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HHL Working Paper
No. 141 March 2015 (rev. version as of November 2015)
Determinants of Investor Reactions to
Error Announcements
Extended Evidence from Germany
Germar Ebnera, Matthias Höltkenb,
Henning Zülchc
a
Germar Ebner is a research associate at the Chair of Accounting and Auditing at HHL Leipzig
Graduate School of Management, Germany. Email: [email protected]
b
Matthias Höltken is a research associate at the Chair of Accounting and Auditing at HHL Leipzig
Graduate School of Management, Germany.
b
Prof. Dr. Henning Zülch is holder of the Chair of Accounting and Auditing at HHL Leipzig
Graduate School of Management, Germany.
Abstract:
This paper contributes to the understanding of the German two-tiered enforcement system and
the ‘name and shame’ mechanism as its deterrent. We investigate short-term reactions to error
announcements published between 2006 and 2013 and find significant evidence for differences
of investor reactions between the early and the current years of enforcement. Disentangling the
contributing factors of error severity, we provide evidence that investor reactions are primarily
driven by the impact of error announcements on profitability and financial leverage. Conversely,
we detect the amount of errors established to be negatively associated with investor reactions
indicating that extensive error announcements have an attenuating effect on investor reactions.
Further multivariate analyses provide additional insights referring to determinants of investor
reactions by examining effects of stated errors on core earnings, effects of errors triggered due to
second-guessing the use of professional judgment, and changes of investor perception over time.
Determinants of Investor Reactions to Error Announcements –
Extended Evidence from Germany
Abstract
This paper contributes to the understanding of the German two-tiered enforcement system and the ‘name and
shame’ mechanism as its deterrent. We investigate short-term reactions to error announcements published
between 2006 and 2013 and find significant evidence for differences of investor reactions between the early and
the current years of enforcement. Disentangling the contributing factors of error severity, we provide evidence
that investor reactions are primarily driven by the impact of error announcements on profitability and financial
leverage. Conversely, we detect the amount of errors established to be negatively associated with investor
reactions indicating that extensive error announcements have an attenuating effect on investor reactions. Further
multivariate analyses provide additional insights referring to determinants of investor reactions by examining
effects of stated errors on core earnings, effects of errors triggered due to second-guessing the use of professional
judgment, and changes of investor perception over time.
Keywords
Enforcement, IFRS, error announcement, regulation, Germany
JEL Classification
M 41, M 48
Acknowledgement
We gratefully acknowledge the valuable and constructive comments of Sebastian Hoffmann, Paul Pronobis, Teri
Yohn and participants of both the 6th HHL Leipzig Graduate School of Management doctoral seminar on
accounting as well as the EAA’s Annual Congress 2015 in Glasgow. In addition, we would like to thank the
anonymous reviewers of the EAA’s Annual Congress 2015 in Glasgow, the AAA’s Annual Congress 2015 in
Chicago, and the VHB’s Annual Congress 2015 in Vienna. We further thank Tobias Kretzschmar and Toni Thun
for their support with regard to data collection.
1
1
Introduction
This paper contributes to the understanding of investor reactions to errors 1 established in the German
enforcement system. Early evidence for this ‘name and shame’ mechanism, which is based on adverse disclosure
as a deterrent, has been provided by Hitz et al. (2012). Their results provide initial evidence that this capitalmarket-based compliance mechanism works in the German set-up, against the conventional wisdom that the
governance role of the capital market is less pronounced than in other countries (Hope, 2003; Leuz and
Wüstemann, 2004; La Porta et al., 2006). Our intention in conducting a differentiated replication (Salterio, 2014)
of the study by Hitz et al. (2012) is to review the existence of the ‘name and shame’ mechanism and its
determinants. We therefore extend the aforementioned study by investigating investor reactions for a
comprehensive sample of established errors between 2006 and 2013 in a short-window event study design.2
Determinants of investor reactions are provided by multivariate regression analyses. Besides expanding the
investigation period – which allows investigating potential differences in investor reactions over time – we
contribute to the understanding of factors determining the ‘name and shame’ mechanism by further assessing
error announcements from a both qualitative and quantitative perspective.
