How do individual investors react to global IFRS adoption?
Ulf Brüggemann
Humboldt University of Berlin
Holger Daske
University of Mannheim
Carsten Homburg
University of Cologne
Peter F. Pope
Cass Business School
August 2012
We appreciate the helpful comments of Kevin Aretz, Hans Christensen, Gary Entwistle, Markus Glaser, JoergMarkus Hitz, Christian Leuz, Steve Lin, Steve Zeff, and seminar participants at HU Berlin, the University of
Chicago, Lancaster University, KU Leuven, Erasmus University Rotterdam, WHU Vallendar, the 2009 ASVHB/IAAER meeting in Munich, the 2009 EAA meeting in Tampere, the 2009 AAA meeting in New York, the
2010 FARS Mid-Year meeting in San Diego, the 2010 Accounting Research Conference at Penn State and the
INTACCT meetings in Frankfurt, Cyprus, London and Valencia. We are grateful to Markus Glaser for providing us
with statistics from his brokerage dataset. Thanks are also due to Paul Rayson for expert advice and Eddie Bell for
programming assistance on the collection of Google News archive search results. Ulf Brüggemann, Holger Daske
and Peter F. Pope gratefully acknowledge the financial contribution of the European Commission Research Training
Network INTACCT (Contract MRTN-CT-2006-035850). Part of this research was carried out while Ulf
Brüggemann was visiting the University of Chicago and Holger Daske was visiting London Business School.
Correspondence: u.bruggemann@hu-berlin.de.
Electronic copy available at: http://ssrn.com/abstract=1458944
How do individual investors react to global IFRS adoption?
Abstract
We examine the impact of global IFRS adoption on cross-border equity investments by
individual investors. Our proxy for cross-border equity investments is trading volume in the
Open Market at Frankfurt Stock Exchange. The Open Market is a segment designed for German
individual investors to trade a large selection of foreign stocks. Using a sample of 5,637 firms
from 31 countries around the world, we find that stocks experience a significant increase in Open
Market trading volume following mandatory adoption of IFRS. This effect is more pronounced
for attention-grabbing stocks (e.g., stocks experiencing an increase in media coverage following
IFRS adoption). Our results suggest that global IFRS adoption enhances cross-border equity
investments by individual investors. However, this effect does not materialize equally across
stocks due individual investors’ limited attention.
JEL classification:
Key Words:
G14, G38, K22, M41, M48
Individual Investors, International Accounting, IFRS, Open Market, Cross-Border
Investments
Electronic copy available at: http://ssrn.com/abstract=1458944
1.
Introduction
Over the last decade, International Financial Reporting Standards (IFRS) have been
introduced in over 100 countries around the world (see http://www.iasplus.com/country/
useias.htm). This development is fuelled by the expectation that global adoption of IFRS will,
among other benefits, enhance the comparability of financial statements across countries and,
thus, reinforce foreign equity investments (e.g., EC Regulation No. 1606/2002).1 The rationale is
that global IFRS adoption moves foreign stocks into the choice set of investors by replacing
unfamiliar country-specific accounting rules with one single set of standards that investors can
familiarize themselves with at lower cost. In this paper, we evaluate this claim by analyzing the
impact of global IFRS adoption on cross-border equity investment by individual investors.2
Prior literature on the economic consequences of global IFRS adoption provides evidence that
an increase in cross-border equity investments by institutional investors depends on whether
compliance with accounting rules is expected to be high and on the degree of change in financial
reporting rules as a result of IFRS adoption (e.g., DeFond et al., 2011; Yu, 2010). We
hypothesize that individual investors react differently in two ways. First, we expect individual
investors to ignore the complex interaction between IFRS adoption and the institutional
environment of the stocks, because they tend to be less sophisticated than their institutional peers
(e.g., Bhattacharya, 2001; Malmendier and Shantikumar, 2007). Second, we predict that
1
The European Union, Australia, Hong Kong, South Africa and many other countries around the world mandated
IFRS or IFRS equivalents for most listed firms from fiscal year 2005 onwards. The treatment group in our
empirical analysis is confined to IFRS adopters from these countries (see Table 1, Panel A). Here and in the
following, we therefore use the term global IFRS adoption to refer to the mandatory introduction of IFRS in
2005.
2
We use the term individual investors to refer to non-professional investors. Retail investors and private investors
are synonymous expressions used in prior studies. It is interesting to note that some of the earliest research
examining individual investors’ use and understanding of financial statement information was conducted by Sir
David Tweedie, the long-time Chairman of the IASB (e.g., Lee and Tweedie, 1977).
1
Electronic copy available at: http://ssrn.com/abstract=1458944
individual investors focus on those stocks within the IFRS-enhanced choice set that catch their
attention, for example, through increased media coverage. This hypothesis is based on the notion
that individual investors actively follow a smaller subset of stocks (e.g., Merton, 1987) and that
this subset is biased towards attention-grabbing stocks (Barber and Odean, 2008; Engelberg et
al., 2012).
We test our hypotheses by analyzing trading volume in the Open Market at Frankfurt Stock
Exchange (FSE) as a proxy for cross-border equity investments by individual investors. The
Open Market is an unofficial trading segment designed for German individual investors to trade
foreign (i.e., non-German) stocks that have their main listing at a home market outside Germany.
The group of foreign stocks quoted on the Open Market is very large (about one quarter of all
firms in the Datastream Universe, see Table 1) and determined by lead brokers who are allowed
to include securities in the Open Market at low cost and without the involvement of the issuer.
Although lead brokers generally set higher bid-ask spreads than in the home market, the Open
Market provides a cost-efficient alternative to the home market when trade sizes are low.
German retail banks and brokers pass on high, mostly fixed order fees when local clients choose
to trade directly in a stock’s home market, whereas fixed charges for trading at FSE are
considerably lower. For small trades executed in the Open Market, lower order processing costs
therefore more than compensate for higher bid-ask spreads (see Appendix A for an illustrative
example). In short, Open Market lead brokers provide German individual investors with cheaper
access to foreign stocks.
Open Market trading volume is a valid proxy for increases in cross-border equity investments
for two reasons. First, liquidity in the Open Market is low so that lead brokers typically cannot
match offsetting orders from individual investors. Instead, lead brokers carry out countertrades in
2
the respective home market when they rebalance inventories. Trading volume in the Open
Market therefore largely reflects changes in cross-border equity investments by German
individual investors rather than trades between these investors. The second reason is that Open
Market investors face no restrictions on the buying side, but they can only sell stock they own
and are not able to sell stock short. Consequently, increases in Open Market trading volume are
more likely to reflect increases rather than decreases in cross-border equity investments.
Appendix B provides statistics from a brokerage dataset supporting this conclusion.
Our empirical analyses are based on a proprietary dataset provided by FSE containing stocklevel information on Open Market trading volume for the period January 2002 to June 2008. The
Open Market sample comprises 5,637 (43,671) unique firms (semiannual firm-periods) from 31
countries around the world. The mean (median) trade size in the Open Market is about 2,700
(1,700) Euro which corroborates that this segment is used by individual investors. Event study
results show that Open Market trading volume increases significantly around annual earnings
announcements. This finding confirms insights from official statistics (DAI, 2008; Deutsche
Bundesbank, 2005) and recent research (e.g., Bailey et al., 2008; Ernst et al., 2009; Graham et
al., 2009) that suggest that individual investors who actively trade foreign stocks are likely to use
accounting information for their trading decisions.
The empirical analyses proceed in two steps. First, we employ a difference-in-differences
design to compare the average impact of global IFRS adoption on Open Market trading volume
of IFRS adopters (treatment group) with its impact on firms that apply local GAAP throughout
the sample period (control group). The regression results show that Open Market trading volume
of mandatory IFRS adopters increases by more than 45% relative to the control group. This
effect is statistically significant and robust to the inclusion of variables that control for
3
concurrent changes in market value, changes in home market trading activity and changes in
number of German institutional investors, respectively. These control variables are designed to
ensure that our results do not reflect already documented effects from global IFRS adoption such
as decreases in the cost of equity capital (Li, 2010), liquidity increases in the home markets
(Daske et al., 2008) or increases in cross-border equity investments by institutional investors
(e.g., DeFond et al., 2011; Yu, 2010). Further tests show that the estimated IFRS effects are not
driven by firms that start preparing their financial statements in English or by concurrent
reductions in the bid-ask spread difference between the Open Market and the respective home
markets. The effect for voluntary IFRS adopters following global IFRS adoption is weaker and
not always statistically significant.
In additional tests addressing the identification of the IFRS effect, we first repeat the
difference-in-differences analysis by artificially changing the starting date of global IFRS
adoption. Results confirm that the estimated IFRS effects in the main analysis reflect a structural
break in Open Market trading volume rather than the continuation of country-specific time
trends. Next, we perform a within-country analysis focusing on the United Kingdom. The results
show that mandatory IFRS adopters listed in the Main Market at London Stock Exchange (LSE)
experience a significant increase in Open Market trading volume following global IFRS adoption
relative to LSE companies that were not required to adopt IFRS before fiscal year 2007.
In the second step of the empirical analyses, we examine cross-sectional variation in the
estimated IFRS effect. In contrast to studies examining cross-border equity investments by
institutional investors (e.g., DeFond et al., 2011; Yu, 2010), we find that the estimated IFRS
effect does not vary with institutional variables at the country- or industry-level. While this result
is consistent with our hypothesis that individual investors ignore the complex interaction
4
between IFRS adoption and the stocks’ institutional environment, it has to be interpreted with
caution due to the specific composition of the Open Market sample. Additional analyses show
that the Open Market sample is significantly tilted towards more visible and transparent firms
and, thus, may not provide sufficient variation in determinants of institutional quality to enable a
powerful test. In contrast, sample selection is less of a concern when testing the effects of limited
attention as the Open Market comprises several thousand stocks and, thus, far more than most
individual investors have the resources to analyze and retain within their investment choice sets.
Our analyses provide strong evidence that the estimated IFRS effect is more pronounced in
stocks that catch individual investors’ attention through increased media coverage (measured by
the number of articles published on Google News) or positive IFRS effects on net income. This
finding supports our hypothesis that individual investors focus on attention-grabbing stocks
within the IFRS-enhanced choice set.
Taken together, the results of our empirical analyses suggest that global IFRS adoption
enhances cross-border equity investments by individual investors. However, this effect does not
materialize equally across stocks due individual investors’ limited attention.
Our paper contributes to the emerging research on the economic consequences of IFRS. To
our knowledge, we provide the first analysis on how individual investors react to global IFRS
adoption. Given the important role that individual investors play in financial markets,3 the lack of
prior evidence may seem surprising at first. However, a likely explanation is that data on
individual investors’ behavior is not publicly available. Prior studies on individual investors have
3
At the end of 2007, domestic individuals owned 14% of the market value of listed stocks in Europe (FESE,
2008). In the United States (U.S.), more than 20% is held directly by individual investors (French, 2008).
Anecdotal evidence suggests that individual investors are more likely to pursue long-term objectives than their
institutional counterparts. Companies therefore make great efforts to attract individual investors, e.g. via
corporate websites and investor relations departments (Vogelheim et al., 2001). The relevance of individual
investors is also recognized by the International Accounting Standards Board (IASB) as reflected in its recent
efforts to include them in the development of IFRS (IASB, 2010).
5
adopted several strategies to address this challenge, including analysis of small trades using
intra-day transactions data (e.g., Lee, 1992), surveys of individual investor opinion (e.g., Elliott
et al., 2008) and examination of a proprietary datasets from online brokers (e.g., Odean, 1998;
Glaser and Weber, 2009). In contrast to these approaches, the Open Market setting is capable of
providing large-sample evidence on the impact of global IFRS adoption on individual investor
decisions. The Open Market may therefore also prove useful for other research, because it allows
direct observation of aggregate trading activities of a large group of individual investors trading
in foreign stocks.
