0.472%

0.832% 0.730%

The table shows a comparison between the forecasts for cost of equity capital, based on the CAPM and GM model, weighted (1) by observations and (2) by market value of equity. The sum of weights in each accounting regime group (G / I / U) totals to one. Abbreviations: G: German GAAP; I: IAS/IFRS; U: U.S. GAAP; CAPM: Capital Asset Pricing model; GM: “German GAAP-minus” model.

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regressand. This suggests that both the IAS/IFRS betas and the U.S. GAAP betas are significantly higher than the German GAAP betas. These results (Table 7) provide evidence for the information-risk effect of introducing a “new” accounting regime and leading to higher betas in the CAPM model. As expected, the extension of the CAPM with the Fama and French factors SMBt and HMLt, applying the typical Fama and French approach as our second model, leads to an increase in the goodness-of-fit, as indicated by the results in Table 8. The adjusted R2 rises to 57.20%. However, in terms of the market value weighted average, it goes up less substantially, only to 59.93%. Again, all (rmt − rtrf) parameters are significant at the 1% level. Out of our 18 portfolios, seven (eight) SMBt (HMLt) parameters do not achieve the 1% level. This can be explained by the correlation of these two factors in our sample, which leads to collinearity in the regressors and reduces the power of the model (Table 9). Applying our new model, with the “German GAAP minus IAS/IFRS”-factor (GMIt) and the “German GAAP minus U.S. GAAP”-factor (GMUt), results in the best explaining model. Applying the differences between the accounting regimes allows us to estimate our new model stated in Eq. (5). The results are depicted in Table 10. As before, the highest validity is implied in the (rmt −rtrf) parameters, followed by the SMBt, and the HMLt parameters. Even though our new regressors, GMIt and GMUt, are individually significant only in 11 of 18 cases, the overall explanatory power increases again. The new average adjusted R2 with 63.51% exceeds the CAPM by 23.43% points and the classical Fama and French model by 6.31% points. In terms of market value weighted numbers, it outperforms the CAPM by 7.78% points and the Fama and French model by 2.63% points.

Table 14 First-stage regression: IAAPit =Probit (δ0 +δ1log(MEit)+ δ2ROAit +δ3CAPINTit +δ4MANUFit + δ5NEWMARKETit + δ6USUKit +εit) Variable

Coefficient

Std. Error

z-statistic

Prob.

Constant log(ME) ROA CAPINT MANUF NEWMARKET USUK Obs with Dep = 0 Obs with Dep = 1 Total obs

− 1.045 0.072 − 0.487 − 0.307 − 0.107 1.670 0.635 1107 1332 2439

0.188 0.015 0.151 0.149 0.061 0.077 0.111 McFadden R-squared LR statistic (6 df) Probability (LR stat)

− 5.563 4.654 − 3.233 − 2.058 − 1.746 21.631 5.721

0.000 0.000 0.001 0.040 0.081 0.000 0.000 0.219 737.586 0.000

In the first stage, we estimate a probit model to analyze the firms' probability to adopt IAS/IFRS or U.S.-GAAP given different explanatory variables. Variable definitions: IAAP is a dummy variable for applying internationally accepted accounting principles (IAS/IFRS or U.S.-GAAP), log(ME) is the natural logarithm of the market value of equity, ROA is the return on assets, CAPINT is the capital intensity (long-term assets divided by total assets), MANUF is a dummy variable indicating if the company is a manufacturing company (SIC b 4000), NEWMARKET is a dummy for being included in the New Market (Neuer Markt) segment of the Frankfurt stock exchange, and USUK is a dummy for indicating whether the company is cross-listed at the U.S. or U.K. market. Based on this regression, we can also calculate the InvMillsRatio.

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Table 15 Second-stage regression: CoECitm = φ0 + φ1IAAPit + φ2log(MEit) + φ3λit + εit Panel A: CAPM-based cost of equity capital estimates A1) Time period: 1998–2004 Variable

Coefficient

Std. Error

z-statistic

Prob.