The positive impact of enforcement on accounting quality, according to prior research, is current
conventional wisdom.3 The quality of national enforcement mechanisms determines the economic consequences
of (mandatory) IFRS adoption, e.g., cost of capital, stock liquidity (Daske et al., 2008) or analyst forecast
accuracy (Preiato et al., 2013). A more recent study by Christensen et al. (2013) provides evidence that
economic consequences observed by cross-country studies are driven by countries that have made substantive
changes to their enforcement mechanisms around mandatory IFRS adoption, such as Germany. However, they
also find comparable results for countries solely making substantive changes to enforcement mechanisms,
concluding that it is the implementation of enforcement that really matters. Additional determinants of
accounting quality on a national level are the governance system, the legal system or the degree of investor
1
We simultaneously use the term “error” for financial misreporting or accounting malfeasance.
First error announcement to be found was published on February 3, 2006. However, FREP already started
investigating financial statements in the second half of 2005 after being jointly recognized by the Federal
Ministry of Finance and the Federal Ministry of Justice on March 30, 2005. For the remainder of this paper we
therefore refer to the period between 2006 and 2013.
3
It is noteworthy that enforcement quality cannot be easily measured directly (Hope, 2003). Thus, research
approximates enforcement quality e.g. via the level of investor protection or efficiency of the judicial system (La
Porta et al., 1998). More recent studies that focus on the enforcement of securities assess public enforcement (La
Porta et al., 2006; Jackson and Roe, 2009) or combine enforcement and audit measures (Hope, 2003; Preiato et
al., 2013; Brown et al., 2014).
2
2
protection (Dechow et al., 1996; Ball et al., 2000; Burgstahler et al., 2006; Bushman and Piotroski, 2006). Those
are complemented by firm characteristics such as accounting resources or the presence of opportunistic motives
(Dechow et al., 1996; Beneish, 1999; Ernstberger et al., 2012b).
Investigating a single-country setting is of particular interest as enforcement systems differ significantly
across the world with regard to institutional infrastructures and financial reporting regulation (Leuz, 2010). The
German enforcement system consists of a private review panel and a federal agency and is the role model for
two-tiered enforcement systems (Hoffmann and Höltken, 2014). The Financial Reporting Enforcement Panel
(FREP, Deutsche Prüfstelle für Rechnungslegung) as first tier was established in 2004 and has been conducting
investigations since 2005. FREP enforces accounting compliance within International Financial Reporting
Standards (IFRS) financial statements on a cooperative basis for firms that are listed on a regulated market in
Germany.4 The two-tiered enforcement system is complemented by the German securities regulator BaFin
(Bundesanstalt für Finanzdienstleistungsaufsicht) as second tier. BaFin has executive powers to enforce
cooperation and obliges firms to disclose errors that have been established within the investigation process.
Overall, the implementation of the two-tier enforcement system was but one reaction to the so-called ‘IAS
Regulation’ (EC No. 1606/2002), at first requiring listed firms to prepare financial statements in compliance
with IFRS. Furthermore, the regulation mandates all EU member states to implement effective enforcement
mechanisms that ensure the faithful and consistent application of IFRS.
In contrast to the predecessor study by Hitz et al. (2012), we do not find unambiguous evidence of
investor reactions to error announcements published in the federal gazette (Bundesanzeiger). While trading
volume and bid-ask spread metrics partially exhibit pronounced statistical significance in line with our
predictions, cumulative abnormal returns provide a mixed picture, with some evidence of information leakage
one day prior the error announcements. Partitioning the sample in two periods 2006-2009 and 2010-2013
emphasizes that weak investor reactions are mainly driven by the second period, which does not yield significant
impact on stock prices. However, we cannot attribute this phenomenon to a change of investor perception over
time, but rather subsample-inherent differences of possible drivers of investor reactions, as e.g. a decline in
profitability impact, legal-entity related accounting errors, and financial leverage of the respective companies.
Additionally supporting this line of reasoning, we find strong evidence of error induced changes to profitability
4
However, enforcement does not solely ensure IFRS compliance but also compliance with principles for orderly
accounting (Mattheus and Schwab, 2004). Therefore, legal entity accounts are subject to investigations by the
enforcement bodies, too.