2.
Hypotheses Development
Regulators expect that global IFRS adoption will, among other benefits, enhance the
comparability of financial statements across countries and, thus, reinforce foreign equity
investments (e.g., EC Regulation No. 1606/2002). The underlying argument is that global IFRS
adoption moves foreign stocks into the choice set of investors by replacing unfamiliar countryspecific accounting rules with one single set of standards that investors can familiarize
themselves with at lower cost.
Extant evidence on reactions by institutional investors suggests that the regulators’
expectation has indeed been met provided that the stocks’ institutional environment ensures high
compliance with accounting rules and allows for a substantial change in financial statements
through IFRS adoption. DeFond et al. (2011) find that mandatory IFRS adoption in the EU
enhances cross-border equity investments by international mutual funds if the increase in the
number of industry peers is sufficiently large and accounting standards are credibly
implemented. Yu (2010) provides similar evidence and identifies accounting distance (i.e., the
number of differences between IFRS and the domestic accounting standards they replace) as an
6
additional determinant for the increase in cross-border equity investments.4 These findings are
consistent with the majority of related literature that shows that capital-market benefits of
mandatory IFRS adoption such as higher market liquidity (Daske et al., 2008), lower cost of
equity capital (Li, 2010), increased institutional holdings (Florou and Pope, 2012), higher
information content of earnings announcements (Landsman et al., 2012) or improved analyst
forecast properties (e.g., Byard et al., 2011; Tan et al., 2011) are contingent on the quality of the
institutional environment.
However, it is not clear whether individual investors react to global IFRS adoption in the
same manner. Prior literature suggests that individual investors systematically differ from their
institutional counterparts in at least two ways. First, they are more naive about incentives and
more likely to use simple heuristics in decision making (see De Bondt, 1998, for a survey of the
earlier literature). For example, Bhattacharya (2001) examines abnormal trading reactions to
earnings announcements and shows that small traders tend to rely on a seasonal random-walk
model when forming earnings expectations. In contrast, large traders incorporate more current
information than the previous year’s earnings into their expectations. In a similar vein,
Malmendier and Shantikumar (2007) provide evidence that small traders follow analysts’ stock
recommendations literally, while large traders display more sophisticated reactions by taking into
account that analysts tend to bias these recommendations upward. Second, individual investors
have fewer resources and therefore tend to actively follow a smaller subset of stocks (e.g.,
Merton, 1987). Barber and Odean (2008) find that this subset is biased towards stocks that grab
the attention of individual investors (e.g., stocks in the news or stocks experiencing high
4
Khurana and Michas (2011) and Shima and Gordon (2011) focus on institutional investors from the U.S. and
also find an increase in cross-border equity investments following mandatory IFRS adoption. The size of this
effect is positively associated with country-level factors such as the strength of the enforcement regime and the
accounting distance.
7
abnormal trading volume). Engelberg et al. (2012) provide supporting evidence for this attention
effect by showing large overnight returns to stock recommendations on the popular television
show “Mad Money”. These returns subsequently reverse in the long run.
Based on these insights from prior literature, we predict that while global IFRS adoption has
the potential to enhance cross-border equity investments by individual investors, this reaction is
likely to differ from that of institutional investors in two aspects. First, we expect individual
investors to ignore the complex interaction between IFRS adoption and the institutional
environment of the stocks, because they tend to be less sophisticated than their institutional
peers. Second, we predict that the lack of resources leads individual investors to focus on those
stocks in the IFRS-enhanced choice set that grab their attention. Our hypotheses can be
summarized as follows:
H1: Global IFRS adoption enhances cross-border equity investments by individual investors.
H2a: The effect of global IFRS adoption on cross-border equity investments by individual
investors is not related to the institutional environment of stocks.
H2b: The effect of global IFRS adoption on cross-border equity investments by individual
investors is more pronounced for attention-grabbing stocks.
In our empirical analyses, we use trading volume in the Open Market at FSE as a proxy for
cross-border equity investments by individual investors. The next section provides institutional
details on the Open Market.
8
3.
The Open Market
3.1
INSTITUTIONAL BACKGROUND
The Open Market (“Freiverkehr” in German) is an unofficial trading segment at FSE. In
contrast to official stock market segments in Europe (e.g., Prime and General Standard at FSE,
Main Market at London Stock Exchange), the Open Market is not subject to regulations and
directives of the European Union (EU), but is exclusively governed by stock exchange rules. It
covers a variety of financial instruments such as stocks (both from Germany and abroad), bonds,
certificates and warrants. The stocks segment is structured into the First Quotation Board and the
Second Quotation Board. The First Quotation Board contains companies with a primary listing in
the Open Market.5 Companies whose stocks are already listed at another domestic or foreign
trading venue (home market) are included in the Second Quotation Board. Since the Open
Market is an unofficial trading segment, the EU regulation mandating IFRS is not applicable to
companies in the First Quotation Board. In contrast, many companies in the Second Quotation
Board are obliged to prepare their financial statements in accordance with IFRS due to regulation
in the respective home markets. In this study, we focus on the foreign (i.e., non-German) stocks
in the Second Quotation Board. For simplicity, we refer to this sub-segment as the Open
Market.6
5
In 2005, FSE introduced the Entry Standard as a sub-segment of the First Quotation Board. Transparency
requirements in the Entry Standard are higher than in the rest of the Open Market, but considerably lower than in
official FSE stock market segments. While the Entry Standard is open to all companies, it is specifically targeted
at small- and mid-caps that seek low cost access to the capital market. The Entry Standard is marketed as an
alternative to the Alternative Investment Market at London Stock Exchange (e.g. Sudmeyer et al., 2005; Schlitt
and Schäfer, 2006). At the end of 2010, 119 (13) German (foreign) companies were listed in the Entry Standard
(FSE, 2009).
6
Official resources and the academic literature provide only little information on the Second Quotation Board of
the Open Market. Much of the following description is based on the insights we gained from interviews with
FSE staff and brokers. For more general information on the Open Market see e.g. Müller-Michaels and Wecker
(2005), Harrer and Müller (2006) or the website of FSE: www.deutsche-boerse.com.
9
Established in 1987, the Open Market has become increasingly popular with German
investors in recent years. At the end of 2000, a total of 4,471 foreign stocks were traded in the
Open Market. This number more than doubled to 10,095 by the end of 2010. For comparison, the
number of domestic stocks traded at FSE increased by merely 17% from 903 to 1,058 during the
same period (FSE, 2010). The remarkably high number of foreign stocks available for trading in
the Open Market is a consequence of its unique set of rules.7 These rules permit eligible
brokerage houses accredited for trading at FSE to include securities in the Open Market on their
own initiative (AGB §2.3). The stock issuing company need not be informed, nor need it approve
inclusion of its securities in the Open Market.8 For the brokerage house, the inclusion process
involves two basic requirements. First, it has to guarantee orderly fulfillment of transactions by
acting as a lead broker (AGB §12.1). Second, it has to pay a non-recurring fee of 750 Euro (AGB
§25). Follow-up obligations of the lead broker are confined to informing the FSE about essential
company news concerning the issuer that can be acquired “by generally accessible information
sources in a reasonable way” (AGB §14.2). Lead brokers are authorized to exclude securities
from the Open Market at any time “subject to an adequate term” (§ 15.1 AGB).9 In summary,
brokerage houses face very few constraints or institutional barriers relating to inclusion or
exclusion of securities in the Open Market.
7
We refer to this set of rules as AGB in the following. AGB stands for “Allgemeine Geschäftsbedingungen für
den Freiverkehr an der Frankfurter Wertpapierbörse” (General Terms and Conditions for the Regulated
Unofficial Market). The specific rules we cite are from the most recent AGB version as of May 2011. The
content of these rules is very similar to that of an earlier AGB version as of September 2002.
8
Such involuntary cross-listings are also observed at other stock exchanges. For example, the unsponsored OTC
ADR program in the U.S. allows for the inclusion of foreign stocks without legal obligation to notify the firms or
obtain their consent (Iliev et al., 2011). In contrast to the Open Market, this program is not specifically designed
for individual investors.
9
Order books of Open Market securities can also be terminated by FSE (AGB §15.2). For example, in December
2005, FSE suspended trading in Turkish stocks until further notice because of unanswered questions about a
planned tax on Turkish equities (Greil, 2005).
10
Once a security has been included, the lead broker holds the exclusive right to set bid and ask
quotes.10 Although officially non-binding, these quotes are de-facto tradable up to a size the lead
broker specifies (Freihube et al., 1999). When an investor places an order to trade on the bid
(ask) quote, the lead broker buys (delivers) the agreed number of stocks. To entirely eliminate
inventory risk the resulting position would then ideally be closed immediately with an offsetting
order. However, due to market illiquidity (see below for supporting evidence), perfect offsetting
may not be possible for Open Market stocks. The lead broker is then forced to execute inventory
rebalancing countertrades in another market, typically the home market where liquidity is usually
much higher. Hence, in setting bid and ask quotes the lead broker faces a trade-off. On one hand,
she has an incentive to offer low bid-ask spreads to generate trades and earn brokerage fees. On
the other hand, there is the risk of losing out on trades if countertrades in home markets are
carried out at unfavorable prices. The resulting Open Market quotes are therefore likely to be
determined by the home market bid-ask spread as a lower bound plus a premium that reflects the
price risks faced by lead brokers in executing inventory rebalancing and due to currency risk
exposure during trade execution. Other factors with a potential impact on the bid-ask spread
premium in the Open Market include trading volume (i.e., the likelihood that the lead broker is
able to match offsetting orders) and competition to the lead broker’s services. Competition may
arise from other German exchanges, e.g. in Berlin, Stuttgart or Munich, where similar but much
smaller trading segments exist. Within the FSE, trading volume can shift from floor trading
where the lead broker operates to the fully electronic platform XETRA where quotes are
10
In case more than one party applies to be the lead broker for a particular stock, the allocation of the order book is
decided by lot. Baader Bank AG, mwb fairtrade Wertpapierhandelsbank AG and Wolfgang Steubing AG
Wertpapierdienstleister are the leading brokerage houses in the Open Market (Hiller von Gaertringen, 2006), but
there are a number of other competitors. Detailed information on the allocation of Open Market order books is
not publicly available.
11
automatically determined by an open limit order book. We provide more details on these
alternative trading channels in Appendix A.
Despite high bid-ask spreads, the Open Market provides a cost-efficient trading venue
alternative to home markets under certain circumstances. German retail banks and brokers pass
on high, mostly fixed order fees when local clients choose to trade directly abroad, whereas fixed
charges for trading at FSE are considerably lower. Hence, for small trade sizes the higher bid-ask
spreads (i.e., variable transaction costs that increase with trade size) are outweighed by lower
order processing costs (i.e., mostly fixed transaction costs that are independent of trade size) in
the Open Market. The combination of low fixed and high variable fees in the Open Market is
likely to be particularly attractive for individual investors who trade small sizes of foreign
stocks.11 Put differently, the Open Market lead broker provides German individual investors with
cheaper access to foreign stocks.12 Appendix B provides an illustrative example on these links.
The first part of Appendix C confirms empirically that individual investors of a German online
broker trade foreign stocks primarily through the Open Market, particularly when trade sizes are
low.
3.2
DATA AND DESCRIPTIVE STATISTICS
In this section, we describe the quantitative features of the Open Market. The analysis is based
on two main samples of equities: (1) the Datastream (DS) Universe, and (2) the Open Market
11
We refer to Open Market investors as German individual investors for two reasons. First, the economics of the
Open Market show that Open Market investors trade through German retail banks and brokers (see Appendix B).
Due to institutional barriers most German retail banks and brokers require their clients to be domiciled in
Germany. Second, German retail banks and brokers typically target their services at investors who speak
German. For example, the web-portal of comdirect bank (www.comdirect.de), the leading online broker in
Germany, provides information in German language only.