Constant IAAP InvMillsRatio log(ME) R-squared Total observations

− 0.172 − 0.036 0.052 0.019 0.030 2108

0.046 0.013 0.054 0.003 F-statistic Prob (F-statistic)

− 3.723 − 2.712 0.957 7.019

0.000 0.007 0.339 0.000 21.390 0.000

Variable

Coefficient

Std. Error

z-statistic

Prob.

Constant IAAP InvMillsRatio log(ME) R-squared Total observations

0.568 0.100 − 0.835 0.006 0.350 238

0.124 0.036 0.142 0.006 F-statistic Prob (F-statistic)

4.599 2.733 − 5.869 1.064

0.000 0.007 0.000 0.288 42.014 0.000

Variable

Coefficient

Std. Error

z-statistic

Prob.

Constant IAAP InvMillsRatio log(ME) R-squared Total observations

− 0.320 − 0.045 0.162 0.026 0.069 720

0.074 0.020 0.081 0.004 F-statistic Prob (F-statistic)

− 4.323 − 2.314 2.002 6.162

0.000 0.021 0.046 0.000 17.600 0.000

Variable

Coefficient

Std. Error

z-statistic

Prob.

Constant IAAP InvMillsRatio log(ME) R-squared Total observations

− 0.161 0.009 0.142 0.010 0.009 1150

0.062 0.020 0.076 0.004 F-statistic Prob (F-statistic)

− 2.598 0.455 1.878 2.534

0.010 0.650 0.061 0.011 3.550 0.014

A2) Time period: 1998–1999

A3) Time period: 2000–2001

A4) Time period: 2002–2004

Panel B: GM model-based cost-of-equity-capital estimates Dependent Variable: CoEC (GM model) Ordinary Least Squares B1) Time period: 1998–2004 Variable

Coefficient

Std. Error

z-statistic

Prob.

Constant IAAP

− 0.103 0.022

0.071 0.020

− 1.458 1.091

0.145 0.275

(continued on next page)

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Table 15 (continued) Panel A: CAPM-based costcost-of-equity-capital of equity capital estimates B: GM model-based estimates A1) B1) Time period: 1998–2004 Variable

Coefficient

Std. Error

z-statistic

Prob.

InvMillsRatio log(ME) R-squared Total observations

0.183 0.005 0.003 2108

0.082 0.004 F-statistic Prob (F-statistic)

2.217 1.186

0.027 0.236 2.014 0.110

Variable

Coefficient

Std. Error

z-statistic

Prob.

Constant IAAP InvMillsRatio log(ME) R-squared Total observations

− 0.118 0.036 − 0.203 0.029 0.145 238

0.135 0.040 0.155 0.006 F-statistic Prob (F-statistic)

− 0.878 0.895 − 1.310 4.584

0.381 0.372 0.191 0.000 13.229 0.000

Variable

Coefficient

Std. Error

z-statistic

Prob.

Constant IAAP InvMillsRatio log(ME) R-squared Total observations

− 0.633 − 0.044 0.187 0.043 0.096 720

0.092 0.024 0.100 0.005 F-statistic Prob (F-statistic)

− 6.910 − 1.817 1.862 8.276

0.000 0.070 0.063 0.000 25.359 0.000

Variable

Coefficient

Std. Error

z-statistic

Prob.

Constant IAAP InvMillsRatio log(ME) R-squared Total observations

0.091 0.040 0.245 − 0.012 0.006 1150

0.102 0.033 0.125 0.006 F-statistic Prob (F-statistic)

0.886 1.225 1.960 − 1.934

0.376 0.221 0.050 0.053 2.434 0.063

B2) Time period: 1998–1999

B3) Time period: 2000–2001

B4) Time period: 2002–2004

In the second stage, we analyze whether the adoption of IAAP significantly influences the cost of equity capital estimates. Variable definitions: CoEC is the cost of equity capital calculated by CAPM (Panel A) or GM-model (Panel B), IAAP is a dummy variable for applying IAS/IFRS or U.S.-GAAP, ln(ME) is the natural logarithm of the market value of equity, and InvMillsRatio is the Inverse Mills Ratio. We restrict the sample to four different time periods: 1998–2004 (A1, B1), 1998–1999 (A2, B2), 2000–2001 (A3, B3), and 2002–2004 (A4, B4).