3
and financial leverage being the main drivers of the magnitude of investor reactions, whereas error
announcements that affect companies’ core earnings yield significantly attenuated results. We suppose that the
prevailing difference of our study’s results compared to more pronounced empirical evidence for the US is
arguably driven by less severe error announcements in the German enforcement setting, either with regard to the
companies’ financials or expected penalty charges. However, ‘the US appears to be a major outlier when it
comes to enforcement’ (Leuz, 2010). We emphasize that inferences on the intended sanctioning function can
only be drawn from the investigated short-term market reactions, however leaving out an array of other potential
sanctioning channels as e.g. management or auditor turnover in the aftermath of error announcements.
The remainder of this paper is structured as follows: In Section 2, we briefly sketch the institutional
background of European and German enforcement activities. Thereafter we highlight relevant prior research and
derive our hypotheses. Section 4 outlines the methodological approach and provides the deduction of our
empirical expectations. Finally, we present and discuss our results in Section 5 and provide various sensitivity
analyses before concluding in Section 6.
2
2.1
The German Enforcement System
Characteristics, Institutions and Adverse Disclosure
Financial reporting in the EU is inter alia regulated by the so-called ‘IAS Regulation’ (EC No. 1606/2002) in
order to ensure a high level of comparability and transparency.This leads to two main consequences for listed
firms and EU member states since 2005: First, firms are required to prepare their financial statements under
IFRS (‘IAS Regulation’, recital 2). Second, European member states are required to install and maintain an
effective enforcement mechanism to ensure accounting compliance (‘IAS Regulation’, recital 16). Further
guidance on the implementation of national enforcement mechanisms is provided by CESR ‘Standard No. 1 on
Financial Information: Enforcement of Standards on Financial Information in Europe’, which provides minimum
requirements for effective enforcement mechanisms (CESR, 2003).5 Consequently, it lies within the sphere of
each member state to implement a national enforcement mechanism.
5
ESMA, based on the enforcement experience accumulated since 2005, performed a review of the principles
outlined in CESR Standards No. 1 and No. 2 (CESR, 2004). Against the background of this review, ESMA
published a consultation paper proposing an enhanced set of guidelines. In 2014, based on the received feedback,
ESMA published the final report with positive feedback on common enforcement activities and methodologies
(ESMA, 2014a).
4
Reviews of implementation practices have outlined that the common approach of enforcement is
characterized by heterogeneity (Berger, 2010; Leuz, 2010; Brown et al., 2014). This is not surprising, since
CESR’s requirements are not legally binding and therefore allow EU member states to implement enforcement
mechanisms in consideration of national circumstances. The annual report published by the ESMA on the
‘Activities of the IFRS Enforcers in Europe in 2013’ shows that all member states have implemented the
required enforcement mechanism (ESMA, 2014b). According to this report, European enforcers reviewed
approximately 1,900 financial statements and took some 500 enforcement actions in this year. Thus, the error
rate at European level for 2013 is about 26%.6 It is noteworthy that most of the material misstatements were
stated in the following fields: impairment of non-financial assets, recognition and measurement of deferred tax
assets, distinction between a change in an accounting policy and a change in accounting estimates, and
recognition of financial liabilities. All of these issues refer to IFRS which are conventionally labeled as complex
and/or require the use of professional judgment.
In light of the aforementioned European developments, the German enforcement system prior to 2004
had to be reformed. Preceding the hereinafter-mentioned reforms, the German financial statement oversight was
constituted by statutory audits and court decisions. The Bilanzkontrollgesetz (BilKoG – Accounting Enforcement
Act), implementing the requirements of the ‘IAS Regulation’ in consideration of the CESR Standard No. 1
guidance, was introduced in the course of a major three-stage financial reporting regulation reform.7 With regard
to the enforcement of financial information a two-tier external enforcement mechanism, consisting of a private
review panel and a federal agency, was established in 2005. As the German enforcement system has been subject
to several recent studies (cf. Ernstberger et al., 2012a and 2012b; Hitz et al., 2012; Strohmenger, 2014; Böcking
et al., 2015), we limit our remarks to the most essential information that is relevant for this paper and refer to the
aforementioned papers for more detailed depictions of the structure, the investigation procedures, and the
underlying sanctioning mechanism of the German enforcement system.