12
Note that institutional investors typically (1) have preferred and cheaper access to home markets through their
lead brokerage houses and (2) trade in volumes well above the break-even where the home market turns into the
more cost-efficient trading alternative.
12
sample - a subset of the DS Universe. At the country-level, the DS Universe includes all firms
covered by Datastream domiciled in countries other than Germany that either introduced IFRS in
2005 (the treatment group) or mandated domestic accounting standards throughout the sample
period (the control group).13 At the firm-level, we restrict the DS Universe to companies that
have their primary listing on the main exchange of their country of domicile (home markets)14
and for which sufficient data on trading volume, stock returns and accounting data are available.
We only include companies in the treatment group that switched from local GAAP to IFRS in
2005 (mandatory IFRS adopters) or before 2005 (voluntary IFRS adopters).15 The control group
consists of companies that used domestic accounting standards throughout the sample period.
We retrieve information on accounting standards followed from Worldscope.
The Open Market sample covers all firm-years within the DS Universe during which the
respective stock could be traded on the FSE. We identify this sample using a proprietary dataset
from FSE containing daily trading volume data (both in Euros and in number of shares traded) as
well as the number of ticks for every stock traded in the Open Market during the sample period.
The FSE trading volume dataset available to us spans the period January 2002 to June 2008. For
consistency, we confine capital market data from Datastream (e.g. trading volume for the home
markets) to the same period. We partition the dataset into a maximum of 13 semiannual periods
13
We deliberately exclude Germany (our focus is on foreign, that is, non-German, stocks), New Zealand (IFRS
introduction in 2007), Singapore (IFRS introduction in 2003) and Switzerland (no mandatory IFRS introduction,
many listed firms use IFRS by choice) from the DS Universe.
14
The main exchange is defined as the trading venue with the largest number of companies listed. We consider
only one exchange per country except for the United States where firms from both New York Stock Exchange
(NYSE) and NASDAQ are included. By focusing on the main exchange(s) in each country, we exclude
companies listed at less regulated trading venues (such as the OTC Bulletin Board in the U.S.) and thus ensure a
minimum level of transparency among sample firms.
15
Thus, in order to obtain a clean sample, companies from the treatment group countries that did not switch to
IFRS during the sample period (e.g., firms that need not prepare consolidated financial statements), did so after
2005 (e.g., firms listed on the Alternative Investment Market at London Stock Exchange) or applied U.S.-GAAP
(e.g., due to a cross-listing in the U.S.) are not considered. We include firms from the Alternative Investment
Market in an additional analysis in section 4.3.2.
13
per firm covering 2002H1 to 2008H1 with the suffix H1 (H2) indicating the first (second) half of
the respective calendar year.
Table 1 presents details on the composition of the DS Universe and the Open Market sample.
Panel A focuses on the treatment group, that is, countries that introduced IFRS in 2005. The DS
Universe includes 266 (6,192) voluntary (mandatory) IFRS adopters from 22 countries. The
Open Market sample covers 50% (27%) of all voluntary (mandatory) IFRS adopters in the DS
Universe. Panel B shows that the DS Universe includes 15,822 firms from 19 countries within
the control group. 24% of these companies are part of the Open Market sample. Open Market
coverage differs substantially across countries and firms. For example, while the majority of
Austrian and U.S. stocks are tradable in the Open Market, some countries (e.g., Poland, Morocco
or South Korea) are not represented at all. At the firm-level, the considerable difference in
coverage rates across accounting standards and IFRS adopter types gives a first indication that
the lead brokers do not randomly choose the securities they offer in the Open Market. From
Panel C, we learn that the number of Open Market firms covered in the DS Universe increases
over time, both in absolute as well as in relative terms. The total number of unique Open Market
firms (Open Market share) climbs from 2,188 (16%) in 2002H1 to 4,936 (25%) in 2008H1. In
total, the Open Market sample (DS Universe) comprises 5,637 (22,280) unique firms and 43,671
(217,772) firm-periods.16
Table 2 shows descriptive statistics on various firm characteristics for the Open Market
sample (Panel A) as well as for the rest of the DS Universe (Panel B). Panel A presents trading
volume, number of trades, trade size and bid-ask spread statistics from FSE and the respective
home markets for the same set of firm-years. Panel B is naturally confined to data from the home
16
Differences in the size of the Open Market sample and the official numbers from the FSE Factbooks stem from
the data requirements we impose on the DS Universe.
14
markets, because the covered sample (DS Universe excluding the Open Market sample) is not
traded at FSE. In addition to liquidity measures, both panels show statistics on other variables
that are independent of the trading venue. Liquidity in stocks from the Open Market sample is
low at FSE. During the average firm period, trading occurs on slightly less than 25% of all
trading days. Average stock-level daily trading volume is about 18,000 Euros, although the
distribution of trading volume is highly positively skewed. In contrast, daily trading volume in
home markets averages nearly 30 million Euros. These massive liquidity differences across
exchanges are hardly surprising. While trading at FSE is confined to a small subset of individual
investors, institutional investors and non-German individual investors will prefer to trade in the
respective home markets.17 Average trade size at FSE is about 2,700 Euro which is well below
the threshold of 10,000 USD typically used in prior literature to distinguish between trades of
individual and institutional investors (e.g., Lee, 1992). Comparison of bid-ask spreads across
exchanges indicates that the variable fee the lead broker charges on Open Market transactions is
in fact substantial: the median bid-ask spread is 3.13% at FSE compared to only 0.96% in the
respective home markets. Taken together, these descriptive statistics confirm that the Open
Market at FSE is a trading segment that is specifically designed for individual investors.
3.3
CHARACTERISTICS OF THE OPEN MARKET
In this section, we complement the descriptive analysis of the Open Market by examining two
important features of the Open Market. First, we provide evidence on the characteristics of firms
17
Untabulated statistics show that trading volume at FSE aggregated over the whole Open Market sample varies
between 10 and 20 billion Euros per year. Hence, despite its relative lack of liquidity the Open Market offers
substantial income opportunities for its participants. For example, with an average brokerage fee of 0.08% of the
order volume (see Appendix A) Open Market lead brokers earn a total of 8 to 16 million Euros per year for their
services.
15
included in the Open Market. Second, we discuss how accounting information influences Open
Market trading volume.
3.3.1 Determinants of Open Market Inclusion
Stocks are tradable in the Open Market if they have been included by the lead broker. This
decision depends on a stock’s potential to generate sufficient Open Market trading volume and,
thus, brokerage fees. Potential trading volume in the Open Market is ultimately determined by
individual investors’ demand for a particular stock.
Table 3, Panel A, presents results from probit regressions relating the likelihood of inclusion
of a stock in the Open Market to various firm- and country-specific variables. The analysis yields
two key findings. First, firms in the Open Market feature higher market values (Market Value),
higher trading volume in the home market (Home Trading Volume), are more likely to prepare
their financial statements in German or English (German/English Reporting) and attract more
German institutional investors (No. of German Inst. Investors) than firms whose stocks are not
tradable at FSE.18 These results suggest that Open Market investors prefer stocks of more visible
companies that prepare financial statements in a familiar language. The second key finding is
that transparent reporting practices are significant determinants of Open Market inclusion, both
at the country and at the firm level (EM Measure). The only other significant determinant at the
country level is a dummy variable that indicates countries from the eurozone (Euro). In contrast,
variables for capital market development (MCAP/GDP) and geographic proximity (Distance
Berlin - Capital) do not load significantly.19
18
We transform highly skewed variables using natural logarithms to mitigate the influence of outliers. All variables
are described in more detail in Table 2 and 3, respectively.
19
Note that some coefficients lose their statistical significance in multivariate regressions due to high correlations
among the determinants. For example, the Pearson correlation coefficient between Log(Market Value) and
Log(Home Trading Volume) over the DS Universe is 0.85.
16
Taken together, these results provide strong evidence that the Open Market sample is a nonrandom subset of the DS Universe. Specifically, the Open Market sample is significantly tilted
towards more visible and transparent companies. Thus, it seems that lead brokers either act as
gatekeepers to the Open Market by proactively screening stocks for inclusion, or they respond to
demand from individual investors who prefer more visible and transparent stocks.
3.3.2 Accounting Information and Open Market Trading Volume
In this subsection, we present official statistics and event study results to provide insights into
how accounting information influences the trading behavior of Open Market investors.
Official statistics show that – similar to evidence from other countries – the majority of
German individuals do not actively invest in stocks and that those who do exhibit a considerable
degree of home bias when selecting individual stocks. For example, in 2004 the number of
individual shareholders over 14 years in Germany amounted to 4.6 million or 6.5% of the entire
population (DAI, 2008). Despite the well known benefits of international diversification, German
individual shareholders invested a total of 145,495 million Euro in domestic, but only 36,873
million Euro in foreign stocks (i.e., 20.2% of all investments in stocks; Deutsche Bundesbank,
2005). These macro-level statistics suggest that the small subset of German individuals that
actively trades individual foreign stocks possesses more financial literacy, on average, than those
who focus on domestic equity or entirely refrain from stock picking. Consistent with this
observation, recent research shows that individual investors are more likely to have
internationally diversified portfolios if they are highly educated, wealthy and/or have more
trading experience (e.g., Bailey et al., 2008; Graham et al., 2009). Survey evidence by Ernst et al.
(2009) reveals that German individual investors use business media and financial statements as
central information sources. Ernst et al. (2009) also find that the usage of accounting information
17
increases with the trading experience of individual investors. Taken together, these findings
suggest that accounting information affects Open Market trading volume through two nonmutually exclusive channels: either Open Market investors utilize financial statements by
themselves and/or they consult other information sources such as the business media and
brokers’ analyst reports that in turn reflect information disclosed in financial statements.
We complement the insights from official statistics and prior literature with direct evidence on
the link between accounting information and Open Market trading by analyzing abnormal
trading volume around annual earnings announcements. Table 3, Panel B, compares reactions at
FSE with those in the respective home markets for the same set of earnings announcements. The
analysis is based on a sample of 18,362 earnings announcement dates from IBES. Abnormal
trading volume is the difference between trading volume on the event day and the mean daily
volume for that stock over the pre-announcement window (-120, -21), scaled by the mean daily
volume (e.g., Bamber et al., 2011). To mitigate the influence of outliers, abnormal trading
volume is winsorized at the 99% level by event day. The results show that abnormal trading
volume at FSE increases significantly around earnings announcements. While the effect on mean
abnormal trading volume is similar to that in the home markets, median abnormal trading
volume remains unchanged at -1.0000 throughout the event window because fewer than 50% of
Open Market stocks trade each day (see, e.g., the statistics on Trading Days (%) in Table 2,
Panel A). These results illustrate that the release of earnings information triggers substantial
trading volume reactions in the Open Market if general liquidity is sufficiently high. Hence, the
event study provides direct evidence that accounting information influences trading behavior of
Open Market investors.
18
4.
Global IFRS Adoption and Open Market Trading Volume
4.1
EMPIRICAL STRATEGY
We test the hypotheses developed in section 2 by using increases in Open Market trading
volume as a proxy for increases in cross-border equity investments by individual investors. This
proxy is valid for two reasons. First, liquidity in the Open Market is low so that the lead broker
typically cannot match offsetting orders by the individual investors (see section 3.2). Instead, the
lead broker has to carry out a countertrade in the respective home market to rebalance her
inventory. Trading volume in the Open Market therefore largely reflects changes in cross-border
equity investments by German individual investors rather than trades between these investors.
The second reason trading volume proxies for cross-border equity investment is that Open
Market investors have no restrictions on the buying side, while they can only sell stocks they
own. Thus, increases in Open Market trading volume are more likely to reflect increases rather
than decreases in cross-border equity investments. Appendix B provides statistics from a
proprietary brokerage dataset that support this conclusion.