To draw a first conclusion, including accounting regime information into the Fama and French model leads to an improvement of the explanatory power.12 The portfolio returns in our model can be described more precisely than when using the CAPM or the 12

Using the adjusted R2 takes into account the loss of degrees of freedom. Including two additional regressors does not automatically increase the adjusted R2.

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Fama and French approach. Table 11 illustrates the consolidated comparison of all three models. As a sensitivity check, we also compare the Schwarz criterion (SC) for the three different models.13 Computing the average of the individual SC for the 18 portfolios estimated with Least Squares, we find support for the results obtained by the adjusted R2. The GM model has the lowest SC (− 3.10) vis-à-vis the Fama and French model (−2.99) and the CAPM (− 2.77). Given a specific endogenous variable, the regression model with the lowest SC is regarded to be the best explaining model. Based on our three models, we can derive the cost of equity capital for the 18 portfolios as expected excess returns less the risk-free rate. The SUR system delivers forecasts of cost of equity capital for both the CAPM and the GM (see Table 12). They are applied to the 18 portfolios. Most notably, differences between the three accounting regimes are quite obvious for the CAPM-based cost of equity capital (Table 13), as the significant differences of the beta values have already indicated. The German GAAP groups with a market value weighted mean of 1.496% exceed the IAS/IFRS groups (1.267%) and the U.S. GAAP groups with (0.832%) in terms of expected cost of equity capital. For the GM model calculation, however, the differences are no longer strong. While the U.S. GAAP firms (0.730%) still have lower cost of equity capital, the German GAAP firms (1.426%) and IFRS/IAS firms (1.429%) show no significant difference. That this relationship is not applicable for the GM estimates is straightforward, given the specification of our new model. Since the GM model already accounts for accountingregime differences, we do not expect significant differences between the cost-of-equitycapital estimates. Taken together, these findings support the first hypothesis of our paper. Companies applying German GAAP have to deal with higher CAPM-based cost of equity capital expectations by the investors in comparison to firms that have adopted IAAP. We call this effect “accounting premium” for IAS/IFRS and U.S. GAAP firms vis-à-vis German GAAP firms. The development of a novel multi-factor model that captures the “accounting premium” leads to an improvement of the CAPM and Fama–French models. 7.2. Firm-level analyses As shown in Table 14, the first-stage regression of the IAAP dummy (IAAPit) on the different explaining factors (see Eq. (8)) in a probit model delivers highly significant results.14 The McFadden R2, at 21.95%, is quite substantial; the marginal effects of all explanatory variables are significant at least at the 10%-level. Especially noticeable are the p-values of the New Market dummy and the cross-listing dummy, which are both smaller than 0.0001. 13 We prefer using the Schwarz criterion (SC) versus using the Akaike Info criterion, since the SC “punishes” the loss of degrees-of-freedoms even more. 14 Since the mean of our dependent dummy variable IAAP is 0.4646, the probit model has to be preferred to the logit model. For a robustness check, however, we also estimate the logit model, finding no substantial differences between the two approaches.