The Financial Reporting Enforcement Panel (FREP) as the private body and the Bundesanstalt für
Finanzdienstleistungsaufsicht (BaFin) as the federal agency constitute the two-tier structure of the German
6
According to ESMA’s annual enforcement reports (2009-2013), the error rate at European level varies around
30% (2013: 26%; 2012: 25%; 2011: 30%; 2010: 37%; 2009: 35%) and has decreased significantly within the last
two years.
7
The Bilanzkontrollgesetz complements the Abschlussprüferaufsichtsgesetz (APAG – Auditor Oversight Law)
and the Bilanzrechtsreformgesetz (BilReG – Accounting Law Reform Act). The APAG introduced a private
oversight body with regard to statutory audits whereas the BilReG modified previous regulations on auditors’
independence (Ernstberger et al., 2012a).
5
enforcement system. This characterizing and unique feature combines the flexibility of the cooperative
assessment in private enforcement with the regulatory authority of a federal agency. 8 FREP conducts
investigations on most recent financial reports (single entity and consolidated financial statements) and
management reports either following referrals (reactive; investigation of a single accounting issue suspected to
be erroneous) or through systematic sampling (proactive; broader approach investigating several accounting
issues). In case of non-cooperation by the firm under investigation or disagreement on FREP’s findings the
investigation is handed over to BaFin. BaFin has the necessary executive powers to enforce cooperation and,
moreover, is capable to enforce the disclosure of error findings.
Whenever erroneous financial accounting is established by one of the two enforcement bodies, BaFin
requires the respective firm to disclose established errors. In case the firm agrees with the enforcement bodies,
errors have to be disclosed through a press release in the electronic platform of the federal gazette
(Bundesanzeiger) and, additionally, have to be made public in at least two daily financial newspapers9 or an
electronic information provider. Firms are required to give insights into the nature and magnitude of the errors
established, cite the relevant accounting norm and the responsible enforcement body. Given the fact that firms
independently phrase their error announcements, the information provided varies widely. However, firms are
required to not trivialize or disguise the nature or impact of the established error.
On an overall basis, the German enforcement mechanism ‘is based on the notion of adverse disclosure
effects’ (Hitz et al., 2012) and is preventive in nature (Berger, 2010). Established errors can arise in respect of
recognizing, measuring, presenting or disclosing facts in financial statements. Nevertheless, the enforcement
bodies solely establish and enforce publication of errors, but cannot enforce restatements of financial statements.
Thus, German legal concept intends management to be responsible for corrections in prior financial statements
(Mattheus and Schwab, 2004). Accordingly, errors have to be retrospectively restated in accordance with IAS 8
to the extent that it is impracticable to determine either the period-specific effects or the cumulative effect of the
error.
8
In 2013, Austria also implemented a two-tier enforcement system. The Austrian Financial Reporting
Enforcement Panel (AFREP) started investigations in the second half of 2014. By now, there are first indications
that both enforcement processes are comparable in the broadest sense, although analysis of the legal foundations
provides evidence that there might be substantial differences with regard to investigational responsibilities
(Hoffmann and Höltken, 2014). This has been supported by outcomes from the first investigations by AFREP
and FMA (Austrian Financial Market Authority).
9
As the Financial Times Deutschland was issued for the last time in December 2012, the number has been
reduced from three (Hitz et al., 2012, p. 258) to two ever since. To our knowledge, Handelsblatt and
Börsenzeitung are currently the only relevant daily financial newspapers.
6
2.2
The Role of Professional Judgment
Since 2005, FREP has been conducting more than a hundred investigations each year on average. Proactive
assessments (random- and risk-based sampling) account for 88% of all investigations performed (see Table 1 for
a comprehensive overview). In recent years, we observe a substantial decline in the error rate from over 20% to
some 15%. In light of the aforementioned European error rate, varying around 30%, this seems to be a welcome
development. Still, by consideration of the multi-year period, more than one out of five financial statements is
classified as erroneous. A possible explanation for the observed decrease could be seen in learning effects of
companies facing a second or third investigation with regard to IFRS application or the examination approach by
FREP. Also, increased topical attention on executive management or supervisory (audit committee) level, and
related discussions between preparers and audit firms might be further drivers of this trend. Moreover, changes
in the population subject to enforcement due to regular delisting, withdrawal from the regulated market or M&A
activities could be valid explanations (FREP, 2014).