The empirical analyses proceed in two steps. First, we examine the first hypothesis (H1) that
global IFRS adoption enhances cross-border investments by individual investors. Section 4.2
comprises the baseline analyses. Since the subsequent tests on cross-sectional variation depend
on the IFRS effect being estimated precisely, we devote a separate section 4.3 to alternative
identification strategies. Section 4.4 comprises the second step of our analyses where we test the
hypotheses on how the IFRS effect relates to the institutional environment of stocks (H2a) and to
proxies for individual investors’ attention (H2b).
19
4.2
BASELINE ANALYSES
4.2.1 Research Design
In this section, we test the first hypothesis (H1) that global IFRS adoption enhances crossborder equity investments by individual investors. Our proxy for cross-border equity investments
is trading volume in the Open Market denoted as FSE Trading Volume.20 The key independent
variable is Post-FY2005, a dummy variable that equals one (zero) in periods after (before and
including) the fiscal year-end of first-time mandatory adoption. For example, if the fiscal yearend of first-time mandatory adoption is in December 2005, Post-FY2005 has a value of one from
2006H1 onwards.21 To test the impact of global IFRS adoption, we interact Post-FY2005 with
binary variables that indicate mandatory IFRS adopters (Mandatory) and voluntary IFRS
adopters (Voluntary), respectively. These interaction terms capture the average effect of global
IFRS adoption on Open Market trading activity for the respective group of companies relative to
the control group of non-IFRS adopters. Combining these variables results in the following basic
regression specification:
FSE Trading Volume =
β0 + β1 Post-FY2005 +
β2 Post-FY2005*Mandatory + β3 Post-FY2005*Voluntary +
β4 Voluntary*IFRS + Σ βj Controlsj + ε
[1]
20
We use trading volume from both the floor and XETRA to calculate FSE Trading Volume. FSE Trading Volume
is identical to Trading Volume (Euro) for Frankfurt Stock Exchange in Table 2, Panel A. The results are similar
when we use trading volume from the floor only (see Appendix A) or percentage trading volume, that is, the
ratio of shares traded and the number of shares outstanding. Since FSE Trading Volume is highly skewed, we use
the natural logarithm to mitigate the influence of outliers. To ensure computing the natural logarithm for all firm
periods, we replace raw values of zero by a small firm-specific constant. This constant is defined as the average
market value divided by the average number of shares outstanding, divided by the number of exchange trading
days during the firm period (i.e., we assume that exactly one stock was traded during the firm period).
21
Many listed companies in Germany adopted IFRS well before global IFRS adoption took off, either voluntarily
(e.g., Leuz and Verrecchia, 2000; Daske, 2006) or due to exchange regulation of the former New Market (e.g.,
Leuz, 2003). These early adoptions enabled German investors to familiarize themselves at an early stage with
IFRS through their investments in domestic stocks. We therefore assume that Open Market investors react
promptly to global IFRS adoption. We test the sensitivity of our results to this assumption in section 4.3.1.
20
where Voluntary*IFRS is an interaction term that equals one (zero) after (before) voluntary IFRS
adoption and Controlsj denotes the set of control variables. Consistent with our main hypothesis,
we expect the coefficient estimate on Post-FY2005*Mandatory to be positive, that is, β2 > 0. To
the extent that the comparability benefits of global IFRS adoption spill over to voluntary
adopters, we also expect a positive coefficient estimate on Post-FY2005*Voluntary (β3).
Following related IFRS literature (e.g., Daske et al., 2008), we estimate regression
specification [1] after including firm fixed-effects. The goal of this difference-in-differences
approach is to identify the relationship between a treatment (IFRS introduction) and an
endogenous variable (Open Market trading activity) by comparing the treatment’s impact on
affected firms (treatment group) with its impact on unaffected firms (control group). To ensure
that OLS estimation produces consistent standard errors, we use standard errors clustered by
country. Note that in our model the estimated effect of global IFRS adoption is exclusively
determined by firms that are part of the Open Market sample both before and after IFRS
introduction. Hence, differences in the composition of the Open Market sample pre- versus postIFRS do not directly influence the regression outcomes.
Since we estimate a firm fixed effects model, our controls are confined to variables that
capture firm-specific changes over time. We include Market Value, Home Trading Volume and
No. of German Inst. Investors to control for changes in market values, changes in trading activity
in the respective home markets and changes in the number of German institutional investors,
respectively. These control variables are designed to ensure that our results do not reflect already
documented effects from global IFRS adoption such as decreases in the cost of equity capital (Li,
2010), liquidity increases in the home markets (Daske et al., 2008) or increases in cross-border
equity investments by institutional investors (DeFond et al., 2011; Yu, 2010). German/English
21
Reporting is included to ensure that the results are not attributable to firms that start preparing
their financial statements in German or English. Finally, Spread Difference controls for changes
in trading costs measured by the difference between the bid-ask spread at FSE and the respective
home market. We expect the coefficient estimate(s) on Spread Difference (all other control
variables) to be negative (positive).22
4.2.2 Empirical Findings
Table 4 presents regression results of the trading volume analysis. Regression models 1
(without controls), 2 and 3 (with controls) are based on the full sample and show that both
mandatory and voluntary IFRS adopters experience a strong and statistically significant increase
in Open Market trading volume following global IFRS adoption. For example, the coefficient
estimate on Post-FY2005*Mandatory (Post-FY2005*Voluntary) in model 2 is 0.679 (0.459)
which corresponds to an increase in percentage trading volume of 97% (58%) relative to the
control group. Untabulated analyses reveal that this coefficient estimate drops to 0.444 (0.200) if
U.S. firms, which constitute over 50% of the full sample, are excluded. This result demonstrates
that U.S. firms substantially affect the main results.
To avoid the possibility that our results are driven by sample firms from a few large countries
such as the U.K. and the U.S., we define a subsample that allows a maximum of 100 firms per
country. We create this subsample by (1) focusing on firms that are part of the Open Market
sample both before and after IFRS introduction,23 (2) sorting these firms within each country by
their average market value over the sample period and (3) selecting every N/100th firm if the
number of firms per country N is greater than 100. Regression models 4 (without controls), 5 and
22
Again, we use the natural logarithm for those variables that have highly skewed raw values. For details on all
control variables, see Table 2, Panel A.
23
Note that firms that do not fulfill this requirement have no direct impact on the estimated IFRS effect due to the
inclusion of firm fixed effects (see section 4.2.1).
22
6 (with controls) confirm that the strong IFRS effects for mandatory adopters hold when the
subsample is used. Specifically, mandatory IFRS adopters experience an increase in Open
Market trading volume of at least 45% (model 6) relative to the control group. This effect
corresponds to 8,250 (140) Euro in Open Market trading volume per firm period relative to the
full sample’s mean (median) of 18,340 (305) Euro (see Table 2, Panel A). The IFRS effect for
voluntary adopters is weaker in the subsample regressions with t-statistics below 2.
The coefficient estimates on Voluntary*IFRS are statistically significant throughout all
regression specifications and suggest that following voluntary IFRS adoption Open Market
trading volume more than doubles relative to the rest of the respective sample. Note, however,
that these estimates are determined by a small group of only 27 (21) voluntary adopters in the
full sample (subsample) that switched to IFRS in fiscal years 2002, 2003 or 2004, and were
tradable in the Open Market both before and after adoption. For the remaining 105 (73)
voluntary adopters in the full sample (subsample), the firm fixed effects capture potential effects
of voluntary IFRS adoption on Open Market trading volume. In all regressions, the coefficient
estimates on the control variables have the expected sign and are statistically significant, except
for German/English Reporting24 and No. of German Inst. Investors.
Taken together, the regression results provide strong evidence consistent with hypothesis H1
that global IFRS adoption enhances cross-border equity investments by individual investors.
24
There are no switches to German reporting in our dataset. Since our dataset does not comprise financial reports
before fiscal year 2003, we only capture switches to English reporting between fiscal year 2004 and 2007. We
document 47 (269) switches in fiscal year 2005 (in total). At the country level, most switches are carried out by
firms from Hong Kong (71), France (43), Greece (27), Spain (25) and Sweden (23). Where comparable, our
statistics are similar to those presented by Jeanjean et al. (2011).
23
4.3
ALTERNATIVE IDENTIFICATION OF THE IFRS EFFECT
4.3.1 Shifting the Switch of the Post-IFRS Dummy
The regression results presented in the previous section are based on the assumption that Open
Market trading volume reacts to financial statement information immediately after the respective
fiscal year-end. We therefore defined that the key independent variable Post-FY2005 switches in
the first firm period following the end of fiscal year 2005. In this section, we gauge the impact of
this research design choice by defining Post-FY2005 to switch up to two periods earlier or later
(see Christensen et al., 2011, for a similar strategy).
Table 5, Panel A, reports the results which are based on re-estimations of model 2 and 5 in
Table 4, respectively. While the coefficient estimate on Post-FY2005*Mandatory remains
largely unchanged for the regressions based on the full sample, the subsample results show that
both the coefficient estimate and the t-statistic peak when Post-FY2005 is defined as in the
previous section. The untabulated coefficient estimates on Post-FY2005*Voluntary are similar
and (not) statistically significant for the full sample (subsample) throughout all regressions. The
subsample results suggest that there is a structural break in Open Market trading volume around
global IFRS adoption. More importantly, this structural break is confined to stocks of mandatory
IFRS adopters. To the extent that the subsample is more powerful than the full sample in
disentangling a potential IFRS effect, this result lends support to our first hypothesis H1 of a
causal relation between global IFRS adoption and cross-border equity investments by individual
investors.
4.3.2 Within-Country Analysis
In this section, we exploit institutional peculiarities in the United Kingdom to gain further
insights on the existence of an IFRS effect on Open Market trading volume. To this end, we use
24
mandatory IFRS adopters listed in the Main Market at London Stock Exchange (LSE) as the
treatment group and companies listed in the Alternative Investment Market (AIM) at LSE as the
control group. AIM companies were not required to adopt IFRS before fiscal year 2007 and
therefore are excluded from the Open Market sample presented in Table 1. We delete firmperiods of AIM companies after they adopted IFRS. This procedure yields a sample of 2,104
(437) firm periods from 302 (93) mandatory IFRS adopters from the Main Market (AIM
companies). Based on hypothesis H1, we expect that following global IFRS adoption firms from
the Main Market experience an increase in Open Market trading volume relative to AIM
companies. Table 5, Panel B, provides evidence that is consistent with this expectation. For
example, the coefficient estimate on Post-FY2005*Mandatory in the regression model with
control variables is 0.990 (t-statistic 2.34) which corresponds to a relative increase in percentage
trading volume of more than 100%.
4.4
CROSS-SECTIONAL VARIATION IN THE IFRS EFFECT
4.4.1 Research Design
In this section, we examine cross-sectional variation in the estimated IFRS effect on Open
Market trading volume to test our second set of hypotheses (H2a) and (H2b). Similar to prior
literature (e.g., Daske et al., 2008), we interact the key independent variables in regression
specification [1] with a binary variable Conditional that partitions the treatment group. This
approach translates into the following basic regression specification:
FSE Trading Volume =
β0 + β1 Post-FY2005 +
β2 Post-FY2005*Mandatory + β3 Post-FY2005*Mandatory*Conditional +
β4 Post-FY2005*Voluntary + β5 Post-FY2005*Voluntary*Conditional +
β6 Voluntary*IFRS + Σ βj Controlsj + ε
[2]
25
where all variables are defined as in regression specification [1]. The partitioning variable
Conditional explains systematic cross-sectional variation in the estimated IFRS effect for
mandatory (voluntary) adopters if the interaction term Post-IFRS*Mandatory*Conditional (PostIFRS*Voluntary*Conditional) is statistically significant.