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After computing the Inverse Mills Ratio from the first-stage regression, we analyze the effect of the IAAP dummy on the cost of equity capital, while simultaneously controlling for self-selection (Inverse Mills Ratio) and, implicitly, for cross-listing and New Market membership in our second-stage. The results are presented in Table 15. We distinguish between the CAPM-based (panel A) and the GM model-based (panel B) cost-of-equity-capital estimates. For each panel we estimate four different time periods: (1) the whole sample (1998–2004), and three subsamples, (2) 1998–1999, (3) 2000–2001, and (4) 2002–2004. Comparing the second-stage estimations, we find two major results. One, the impact of the IAAP dummy (IAAPit) is significantly different for the CAPM versus the GM model. Comparing especially the whole-sample estimations (1998–2004, panels A1 and B1), we find that IAAPit is highly significant for CAPM (p-value 0.007), whereas IAAPit is not significant for the GM model (p-value 0.275). Moreover, for the GM model the F-statistic for the whole sample period has a p-value of 0.110, meaning that all explaining variables are not significant at the 10%-level. Looking at all of the panel estimations, while for the CAPM regressions IAAPit is significant in three of four cases with two p-values below 1% and one below 5%, for the GM model regressions IAAPit is significant only in one case (panel B3), with a p-value below 10%. The rational for these findings is that the cost-of-equity-capital estimates for the CAPM do not account for accounting-regime differences, while the estimates in the GM model do. Consequently, it is not surprising that on the one hand the IAAP dummy (IAAPit) has no significant influence on the cost-of-equity-capital estimates once we include this information when estimating the dependent variable. On the other hand, documenting the differences for the CAPM regressions (panel A), we find significant differences between the regimes, which supports our H1. It is important to note that this time we also control for self-selection, cross-listing, and New Market membership effects. The second result is that there are time-period differences. Concentrating on the CAPM method (panel A), we find a negative sign for the IAAP dummy (IAAPit) for the whole period (1998–2004, panel A1), which is highly significant (p-value 0.007). This supports our first hypothesis that applying an IAAP leads to lower cost of equity capital. Looking at the subperiods provides more detailed results. For the years 1998 and 1999 the effect was positive (p-value 0.007, panel A2); for the years 2000 and 2001 it was negative (p-value 0.021, panel A3). Our rationale for this observation is as follows: in the beginning, uncertainty dominated the true substantial power of the IAAP, provisions for several important items were missing under IAS, and level of compliance with IAAP was low. These disadvantages at least partly diminished in the second subperiod when, under IAS, several new standards became effective and companies as well as users of financial statements became used to the IAAP. Yet, for the period 2002–2004, we cannot draw any interference, since IAAPit is not significant. Our explanation for this phenomenon is that in that period new revisions of accounting standards became effective which granted leeway for management's discretion, the credibility in the IAAP decreased after several accounting scandals and that German GAAP abolished the option of including tax-induced accounting practices in the consolidated accounts.

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Consequently, this gives support to H2, that differences in the effects of adopting IAAP on the cost of equity capital are time-specific. Regarding the goodness-of-fit, it is fair to mention that most of the resulting R2 are not considerably high, which can be seen as a drawback for this analysis. There are two points to mention, however. First, this is economically comprehensible, since we have stated that our cost of equity capital models (CAPM and GM model) already explain the cost of equity capital sufficiently. Consequently, we would even expect that the explaining power of the secondstage regressions is fairly low, meaning that our cost of equity capital methods were efficient before. Second, we see the second-stage as a ceteris-paribus analysis. In that context, low R2s are in fact also econometrically expectable and acceptable and do not impair the explanatory power, as long as the individual p-values are valid and the overall F-test does not lead to refusing the significance of all variables (see, e.g., Wooldridge, 2002, p. 41). In other words, we do not want to explain the calculation of the cost of equity capital in the second stage — we already did this with our portfolio models. Here, a low R2 is rather a sign for a well-specified cost-of-equity-capital calculation, which can also be seen as a robustness test for our H1. 8. Conclusions Our results suggest that the voluntary adoption of IAS/IFRS or U.S. GAAP by German companies goes along with a decrease in their cost of equity capital. Notwithstanding the specific institutional framework in Germany, our findings support the general expectation that higher quality accounting standards lead to lower cost of equity capital, using conventional models. We call this effect “accounting premium” for IAAP. These results also hold when controlling for effects like self-selection, cross-listing, and New Market (Neuer Markt) listing. Additionally, by developing a novel multi-factor model (that we call the “GM model”), we can capture the “accounting premium” effect and hence improve the CAPM as well as Fama and French model. Our study calls for more caution in future studies which investigate the relationship between the adoption of a certain accounting regime, information quality, and the cost of equity capital. Several additional effects and factors like accounting incentives, enforcement, other institutional settings, and the properties of the capital market, as well as the interactions of these effects, have to be considered. The novel methodology we developed for this study uses the classical CAPM and a multifactor model, based on the Fama and French model, which included the accounting regime information. This methodology has the advantages of not being biased or compromised by estimation errors of analysts' forecasts, of mitigating disclosure-level differences, and of being applicable to companies that are not followed by financial analysts. The self-selection issue is addressed by using a two-stage estimation procedure, explicitly controlling for self-selection and other effects, like cross-listing and New Market membership. Similar to Barth, Konchitchki et al. (2007), we find that the inclusion of an information quality or accounting factor improves the performance of the multi-factor model. We therefore provide further evidence that the traditional three-factor model of Fama and French lacks an additional factor which could proxy the level and quality of information provided to investors.