== Insert Table 1 here ==
On a more general level, the observed magnitude of error findings is regularly attributed by FREP to the
‘comprehensiveness and application challenges of certain IFRS’ and the ‘insufficient reporting in the notes and
the management report’ (FREP, 2014). Application challenges are often linked to the fact that IFRS are
principles-based, which means that issuers and auditors cannot follow detailed application guidance but have to
adapt general principles to specific situations (Ball, 2006; Plumlee and Yohn, 2010). This often also involves the
application of professional judgment with regard to determining key assumptions or assessing future
developments (Schipper, 2003; Carmona and Trombetta, 2008). Representative examples of errors related to
professional judgment include inter alia: determining future cash flows or growth rates for the purpose of
impairment of non-financial assets, recognizing and measuring subsequently deferred tax assets or determining
the fair value of financial instruments. What is common to all of these is that they provide relevant information,
but ‘some of such estimates have low reliability’ (Nobes, 2005). The lack of reliability might be mitigated
through effective enforcement. Hence, it does not surprise that ‘business combinations and impairment of nonfinancial assets’ (73 error findings), ‘income taxes’ (30 error findings) and ‘financial instruments’ (59 error
findings) are not only regular enforcement priorities defined by FREP but also top-5-error types of all errors
7
established during 2006-2013. Top-5-errors are complemented by issues related to the ‘presentation of the
financial statement’ (40 error findings) and the ‘statement of cash flows’ (26 error findings). 10
3
3.1
Previous Research and Hypotheses Development
Previous Research
There is a multiplicity of empirical studies investigating different enforcement mechanisms all over the world.
However, most research has been conducted on SEC’s enforcement actions in the US whereas research on
European enforcement is scarcely growing (cf. Höltken and Ebner, 2015, for a systematic overview).
Enforcement of accounting standards is considered as but one of various factors that determine accounting
quality and accounting standards compliance. Various cross-country studies (La Porta, 1998; Hope, 2003; Leuz
et al., 2003) classify Germany as relatively weak in terms of broad enforcement quality. Nevertheless, those
studies have in common that they refer to the prevailing enforcement system in Germany before 2005, which is
mainly characterized by low investor protection. Thus, these studies do not take into account the current
accounting enforcement system. In contrast, the enforcement indicator constructed by Preiato et al. (2013),
measuring enforcement in the light of the implementation of CESR Standard No. 1’s criteria by each EU
member state, considers the changes made to the German enforcement system and ranks Germany second in
terms of compliance. In the light of this finding one must acknowledge that the German financial reporting
oversight has changed significantly due to the implementation of the two-tier enforcement mechanism (also
Brown et al., 2014).
Looking at the first years of German enforcement, Hitz et al. (2012) provide initial evidence for the
existence of the ‘name and shame’ mechanism by showing that investors seem to react negatively to error
announcements. Hence, their results are in line with the literature examining adverse investor reactions following
disclosure of SEC-induced enforcement releases (Feroz et al., 1991; Nourayi, 1994; Dechow et al., 1996;
Beneish, 1999; Anderson and Yohn, 2002; GAO, 2002; Richardson et al., 2002; Hribar and Jenkins, 2004;
Palmrose et al., 2004; Callen et al., 2006; Karpoff et al., 2009; Plumlee and Yohn, 2010). In addition, the
likelihood of material errors in German IFRS financial statements is positively associated with the presence of
10
We note that several other studies have descriptively analyzed the number and proportion of error
announcements by FREP over different periods (inter alia Ernstberger et al., 2012b). Differences in numbers or
proportions might result from different classifications.
8
opportunistic motives and low-quality governance mechanisms (Ernstberger et al., 2012b; Böcking et al., 2015).
Those findings are in line with prior US-based research, too, finding determinant factors for accounting noncompliance including low governance quality (Dechow et al., 1996), opportunistic (Dechow et al., 1996;
Beneish, 1999) or fraudulent motives (Palmrose et al., 2004; Swanson et al., 2007).
3.2
Hypotheses Development
Investigating the first years of German enforcement, prior research has shown that adverse disclosure yields
marginally negative market reactions for the German enforcement system (Hitz et al., 2012). By extending the
investigation period and investigating additional determinants, our study contributes to the growing capitalmarket-based evidence for the German enforcement system. We therefore analyze the impact of both
quantitative and qualitative criteria of the respective error announcements on investor reactions. As a follow-up
to this, we also investigate potential changes of investor reactions over time.