To test hypothesis (H2a) on the relation between the IFRS effect and the institutional
environment of the stocks, we use the following country- and industry-level variables to partition
the treatment sample (DeFond et al., 2011; Yu, 2010): (1) Long Accounting Distance is based on
the Bae et al. (2008) summary score of how a country’s local GAAP differs from IFRS on 21
key accounting dimensions and equals one (zero) for countries with a score greater (equal to or
less) than the sample median of 9. (2) Strong Credibility is based on the earnings management
score from Leuz et al. (2003) and takes a value of one (zero) for countries with a score of less
(equal to or greater) than the sample median of 18.3. (3) Large Δ Uniformity is based on the
changes in uniformity measure from DeFond et al. (2011) and equals one (zero) for industrycountry clusters with changes in uniformity greater (equal to or less) than the sample median of
39.67. (4) Strong Credibility * Large Δ Uniformity is the interaction of the second and third
variable. It takes a value of one if both variables equal one, and zero otherwise.
To test hypothesis (H2b) whether the IFRS effect is more pronounced for attention-grabbing
stocks, we use the following firm-level variables to proxy for individual investors’ attention and
partition the treatment sample: (1) Increase in Total Media Coverage equals one (zero) if
worldwide media coverage is, on average, higher (lower or the same) after fiscal year 2005 than
before. Total media coverage is measured as the number of search results in the Google News
archive (http://news.google.com/archivesearch) in any language. This number reflects the
number of articles that were published on Google News during the relevant period and that
26
contain either the company name (as provided by Worldscope item WC06001) or the firmspecific ISIN code. (2) Increase in German Media Coverage is similar to the first variable except
that it only counts Google News archive search results in German language. 25 The media
coverage variables are based on the notion that stocks in the news are more likely to draw
individual investors’ attention (Barber and Odean, 2008). (3) Large IFRS Restatements equals
one (zero) if the percentage difference between the restated net income under IFRS and the
originally reported net income under local GAAP for fiscal year 2004 is above (below or equal
to) the median percentage difference of 2.17%. Restatement information is from Worldscope
(item WC01551R) and only available for a subset of mandatory IFRS adopters. The underlying
assumption of this variable is that mandatory IFRS adopters are more likely to trigger individual
investors’ attention if the transition from local GAAP to IFRS suggests a substantially positive
impact on net income. (4) Strong EA Reactions equals one if the average Open Market trading
volume during the three-day window around the earnings announcement is higher than the
average Open Market trading volume over the relevant firm period, and zero otherwise. To the
extent that earnings announcements are events that trigger attention-driven Open Market trading
volume (see section 3.3.2), firm periods where trading volume is clustered around these events
are likely to reflect periods of relatively high attention by individual investors.
4.4.2 Empirical Findings
Table 6 presents results of the analyses on cross-sectional variation in the estimated IFRS
effect on Open Market trading volume. All reported regressions are based on the subsample used
in the previous sections and include the same control variables as regression model 5 in Table 4.
25
The number of search results in the Google News archive in any language (German language only) varies
between 0 (0) and 1,615,000 (26,800) per firm period with a mean of 4,656 (88) and a median of 82 (2).
27
Panel A provides evidence that the IFRS effect is not related to institutional variables at the
country- or industry-level as none of the treatment sample partitions load significantly.
Untabulated analyses confirm that other institutional variables such as the Rule of Law or
Membership in the European Union (e.g., Daske et al., 2008) neither explain cross-sectional
variation in the estimated IFRS effects.26 These results are consistent with hypothesis (H2a) that
the effect of global IFRS adoption on cross-border equity investments by individual investors is
not related to the institutional environment of the stocks. However, since the Open Market
sample is tilted towards more visible and transparent firms (see section 3.3.1), it may not provide
sufficient variation in determinants of institutional quality to enable a powerful test of this
hypothesis. We therefore abstain from drawing strong conclusions with regard to the relation
between the estimated IFRS effect and the institutional environment of the stocks.
In contrast, the Open Market sample provides a useful setting to test hypothesis (H2b),
because it comprises several thousands of stocks and, thus, far more than a single individual
investor is able to draw her attention on. Panel B shows that the estimated IFRS effect is related
to proxies for increases in individual investors’ attention. In the regression partitioning the
sample based on Increase in Total Media Coverage, the coefficient estimate on
IFRS*Mandatory*Conditional is positive (0.258) and statistically significant (t-statistic 2.02)
suggesting that the IFRS effect is particularly strong for firms that experience enhanced
worldwide media coverage after mandatory IFRS adoption. This pattern is even more
pronounced when the increase in media coverage is based only on articles in German language
(coefficient estimate: 0.455, t-statistic: 2.78). The third regression illustrates that mandatory
26
For example, the coefficient estimate on Post-FY2005*Mandatory*Conditional is -0.034 with a t-statistic of
-0.18 if the regression model in Table 6 is re-estimated with EU Membership as partitioning variable. This result
also suggests that other EU regulation such as the Transparency Directive that was implemented towards the end
our sample period does not drive the estimated IFRS effect in our analyses (Christensen et al., 2011).
28
adopters that report a substantially higher net income under IFRS than under local GAAP
experience a stronger increase in Open Market trading volume following global IFRS adoption.
This effect is statistically significant at the 10% level. The final regression of Panel B shows that
the IFRS effect is significantly stronger during firm periods where Open Market trading volume
is clustered around earnings announcements. Taken together, these findings are consistent with
hypothesis (H2b) that the effect of global IFRS adoption on cross-border equity investments by
individual investors is more pronounced for attention-grabbing stocks.
5.
Conclusions
This study examines the impact of global IFRS adoption on cross-border equity investments
by individual investors. Our proxy for these cross-border equity investments is trading volume in
the Open Market, a segment at FSE designed for German individual investors to trade foreign
(i.e. non-German) stocks. The empirical analyses provide evidence that stocks experience a
significant increase in Open Market trading volume following mandatory adoption of IFRS. This
effect is more pronounced for attention-grabbing stocks (e.g., stocks experiencing an increase in
media coverage following IFRS adoption). Our results suggest that global IFRS adoption
enhances cross-border equity investments by individual investors. However, this effect does not
materialize equally across stocks due to individual investors’ limited attention.
Taken at face value, our results support the efforts by the IASB and standard setters around
the world to foster a single global set of financial reporting standards. However, we urge caution
in interpreting the results in this study. First, our dataset does not allow us to directly observe
through which channels global IFRS adoption influences individual investors’ decision making
and trading in the Open Market. Despite our extensive efforts to identify the IFRS effect, we can
therefore not fully rule out the possibility that the results reflect concurrent changes that are not
29
related to global IFRS adoption. Second, our analyses are based on the Open Market sample. The
Open Market sample is a large but selected subset of the universe of global stocks comprising
companies that are significantly more visible and transparent. It is an open issue whether our
results on individual investor demand also apply to other less-visible and less-transparent stocks.
However, this potential selection issue is common in IFRS literature as most studies rely on
commercial databases that suffer from biases towards large firms (e.g., Brüggemann et al.,
2012). Moreover, the size of the Open Market sample compares well to sample sizes in related
studies. For example, while DeFond et al. (2011) examine 1,365 mandatory IFRS adopters from
14 countries, the Open Market sample comprises 1,693 mandatory IFRS adopters from 20
countries (see Table 1, Panel A). Third, we recognize that individual Open Market investors
taking active positions in individual foreign stocks are not necessarily representative of the
universe of individual investors in the global economy. However, the IASB’s and other standard
setters’ efforts are naturally targeted towards investors who use financial statement information
in their investment decisions. Individual investors who engage in cross-border investments are an
important subset of this group.
30
APPENDIX A
Competitors of the FSE Lead Broker
The lead brokers at FSE face competition from alternative trading channels within and outside
FSE. This appendix provides details on these trading channels.
Within FSE, there are two trading platforms that work in parallel: (1) the floor where the lead
brokers operate and (2) the fully electronic XETRA where quotes are automatically determined
by an open limit order book. Order processing costs are lower in XETRA, but its anonymity
induces higher costs arising from adverse selection. Since the adverse selection component
becomes more important when trading volume is low, the floor is more attractive for less liquid
stocks (Theissen, 2002). Consistent with this evidence, we find that trading activity in the illiquid
Open Market usually takes place in the floor, but shifts to XETRA if liquidity is high.
Specifically, trading activity in the floor is higher in 97.71% of all firm periods in the Open
Market sample. In contrast, the difference in mean trading volume (Trading Volume (Euro)) is
less pronounced between both systems (floor: 11,430 Euro, XETRA: 6,910 Euro). For our main
analyses, we use trading volume from both the floor and from XETRA. We perform the same set
of tests using only trading volume from the floor. The results remain largely unchanged. For
example, the coefficient estimate on Post-FY2005*Mandatory is 0.384 with a t-statistic of 2.61 if
regression model 5 in Table 4 is re-estimated with floor trading volume as the dependent
variable.
Outside FSE, trading segments similar to the Open Market exist at regional German
exchanges in Berlin, Stuttgart and Munich. Datastream data indicates that these segments are
smaller and less liquid than the Open Market in Frankfurt. Applying the selection criteria
described in section 3.2, we identify a sample of 13,805 firm periods (6% of the DS Universe)
for Berlin, 2,872 firm periods (1%) for Stuttgart and 441 firm periods (0%) for Munich Stock
31
Exchange. Hence, the Open Market sample (43,671 firm periods) is more than twice as large as
the combined samples of the other three German exchanges. The mean proportion of non-zero
trading volume days is 2.41% in Berlin, 3.79% in Stuttgart and 4.76% in Munich, compared to
24.73% at FSE (see the statistics on Trading Days (%) in Table 2, Panel A). We conclude that
trading is a rare phenomenon at the regional German exchanges. To address potential coverage
and quality issues with Datastream data, we analyze a further proprietary dataset from FSE that
contains monthly trading volume in all Open Market stocks separately for FSE and, if applicable,
for other German exchanges.27 Descriptive statistics show that FSE combines 82% of the
aggregate trading volume in these stocks. Taken together, these results suggest that the Open
Market lead broker at FSE faces little competition from other German exchanges.
27
Note that the FSE dataset is confined to Open Market stocks, i.e. stocks that are tradable at FSE. However,
analysis of Datastream data shows that some stocks are tradable in Berlin, Stuttgart and/or Munich that are not
included in the Open Market.
32
APPENDIX B
Comparison of Transaction Costs: Open Market versus Home Markets
German investors have two options when trading foreign stocks that are included in the Open
Market. They can either trade at FSE or they can trade abroad, that is, in the respective home
market. Both options involve transaction costs that differ considerably in nature. This appendix
seeks to illustrate these differences by means of an example.
Table A1 shows concurrent quotes for Fiat stock (ISIN IT0001976403) at the home market in
Milan and at the Open Market in Frankfurt. We collect this information through the web-portal
of comdirect bank, the leading online broker for German individual investors. Table A2 presents
order fees at comdirect bank for trading stocks at Milan Stock Exchange (MSE) and FSE,
respectively. While MSE offers better prices (i.e., the MSE bid-ask spread is inside the FSE
quotes), comdirect bank clients incur lower order fees (both fixed and variable) for trading at
FSE. Lower order fees at FSE outweigh the price advantage at MSE if and only if the size of the
trade is sufficiently small.28 For example, a buy of 100 Fiat stocks would cost 785.06 Euro at
MSE, but only 772.01 Euro at FSE.29 In contrast, buying 1,000 Fiat stocks is cheaper at MSE
(7,609.42 Euro versus 7,617.48 Euro at FSE).30 The break-even where MSE turns into the more
cost-efficient trading alternative is about 500 units or 3,800 Euro in this particular case. This is
also reflected in the different quote sizes at MSE (around 10,000 stocks) and FSE (200 stocks).
Taken together, this example confirms our depiction of the Open Market as a platform for
German individual investors to trade small sizes of foreign stocks.
28
The difference in transaction costs between FSE and MSE is a monotonic function, because the price advantage
of MSE (about 0.5%) is larger than the advantage of FSE in variable order fees (about 0.12%). The advantage of
FSE in fixed order fees therefore has less impact on the total transaction costs the higher the trade size.