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We recognize, however, that our study is subject to caveats. First, we compare the effects of voluntary adoption of IAS/IFRS or U.S. GAAP by German companies. The effects of mandatory adoption in the periods from 2005 on might have been different where companies no longer committed themselves to increased disclosure by adopting IAS/IFRS or U.S. GAAP but instead are obliged to do so. Besides, the new enforcement system in Germany and the new oversight body for statutory auditors, established in 2005, might have impacted the accounting practice. Furthermore, companies, auditors, and investors have gained more expertise in IFRS and U.S. GAAP provisions thus might cope better with the more complex information provided by IAAP today. In summary, these changes might have lead to a different overall impact of the adoption of IFRS and U.S. GAAP by German companies on the cost of capital capital after 2004. Second, there is still a controversy in the literature about the assumption of diversifiability of information risk. However, apart from our study several other papers have already found results supporting the view that market beta does not suffice to explain cost of capital capital and see other information risk factors applicable to improve the explanatory power (see, e.g., Francis, LaFond et al., 2005; Botosan, 2006). Multi-factor models are especially crucial for determining cost of equity capital on the firm level for which the diversifiability argument does not apply. We see our research as another empirical contribution toward this discussion. These two caveats provide opportunities for future research. Studies about the impact of the mandatory adoption of IFRS, the changes in standards of IAS/IFRS and U.S. GAAP, and of the new enforcement system, as well as auditor oversight in Germany, on the information quality and the cost of equity capital might be valuable tasks. Moreover, future research could examine the ability of our multi-factor model in evaluating the costof-equity-capital impact of different IAAP and could test our model in other settings, e.g., to investigate the impact of the adoption of IFRS in other countries or institutional settings. References Armstrong, C. S., Barth, M. E., Jagolinzer, A. D., & Riedl, E. J., (2007). Market reaction to the adoption of IFRS in Europe. Working paper series. Ashbaugh, H., & Pincus, M. (2001). Domestic accounting standards, international accounting standards, and the predictability of earnings. Journal of Accounting Research, 39, 417−434. Auer, K. V. (1996). Capital market reactions to earnings announcements: Empirical evidence on the difference in the information content of IAS-based earnings and EC-Directives-based earnings. The European Accounting Review, 5(4), 587−623. Auer, K. V. (1998). Der Einfluß des Wechsels vom Rechnungslegungsstandard auf die Risikoparameter von schweizerischen Aktien. Zeitschrift für betriebswirtschaftliche Forschung, 50, 129−155. Baetge, J., Berndt, H., Bruns, H., Busse von Colbe, W., Coenenberg, A. G., Korst, H., et al. (1995). German accounting principles: An institutional framework. Accounting Horizons, 9(3), 92−99. Ball, R. (2006). International Financial Reporting Standards (IFRS): Pros and cons for investors. Accounting and business research. International Accounting Policy Forum, 5−27. Ball, R., Robin, A., & Wu, J. S. (2003). Incentives versus standards: properties of accounting income in four East Asian countries. Journal of Accounting and Economics, 36(1−3), 235−270. Barth, M., Konchitchki, Y., & Landsman, W. R. (2007). Cost of Capital and Financial Statements Transparency. Working paper series.

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