Investor reactions to error announcements
Enforcement of financial reporting aims to ensure consistent and faithful application of accounting standards.
Established errors are therefore unambiguous indicators of corporate governance failure as both preparer and
auditor infringed their duty of correct financial reporting (Desai et al., 2006). This is ominous since corporate
governance mechanisms are established to guarantee the conformity of managerial actions and shareholders’
interests (Shleifer and Vishny, 1997). Under the notion of adverse disclosure, managers are penalized via
disclosure of material accounting errors. In this context, managers are supposed to be deterred a priori to violate
accounting standards. In addition, managers are penalized a posteriori via negative investor reactions.
Error announcements are new information to capital market participants giving reason to adverse
investor reactions. Hence, the publication of an error announcement has a negative effect on market value if
investors revise their assumptions on future cash flows or (residual) earnings taking into account the firm’s level
of uncertainty. For example, a restatement of income that results in a downward revision of a firm’s future cash
flows can have a negative market value effect (Feroz et al., 1991, p. 122; Palmrose et al., 2004, p. 63; Callen et
al., 2006a, p. 57). Error announcements can also increase information risk measured by an increase in cost of
capital (Hribar and Jenkins, 2004, p. 338). We therefore reassess the first hypothesis by Hitz et al. (2012) to
9
confirm the general interdependence between error announcements and investor reactions for the extended time
period:
H1: Investors react negatively to error announcements.
Quantitative impact of error announcements
Furthermore, we investigate the quantitative and qualitative determinants of investor reactions to error
announcements. Prior research finds that the magnitude of investor reactions is determined by various factors,
e.g. the persistence of restatements (Palmrose et al., 2004), the materiality of restatements (Feroz et al., 1991;
Palmrose et al., 2004; Plumlee and Yohn, 2010) or restatements’ effect on cash or accruals (Callen et al., 2006).
With respect to the materiality of restatements, studies regularly refer to restatements’ impact on net income or
core earnings (Palmrose et al., 2004; Plumlee and Yohn, 2010).
In order to approximate the association between error characteristics and investor reactions, we use
different aspects of error announcements including the number of errors established and the quantitative impact
on both profitability and financial leverage. Beyond those, we identify the risk of legal litigation and the firms’
general attitude to cooperation within the enforcement process, indicating the level of accounting compliance, as
further determinants of the magnitude of investor reactions within the German enforcement system. All of these
characteristics are used by Hitz et al. (2012), too. As second hypothesis, we therefore propose a positive
association between impact on profitability and financial leverage, number of errors, risk of legal litigation, lack
of compliance attitude, and the magnitude of investor reactions:
H2: The magnitude of investor reactions to an error announcement is determined by error severity.
Qualitative impact of error announcements
Prior research provides evidence that distinguishing the content of restatements in accordance with qualitative
criteria strengthens the explanatory power of empirical studies. The study by Palmrose et al. (2004) was the first
to highlight the importance of distinguishing between intentional and unintentional restatements when examining
related investor reactions. On this basis, Hennes et al. (2008) provide evidence that this distinction aids the
understanding of deliberate misreporting. Both studies focus on publicly announced restatements. Conversely,
Plumlee and Yohn (2010) use a larger sample by including restatements that are not only announced via 8-K
10
reports but can be derived from other publicly available information. Additionally, the authors do not simply
distinguish between intentional and unintentional errors but between internal company errors, intentional
manipulation, transaction complexity, and some characteristics of the respective accounting standard that has
been violated. Concerning the latter, they identify three types of contributing factors: (1) the lack of clarity in the
standard and/or the proliferation of literature because the original standard lacks clarity; (2) the use of
professional judgment in applying the standard; and (3) complications in applying detailed rules. 11 Hence, these
approaches encourage us to examine the qualitative nature of error announcements, too.