29
The order value (order processing costs) is (are) 756.00 (29.06) Euro at MSE and 760.00 (12.01) Euro at FSE.
30
The order value (order processing costs) is (are) 7,560.00 (49.42) Euro at MSE and 7,600.00 (17.48) Euro at
FSE.
33
Table A1: Concurrent Quotes for Fiat Stocks (ISIN IT0001976403)
This table presents concurrent quotes for Fiat stocks (ISIN IT0001976403) at the home market in Milan (German: Mailand) and
at the Open Market in Frankfurt. The information was retrieved from the website of comdirect bank (www.comdirect.de) on 22
April 2009. The upper part of the table contains information on the relevant exchange (Börse), the last price (Aktuell), the time
the last price was set (Zeit), the percentage difference between the last price and the price of the previous day (Diff. Vortrag) as
well as the trading volume in Euro (Tages-Vol.) and in units (Gehandelte Stück). The lower part of the table provides details on
the current bid (Geld) and ask quote (Brief), the time these quotes were set (Zeit), the percentage spread (Spread) as well as the
size of the current bid (Geld Stk.) and ask quote (Brief Stk.).
Table A2: Order Fees at comdirect bank
Type of Fee
Milan Stock Exchange (MSE)
Frankfurt Stock Exchange (FSE)
Order Provision
7.90 Euro + 0.25% of order value
(min. 12.90 Euro, max. 62.90 Euro)
4.90 Euro + 0.25% of order value
(min. 9.90 Euro, max. 59.90 Euro)
Brokerage Fee
-
0.08% of order value
Exchange Fee
0.20% of order value
(min. 8.66 Euro)
0.0015% of order value
(min. 1.50 Euro)
Delivery Fee
7.50 Euro
-
This table presents order fees at comdirect bank for trading stocks at Milan Stock Exchange (MSE) and Frankfurt Stock
Exchange (FSE), respectively. The information was provided by comdirect bank customer support. Order Provision is the fee
comdirect bank charges for its services. All other fees are charges by third parties that comdirect bank passes on. Brokerage Fee
(Exchange Fee) is a charge for the FSE lead broker (respective exchange). Delivery Fee is a charge for stock clearing.
34
APPENDIX C
Statistics from Brokerage Dataset
This appendix describes a proprietary dataset from a German online broker which comprises
information on trading activities and portfolio positions for a sample of approximately 3,000
German individual investors during the period January 1997 – April 2001.31 We use this dataset
to empirically validate the following two arguments.
First, we examine where the individual investors covered by the brokerage dataset trade
foreign (i.e., non-German) stocks. Table B1 shows that these investors make 93.5% (113,352 /
(113,352 + 7,817) of the trades in foreign stocks through the Open Market and that trade sizes in
the Open Market are substantially lower than trades that are made through the home markets.
Untabulated statistics show that the mean size for trades in the Open Market (home markets) is
4,840 (11,289) Euro. These results confirm that German individual investors trade foreign stocks
primarily through the Open Market, especially when trade sizes are low.
The second analysis focuses on the relation between changes in Open Market trading volume
and changes in portfolio holdings. To ensure consistency with the main analyses, we calculate
these changes for each semiannual firm-period and aggregate over all individual investors in the
brokerage dataset. Table B2 illustrates that an increase in aggregate Open Market trading volume
coincides with an increase in aggregate portfolio holdings in the same foreign stock in 763 out of
1,201 firm periods (64%). Similarly, a decrease in aggregate Open Market trading volume
coincides with a decrease in aggregate portfolio holdings in the same stock in 651 out of 1,031
firm periods (63%). The Pearson correlation coefficient (or phi coefficient) between changes in
Open Market trading volume and changes in portfolio holdings is 0.27 and statistically
31
The full dataset is described in detail in Glaser and Weber (2009). We are grateful to Markus Glaser for
providing us with the statistics presented in this appendix.
35
significant at the 1% level. These results provide evidence that changes in Open Market trading
volume are positively correlated with changes in portfolio holdings and, thus, cross-border equity
investments.
Table B1: Open Market versus Home Markets
Number of Trades
Trade Size
in Euro (converted)
from
to
Open Market
Home Markets
500
7,309
6%
178
2%
500
1,000
12,485
11%
232
3%
1,000
2,500
34,185
30%
933
12%
2,500
5,000
28,288
25%
1,586
20%
5,000
10,000
18,920
17%
2,134
27%
10,000
25,000
9,826
9%
2,031
26%
25,000
50,000
1,875
2%
551
7%
464
0%
172
2%
113,352
100%
7,817
100%
50,000
Total
This table presents statistics on foreign (i.e., non-German) stock trading by individual investors in the brokerage dataset. The
dataset covers the period January 1997 – April 2001. The table distinguishes between trades in the Open Market and trades in the
home markets. The markets are identified via the currency of the trade. For example, U.S. stocks traded in Euro (U.S. Dollar) are
classified as Open Market (home market) trades. Observations where such a classification cannot be made unambiguously (e.g.,
Italian stocks traded in Euro) are eliminated. These eliminations are more prevalent after 1999 when many European exchanges
started to quote stocks in Euro. The table shows the number of trades and related trade sizes in Euro (converted at the historical
exchange rate for non-Euro stocks) across markets.
Table B2: Changes in Open Market Trading Volume versus Changes in Portfolio Holdings
Change in
Aggregate Portfolio
Holdings
Change in Aggregate
Open Market Trading Volume
Decrease
Total
Increase
Decrease
651
438
1,089
Increase
380
763
1,143
1,031
1,201
2,232
Total
This table shows how changes in aggregate Open Market trading volume relate to changes in aggregate portfolio holdings in the
same foreign (i.e., non-German) stock. The analysis is based on the brokerage dataset described in Table B1. Aggregate Open
Market trading volume is the total number of stocks of a firm that the individual investors in the brokerage dataset bought or sold
during semiannual firm periods. Aggregate portfolio holdings reflect the total number of stocks of a firm held by these investors
at the end of the respective semiannual firm period. The resulting sample comprises 2,232 firm periods.
36
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40
TABLE 1
Sample Composition by Country and Firm Period
Panel A: Treatment Group
IFRS Adoption
Countries
IFRS Voluntary
IFRS Mandatory
Local GAAP
Total
Unique Firms
Unique Firms
Unique Firms
Firm-Periods
Open
Market
Australia
DS
Universe
Share
Open
Market
DS
Universe
Share
Open
Market
DS
Universe
Share
Open
Market
DS
Universe
Share
5
14
36%
301
1,455
21%
-
-
-
1,908
15,122
13%
Austria
46
53
87%
15
24
63%
-
-
-
437
744
59%
Belgium
10
22
45%
28
98
29%
-
-
-
280
1,279
22%
Czech Republic
5
14
36%
3
10
30%
-
-
-
43
169
25%
Denmark
8
17
47%
22
113
19%
-
-
-
256
1,554
16%
Finland
8
10
80%
39
124
31%
-
-
-
469
1,607
29%
France
3
13
23%
178
550
32%
-
-
-
1,543
6,462
24%
Greece
3
10
30%
29
265
11%
-
-
-
286
2,898
10%
Hong Kong
5
15
33%
349
1,038
34%
-
-
-
2,187
10,416
21%
Hungary
16
18
89%
2
7
29%
-
-
-
144
268
54%
Ireland
0
0
-
18
37
49%
-
-
-
115
396
29%
Italy
1
2
50%
97
263
37%
-
-
-
839
2,894
29%
Luxembourg
0
4
0%
1
14
7%
-
-
-
5
159
3%
Netherlands
2
3
67%
29
65
45%
-
-
-
180
800
23%
Norway
2
4
50%
54
172
31%
-
-
-
319
1,748
18%
Philippines
0
1
0%
0
166
0%
-
-
-
0
1,645
0%
Poland
0
17
0%
0
152
0%
-
-
-
0
1,344
0%
Portugal
3
5
60%
14
40
35%
-
-
-
164
502
33%
South Africa
10
27
37%
38
291
13%
-
-
-
225
3,275
7%
Spain
0
0
-
85
127
67%
-
-
-
693
1,399
50%
Sweden
3
7
43%
89
288
31%
-
-
-
757
3,302
23%
United Kingdom
2
10
20%
302
893
34%
-
-
-
2,118
9,264
23%
132
266
50%
1,693
6,192
27%
-
-
-
12,968
67,247
19%
Total
Panel B: Control Group
Non-IFRS Adoption
Countries
IFRS Voluntary
IFRS Mandatory
Local GAAP
Total
Unique Firms
Unique Firms
Unique Firms
Firm-Periods
Open
Market
DS
Universe
Share
Open
Market
DS
Universe
Share
Open
Market
DS
Universe
Argentina
-
-
-
-
-
-
1
58
2%
4
485
Brazil
-
-
-
-
-
-
11
353
3%
57
2,362
2%
Canada
-
-
-
-
-
-
456
1,380
33%
2,375
11,043
22%
Chile
-
-
-
-
-
-
1
190
1%
4
1,671
0%
China
-
-
-
-
-
-
30
1,508
2%
183
11,345
2%
Colombia
-
-
-
-
-
-
0
22
0%
0
158
0%
India
-
-
-
-
-
-
0
946
0%
0
6,095
0%
Indonesia
-
-
-
-
-
-
68
262
26%
467
2,370
20%
Israel
-
-
-
-
-
-
12
108
11%
50
996
5%
Japan
-
-
-
-
-
-
458
2,635
17%
4,441
29,032
15%
Malaysia
-
-
-
-
-
-
0
857
0%
0
8,978
0%
Mexico
-
-
-
-
-
-
19
127
15%
49
1,117
4%
Morocco
-
-
-
-
-
-
0
18
0%
0
147
0%
Pakistan
-
-
-
-
-
-
0
52
0%
0
359
Share
Open
Market
DS
Universe
Share
1%
0%
(continued)
TABLE 1 (continued)
Panel B: Control Group
Non-IFRS Adoption
Countries
IFRS Voluntary
IFRS Mandatory
Local GAAP
Total
Unique Firms
Unique Firms
Unique Firms
Firm-Periods
Open
Market
DS
Universe
Share
Open
Market
DS
Universe
Share
Open
Market
South Korea
-
-
-
-
-
-
0
Sri Lanka
-
-
-
-
-
-
0
Taiwan
-
-
-
-
-
-
Thailand
-
-
-
-
-
United States
-
-
-
-
Total
-
-
-
-
DS
Universe
Share
Open
Market
DS
Universe
740
0%
0
8,117
0%
23
0%
0
213
0%
2
790
0%
4
8,888
0%
-
0
402
0%
0
3,902
0%
-
-
2,754
5,351
51%
23,069
53,247
43%
-
-
3,812
15,822
24%
30,703
150,525
20%
Share
Panel C: Treatment and Control Group
Firm Period
Open
Market
IFRS Voluntary
IFRS Mandatory
Local GAAP
Total
Unique Firms
Unique Firms
Unique Firms
Unique Firms
DS
Universe
Share
Open
Market
DS
Universe
Share
Open
Market
DS
Universe
Share
Open
Market
DS
Universe
Share
2002H1
55
181
30%
488
3,996
12%
1,645
9,551
17%
2,188
13,728
16%
2002H2
55
182
30%
516
4,062
13%
1,776
9,595
19%
2,347
13,839
17%
2003H1
54
185
29%
533
4,241
13%
1,834
9,689
19%
2,421
14,115
17%
2003H2
62
196
32%
602
4,410
14%
1,906
9,823
19%
2,570
14,429
18%
2004H1
85
210
40%
680
4,672
15%
2,045
10,834
19%
2,810
15,716
18%
2004H2
87
210
41%
728
4,838
15%
2,149
11,373
19%
2,964
16,421
18%
2005H1
96
225
43%
834
5,035
17%
2,323
11,762
20%
3,253
17,022
19%
2005H2
102
229
45%
934
5,219
18%
2,502
12,030
21%
3,538
17,478
20%
2006H1
100
223
45%
1,048
5,501
19%
2,629
12,800
21%
3,777
18,524
20%
2006H2
105
220
48%
1,150
5,606
21%
2,739
12,949
21%
3,994
18,775
21%
2007H1
106
214
50%
1,265
5,646
22%
2,874
13,219
22%
4,245
19,079
22%
2007H2
108
211
51%
1,473
5,688
26%
3,047
13,360
23%
4,628
19,259
24%
2008H1
112
211
53%
1,590
5,636
28%
3,234
13,540
24%
4,936
19,387
25%
Total Firm-Periods
1,127
2,697
42%
11,841
64,550
18%
30,703
150,525
20%
43,671
217,772
20%
Total Unique Firms
132
266
50%
1,693
6,192
27%
3,812
15,822
24%
5,637
22,280
25%
This table presents the sample composition by country and across time. The Datastream Universe (DS Universe) comprises a
total of 217,772 semiannual firm periods from 41 countries between 2002H1 and 2008H1 with sufficient data on trading volume,
stock returns and accounting standards followed. H1 (H2) indicates the first (second) half of the respective year. We split the DS
Universe into two groups: (1) countries that introduced IFRS in fiscal year 2005 (treatment group), and (2) countries that required
domestic accounting standards throughout the sample period (control group). We include only companies in the treatment group
that switched from local GAAP to IFRS before (IFRS Voluntary) or in fiscal year 2005 (IFRS Mandatory). The control group
consists of companies that used domestic accounting standards (Local GAAP) throughout the sample period. Information on
accounting standards followed is from Worldscope. For simplicity, we refer to Hong Kong as a country in our analyses, although
it has the status of a Special Administrative Region of the People’s Republic of China. Using proprietary data from Frankfurt
Stock Exchange (FSE) we identify all firms within the DS Universe whose stocks are traded in the Open Market. The Open
Market sample (Open Market) is a subset of the DS Universe and consists of 43,671 firm periods from 31 countries. Share
indicates the proportion of the Open Market sample relative to the DS Universe. Panel A (Panel B) reports the number of unique
firms and the number of firm periods by country for the treatment (control) group. Panel C shows the number of unique firms
across time for treatment and control group combined.
TABLE 2
Descriptive Statistics
Panel A: Open Market Sample
Variables
Firm-Periods
Mean
Std.Dev.
P1
P25
Median
P75
P99
Frankfurt Stock Exchange
Trading Volume (Euro)
43,671
18,340
133,673
0
46
305
1,889
429,491
Trading Days (%)
43,671
24.73%
31.41%
0.00%
2.36%
9.02%
36.15%
100.00%
No. of Trades
43,671
2.40
10.89
0.00
0.05
0.20
0.94
45.71
Trade Size (Euro)
39,815
2,659
10,754
60
845
1,713
3,178
15,358
Bid-Ask Spread (%)
38,913
5.24%
9.19%
0.40%
1.97%
3.13%
4.99%
50.00%
Trading Volume (Euro)
43,671
29,678,350
86,143,152
6,756
778,230
5,600,336
24,427,630
394,500,000
Trading Days (%)
43,671
96.55%
5.63%
70.40%
96.85%
97.62%
97.69%
100.00%
Bid-Ask Spread
38,913
0.70%
1.56%
0.03%
0.11%
0.28%
0.67%
6.70%
Market Value (m Euro)
43,671
4,904
15,128
7
196
875
3,516
65,046
German/English Reporting
38,205
0.83
No. of German Inst. Investors
35,049
0.48
1.77
0.00
0.00
0.00
0.00
9.50
EM Aggregate (Firm Level)
36,822
0.45
0.21
0.07
0.28
0.45
0.60
0.91
Home Markets
Other Variables
Panel B: Datastream Universe (excluding Open Market Sample)
Variables
Firm-Periods
Mean
Std.Dev.
P1
P25
Trading Volume (Euro)
174,101
2,369,077
20,538,086
126
28,431
Trading Days (%)
174,101
86.51%
20.50%
7.87%
88.19%
Bid-Ask Spread (%)
149,594
2.93%
7.06%
0.06%
Market Value (m Euro)
174,101
523
2,555
German/English Reporting
158,467
0.42
No. of German Inst. Investors
137,334
0.05
EM Aggregate (Firm Level)
131,550
0.51
Median
P75
P99
190,251
1,153,291
30,613,758
94.62%
97.60%
100.00%
0.41%
0.96%
2.53%
33.33%
2
32
106
342
6,468
0.40
0.00
0.00
0.00
0.00
1.50
0.21
0.08
0.36
0.51
0.67
0.93
Home Markets
Other Variables
This table reports descriptive statistics of all relevant variables for the Open Market sample (Panel A) and for the rest of the
Datastream (DS) Universe (Panel B). The DS Universe comprises a total of 217,772 semiannual firm periods from 41 countries
between 2002H1 and 2008H1. The Open Market sample is a subset of the DS Universe and consists of 43,671 firm periods from
31 countries. Each panel is subdivided into groups of variables that depend (Frankfurt Stock Exchange (FSE) and/or Home
Markets) or do not depend (Other Variables) on the trading venue. Trading Volume (Euro) is the firm period’s trading volume in
Euro divided by the number of exchange trading days during the firm period. Trading Days (%) is the number of exchange
trading days with non-zero trading volume divided by the number of exchange trading days during the firm period. No. of Trades
is the number of ticks divided by the number of exchange trading days during the firm period. Trade Size (Euro) is the firm
period’s trading volume in Euro divided by the number of trades during the firm period. FSE trading volume information is based
on a proprietary dataset from Frankfurt Stock Exchange. We use trading volume from both the floor and from XETRA to
compute FSE trading volume variables. Trading volume data for the home markets is retrieved from Datastream. No. of Trades
and Trade Size (Euro) are available for FSE only, because Datastream does not provide information on number of trades. Bid-Ask
Spread is the firm period’s median quoted spread (i.e., the difference between the closing bid and the closing ask price divided by
the midpoint). For FSE, we use bid-ask spreads from the floor. For the home markets, information on closing bid and ask prices
is obtained from Datastream (CRSP) for non-U.S. (U.S.) exchanges. Market Value (m Euro) is the market value of outstanding
equity in Million Euro at the end of the firm period (Datastream). German/English Reporting indicates whether a firm prepares
its financial statements in German or English language. We assume that all firms from Austria report in German and all firms
from Australia, Canada, Ireland, New Zealand, South Africa, the United Kingdom and the United States report in English. For all
other countries, we manually collect information on the reporting language from financial statements available on Thomson ONE
Banker. No. of German Inst. Investors is the number of institutional investors (mutual funds, pension funds, insurance companies,
TABLE 2 (continued)
hedge funds, private equity funds and venture capital funds) domiciled in Germany that hold shares of the firm during the firm
period. We obtain this information from the Thomson Financial Ownership quarterly data feed. For details on this database, see
Florou and Pope (2012). EM Aggregate (Firm Level) is the average within-country rank of two earnings management (EM)
measures: (1) the ratio of the firm level standard deviations of operating earnings and operating cash-flow (both scaled by lagged
total assets), and (2) the ratio of the absolute value of accruals and the absolute value of operating cash-flows. Accounting data is
obtained from Worldscope.
TABLE 3
Characteristics of the Open Market
Panel A: Determinants of Open Market Inclusion
Dependent Variable: Open Market Sample (0 = No, 1 = Yes)
Independent variables
Univariate
Log(Market Value)
Multivariate
0.329
0.015
0.060
(12.67)***
(0.14)
(0.66)
Log(Home Trading Volume)
German/English Reporting
Log(No. of German Inst. Investors)
EM Measure (Firm Level)
0.280
0.270
0.244
(8.77)***
(3.75)***
(4.19)***
1.045
0.557
(3.57)***
(2.12)**
0.847
0.177
(5.98)***
(1.35)
-0.750
-0.791
(-9.81)***
EM Measure (Country Level)
(-4.47)***
-0.035
-0.038
-0.027
(-3.56)***
(-4.41)***
(-3.21)***
0.416
0.173
0.099
(1.84)*
(0.82)
(0.52)
0.223
1.040
0.864
Log(MCAP/GDP)
Euro
Log(Distance Berlin - Capital)
Fixed Effects
(0.83)
(4.74)***
(3.83)***
-0.123
0.002
0.081
(-1.56)
(0.03)
(1.00)
Year
Year
0.31
0.36
217,772
133,072
-
Pseudo R-squared
Observations
Panel B: Abnormal Trading Volume of Open Market Stocks around Earnings Announcements
Day
Frankfurt Stock Exchange
Mean
Median
-5
0.0010
-1.0000
-4
-0.0989
-1.0000
-3
-0.0503
-2
-1
Home Markets
Mean
Median
-0.30
-0.0068
-0.1961
-0.33
-0.70
0.0017
-0.1850
-0.11
-1.0000
-0.44
-0.0227
-0.2016
-0.47
-0.0272
-1.0000
-0.30
0.0309
-0.1702
0.10
0.0147
-1.0000
0.58
0.1321
-0.0693
1.45
0
0.5795
-1.0000
4.91 ***
0.9632
0.4005
5.82 ***
1
1.1099
-1.0000
6.63 ***
1.1195
0.4890
6.29 ***
2
0.3569
-1.0000
3.34 ***
0.4165
0.1266
3.53 ***
3
0.1903
-1.0000
1.54
0.2714
0.0254
2.55 **
4
0.1512
-1.0000
1.17
0.1850
-0.0393
1.79 *
5
0.0585
-1.0000
0.20
0.1531
-0.0536
1.58
Observations
18,362
Rank Statistic
18,362
Rank Statistic
TABLE 3 (continued)
This table reports descriptive evidence on the determinants of Open Market inclusion (Panel A) and on trading volume reactions
around earnings announcements in the Open Market versus the respective home markets (Panel B). In Panel A, we relate the
likelihood of inclusion into the Open Market to various country- and firm-specific variables. The DS Universe comprises a total
of 217,772 semiannual firm periods from 41 countries between 2002H1 and 2008H1. The Open Market sample is a subset of the
DS Universe and consists of 43,671 firm periods from 31 countries. The dependent variable in all regressions is a binary variable
that equals one (zero) for all firm periods in the Open Market sample (the rest of the DS Universe). EM Measure (Country Level)
is an updated country-specific earnings management score based on Leuz et al. (2003). To construct this score we use all firms in
the DS Universe with sufficient accounting data in Worldscope for fiscal years 2001-2009. The score is the average countryspecific rank of three earnings management measures: (1) the country’s median ratio of the firm level standard deviations of
operating earnings and cash flow from operations (both scaled by lagged total assets), (2) the country’s Spearman correlation
between the change in accruals and the change in cash flow from operations (both scaled by lagged total assets), and (3) the
country’s median ratio of the absolute value of accruals and the absolute value of the cash flow from operations. MCAP/GDP is
the ratio of a country’s stock market capitalization to its Gross Domestic Product (GDP). Yearly ratios are obtained from the
World Bank (www.fsdi.org). We compute this variable as the mean ratio over the period 2001-2004. Euro distinguishes between
countries that have adopted the Euro as their national currency (variable equals one) and countries that have not (variable equals
zero). We collect this information from the European Central Bank (www.ecb.int). Distance Berlin – Capital is the distance
between Berlin, capital of Germany, and the capital of the respective country in kilometers. Data source is the French research
centre in international economics (CEPII). For a description of the remaining variables see Table 2. Panel A reports probit
coefficient estimates, and (in parentheses) t-statistics. The t-statistics are based on standard errors that are clustered by country.
We use the natural logarithm of the raw values where indicated. ***, ** and * indicate statistical significance at the 1%, 5% and
10% levels (two-tailed) respectively. In Panel B, we focus on Open Market stocks and compare abnormal trading volume around
earnings announcements at Frankfurt Stock Exchange (FSE) versus the respective home markets. The analysis is based on a
sample of 18,362 annual earnings announcement dates retrieved from IBES. Abnormal trading volume is the difference between
trading volume on the event day and the mean daily volume for that stock over the pre-announcement window (-120, -21), scaled
by the mean daily volume. Abnormal trading volume is winsorized, by event day, at the 99% level. Since the average Open
Market stock is traded on less than half of the exchange trading days (see descriptive statistics on Trading Days (%) in Table 2,
Panel A), the median abnormal trading volume at FSE remains at -1.000 throughout the event window. To test for significance
we use Corrado’s (1989) non-parametric rank test. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels
(two-tailed) respectively.
TABLE 4
The Effect of Global IFRS Adoption on Open Market Trading Volume
Dependent Variable: Log(FSE Trading Volume)
Independent Variables
Full Sample
(1)
Subsample (max. 100 Firms per Country)
(2)
(3)
0.052
-0.359
-0.315
(0.48)
(-6.82)***
0.912
0.679
(6.59)***
(5.23)***
(4)
(5)
(6)
0.361
-0.203
-0.279
(-11.20)***
(1.67)
(-1.46)
(-3.67)***
0.511
0.647
0.457
0.374
(5.42)***
(2.83)***
(2.96)***
(2.91)***
Test Variables
Post-FY2005
Post-FY2005 * Mandatory
Post-FY2005 * Voluntary
Voluntary * IFRS
0.841
0.459
0.163
0.523
0.206
0.001
(3.16)***
(2.17)**
(0.97)
(1.61)
(0.97)
(0.00)
1.247
0.737
0.847
1.269
0.759
0.987
(4.16)***
(2.85)***
(2.82)***
(4.32)***
(3.56)***
(3.38)***
Control Variables
Log(Market Value)
Log(Home Trading Volume)
0.304
0.245
0.672
0.715
(1.33)
(1.05)
(4.90)***
(4.79)***
0.864
0.894
0.723
0.733
(12.68)***
(12.44)***
(10.44)***
(9.76)***
German/English Reporting
Log(No. of German Inst. Investors)
Log(Spread Difference)
0.008
0.174
(0.03)
(0.49)
0.040
0.181
(0.32)
(1.13)
-0.380
-0.322
(-11.30)***
(-5.86)***
Fixed Effects
Firm
Firm
Firm
Firm
Firm
Firm
R-squared
0.72
0.76
0.79
0.74
0.78
0.81
43,671
43,671
27,305
12,063
12,063
7,867
Observations
This table presents results from regressions that relate Open Market trading volume at Frankfurt Stock Exchange (FSE) to IFRS
adoption. The Open Market sample comprises a total of 43,671 semiannual firm periods from 31 countries between 2002H1 and
2008H1. Regression model 1-3 (4-6) is based on the full sample (a subsample that allows a maximum of 100 firms per country).
To create the subsample we focus on firms that are part of the Open Market sample both before and after IFRS introduction, sort
these firms within each country by their average market value over the sample period and then select every N/100th firm if the
number of firms per country N is greater than 100. Post-FY2005 equals one (zero) for fiscal year 2005 and later (2004 and
earlier). We define that Post-FY2005 switches in the first firm period following the end of fiscal year 2005, that is, if the fiscal
year ends in December 2005 Post-FY2005 switches in 2006H1. Voluntary (Mandatory) is a firm-level dummy variable and
indicates companies that switched from local GAAP to IFRS before (in) fiscal year 2005. IFRS is a binary variable measured at
the firm period level that indicates whether IFRS is applied or not. Spread Difference is the difference between Bid-Ask Spread at
FSE and Bid-Ask Spread in the respective home market. For a description of the remaining variables see Table 2. The table
reports OLS coefficient estimates and (in parentheses) t-statistics. The t-statistics are based on standard errors that are clustered
by country. We use the natural logarithm of the raw values (plus a small constant when the raw value is zero) where indicated.
***, ** and * indicate statistical significance at the 1%, 5% and 10% levels (two-tailed) respectively.
TABLE 5
Alternative Identification of the IFRS Effect
Panel A: Shifting the Switch of Post-FY2005 Dummy
Dependent Variable:
Log(FSE Trading Volume)
Full Sample
Subsample
(max. 100 Firms per Country)
Re-estimation of Table 4, Model (2)
Re-estimation of Table 4, Model (5)
Post-FY2005 * Mandatory
Post-FY2005 * Mandatory
Coefficient
t-stat
Coefficient
t-stat
Post-FY2005 Dummy switches...
... 2 Periods earlier
0.667
(5.00)***
0.338
(1.65)
... 1 Period earlier
0.672
(4.80)***
0.408
(2.30)**
... in the same Period (= Table 4)
0.680
(5.22)***
0.454
(2.93)***
... 1 Period later
0.597
(5.52)***
0.463
(2.85)***
... 2 Periods later
0.569
(4.58)***
0.455
(2.67)**
Panel B: Within-Country Analysis
Independent Variables
Dependent Variable: Log(FSE Trading Volume)
United Kingdom: Main Market versus AIM
Test Variables
Post-FY2005
Post-FY2005 * Mandatory
-0.587
-0.482
(-1.38)
(-1.22)
1.386
0.990
(3.03)***
(2.34)**
Control Variables
Log(Market Value)
0.318
(1.34)
Log(Home Trading Volume)
0.885
(6.12)***
Fixed Effects
R-squared
Observations
Firm
Firm
0.78
0.80
2,541
2,541
This table shows alternative analyses to identify the IFRS effect on Open Market trading volume. The Open Market sample
comprises a total of 43,671 semiannual firm periods from 31 countries between 2002H1 and 2008H1. In Panel A, we re-estimate
regression model 2 and 5 (Table 4), respectively, by defining Post-FY2005 to switch up to two periods earlier or later. For
brevity, we only report coefficient estimates and t-statistics for the interaction term Post-FY2005 * Mandatory. In Panel B, we
perform within-country analysis focusing on the United Kingdom. We use mandatory IFRS adopters listed in the Main Market at
London Stock Exchange (LSE) as the treatment group and companies listed in the Alternative Investment Market (AIM) at LSE
as the control group. AIM companies were not required to adopt IFRS before fiscal year 2007 and therefore not included in the
Open Market sample presented in Table 1. All variables are defined as in Table 4. We delete firm periods of AIM companies
after these adopted IFRS. This procedure yields a sample of 2,104 (437) firm periods from 302 (93) mandatory IFRS adopters
(AIM companies). Panel B reports OLS coefficient estimates and (in parentheses) t-statistics. The t-statistics are based on
standard errors that are clustered by firm. We use the natural logarithm of the raw values (plus a small constant when the raw
value is zero) where indicated. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels (two-tailed)
respectively.
TABLE 6
Cross-Sectional Variation in the IFRS Effect
Panel A: IFRS Effect Conditional on the Institutional Environment
Dependent Variable: Log(FSE Trading Volume)
Independent Variables
Post-FY2005
Post-FY2005 * Mandatory
Post-FY2005 * Mandatory * Conditional
Post-FY2005 * Voluntary
Post-FY2005 * Voluntary * Conditional
Voluntary * IFRS
Control Variables
Fixed Effects
R-squared
Observations
Strong
Credibility
Large Δ
Uniformity
-0.204
-0.204
-0.194
-0.194
(-1.46)
(-1.46)
(-1.33)
(-1.33)
0.513
0.452
0.492
0.478
(2.53)**
(3.17)***
(2.95)***
(2.93)***
-0.099
0.006
-0.090
-0.032
(-0.61)
(0.04)
(-0.41)
(-0.14)
0.187
0.423
-0.072
0.393
(0.44)
(2.22)**
(-0.22)
(1.81)*
Long Accounting
Distance
Strong Credibility *
Large Δ Uniformity
0.022
-0.394
0.422
-0.666
(0.05)
(-1.37)
(1.07)
(-1.52)
0.759
0.766
0.805
0.850
(3.56)***
(3.46)***
(4.33)***
(4.08)***
Included
Included
Included
Included
Firm
Firm
Firm
Firm
0.78
0.78
0.78
0.78
12,063
11,889
9,589
9,589
Panel B: IFRS Effect Conditional on Attention Proxies
Dependent Variable: Log(FSE Trading Volume)
Independent Variables
Post-FY2005
Post-FY2005 * Mandatory
Post-FY2005 * Mandatory * Conditional
Post-FY2005 * Voluntary
Post-FY2005 * Voluntary * Conditional
Increase in Total
Media Coverage
Increase in German
Media Coverage
Large IFRS
Restatements
Strong EA
Reactions
-0.200
-0.200
-0.190
-0.202
(-1.44)
(-1.44)
(-1.31)
(-1.46)
0.254
0.166
0.356
0.427
(1.69)
(0.96)
(2.07)**
(2.79)***
0.258
0.455
0.206
0.272
(2.02)*
(2.78)**
(1.81)*
(2.88)***
0.262
0.094
0.220
0.195
(1.03)
(0.37)
(1.02)
(0.91)
-0.063
0.166
0.113
(-0.29)
(0.82)
(0.87)
0.762
0.751
0.778
0.759
(3.57)***
(3.48)***
(3.56)***
(3.56)***
Included
Included
Included
Included
Fixed Effects
Firm
Firm
Firm
Firm
R-squared
0.96
0.91
0.79
0.78
12,063
12,063
10,811
12,063
Voluntary * IFRS
Control Variables
Observations
TABLE 6 (continued)
This table presents results from regressions that relate Open Market trading volume at Frankfurt Stock Exchange (FSE) to IFRS
adoption conditional on variables related to the institutional environment (Panel A) and to individual investors’ attention (Panel
B). The Open Market sample comprises a total of 43,671 semiannual firm periods from 31 countries between 2002H1 and
2008H1. All regressions are based on model 5 in Table 4 using Conditional as an additional independent variable to partition the
treatment group. The conditional variables in Panel A are defined as follows: Long Accounting Distance is based on the Bae et al.
(2008) summary score of how a country’s local GAAP differs from IFRS on 21 key accounting dimensions and equals one (zero)
for countries with a score greater (equal to or less) than the sample median of 9. Strong Credibility is based on the earnings
management score from Leuz et al. (2003) and takes a value of one (zero) for countries with a score of less (equal to or greater)
than the sample median of 18.3. Large Δ Uniformity is based on the changes in uniformity measure from DeFond et al. (2011)
and equals one (zero) for industry-country clusters with changes in uniformity greater (equal to or less) than the sample median
of 39.67. The conditional variables in Panel B are defined as follows: Total Media Coverage (German Media Coverage) is based
on the number of search results in the Google News archive (http://news.google.com/archivesearch) in any language (German
only). This number reflects the number of articles that were published on Google News during the relevant period and that
contain either the company name (as provided by Worldscope item WC06001) or the firm-specific ISIN code. The related
conditional variables equal one (zero) if media coverage of treatment group firm is, on average, higher (lower or the same) after
fiscal year 2005 than before. Large IFRS Restatements equals one (zero) if the percentage difference between the restated net
income under IFRS and the originally reported net income under local GAAP for fiscal year 2004 is above (below or equal to)
the median percentage difference of 2,17%. Restatement information is from Worldscope (item WC01551R) and only available
for a subset of mandatory IFRS adopters. Strong EA Reactions equals one if the average Open Market trading volume during the
three-day window around the earnings announcement is higher than the average Open Market trading volume over the relevant
firm period, and zero otherwise. The table reports OLS coefficient estimates and (in parentheses) t-statistics. The t-statistics are
based on standard errors that are clustered by country. We use the natural logarithm of the raw values (plus a small constant when
the raw value is zero) where indicated. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels (two-tailed)
respectively.