For our third hypothesis, we thus consider information provided by error announcements with respect to
the income statement. Prior research has shown that restatements yield higher investor reactions if they affect
core earnings (Palmrose et al., 2004). This is based on the assumption that core earnings indicate the companies’
long-run earnings ability (Penman, 2007). Research based upon earnings-response coefficients indicates that the
market is more surprised by information related to ongoing operating income than to one-time (special) income
(Strong and Meyer, 1987; Elliot et al., 1988; Elliot and Hanna, 1996). More recent research provides evidence
that ‘investors regard restatements of core accounts as more serious’ (Palmrose and Scholz, 2004). We thus
expect investor reactions to be positively associated with errors affecting core income:
H3a: Error announcements affecting core income yield more severe investor reactions.
As outlined in section two, applying principles-based accounting standards regularly involves the use of
professional judgment by preparers, auditors, and regulators. Prior research has shown that investors are more
concerned about management integrity than technical accounting issues (Palmrose et al., 2004). However,
Plumlee and Yohn (2010) provide evidence that accounting standard complexity is but one major driver of
erroneous financial reporting. The restatements of the Plumlee and Yohn study are attributed to the lack of
clarity in the respective accounting standard, but almost 40% are related to the use of professional judgment in
applying the standard. Nevertheless, they cannot find evidence for more negative impact on reported income.
We argue that the establishment of an error related to professional judgment also indicates a lack of
management integrity. Conversely, following the distinction made by Plumlee and Yohn (2010), errors related to
non-professional judgment rules are attributed to the lack of clarity or complications in applying the respective
11
For detailed explanations regarding the distinction between the four main categories and the contributing
factors to the characteristics of accounting standards see Plumlee and Yohn (2010), pp. 46-48. Explanatory
examples are given in the same article on pages 58-63.
11
accounting standard. Principles-based accounting standards rely on the sufficient experience and expertise of
management to exercise professional judgment. Therefore, it is necessary to enforce those (parts of) standards
that require the use of professional judgment to secure comparable and consistent application of IFRS. If
regulators decide to establish an error after second-guessing management’s judgment, we argue that this is
related to a lack of reasonableness of the respective judgments made based on the facts and knowledge available
at the time of judgment. Thus, we expect such an error to be associated with more negative investor reactions:
H3b: Error announcements related to the misuse of professional judgment yield more severe investor
reactions.
Differences over time in the impact of error announcements
The German enforcement system was simultaneously implemented with the mandatory adoption of IFRS as the
single set of accounting standards for financial statements of listed firms. Thus, firms listed on a German stock
exchange faced a change from the German Commercial Code (Handelsgesetzbuch), which is mainly influenced
by tax regulations to IFRS, aiming to provide information that is useful for investors’ decisions. From the AngloSaxon investor perspective German GAAP, permitting firms to build up hidden reserves, allows too much
discretion. In addition, German GAAP are considered not requiring firms to disclose sufficient investor-relevant
information (for a detailed outline of these arguments see Leuz and Verrecchia, 2000).
Prior research shows that the adoption of IFRS on the quality of financial reporting bears some sideeffects that can be mitigated by enforcement mechanisms. First, previous literature provides evidence for noncompliance with IFRS, especially with regard to disclosure requirements, during the first years of adoption
(Verriest et al., 2012; Glaum et al., 2013). Reviewing financial statements for faithful and consistent application
of IFRS, FREP does investigate recognition, measurement and disclosure issues. Hence, investigation-related
discussions between preparers, auditors, and regulators as well as announced errors should increase the overall
disclosure level. We therefore argue that investors attribute disclosure errors during the first years after IFRS
adoption (2006-2009), which correspond to the first years of enforcement, to the firms’ adoption process to IFRS
whereas disclosure errors after 2009 can be attributed to a lack of integrity.
Second, prior literature provides consistent results with regard to an increase in earnings management
under IFRS in Germany (van Tendeloo and Vanstraelen, 2005; Callao and Jarne, 2010). Both studies observe an
increase in discretionary accruals. Ensuring faithful application of IFRS, an effective enforcement mechanism
12
would identify firms that use earnings management, restrain the respective companies from using earnings
management, and thus decrease the overall level of earnings management. With regard to the German
enforcement system the identification process seems to be efficient but cannot restrain firms from using earnings
management (Böcking et al., 2015). Further evidence indicates that managers do not solely use professional
judgment to manage earnings but use the lack of clarity in the standard to do so (Plumlee and Yohn, 2010).
We argue that lack of clarity in the standards especially suffers within the first years of IFRS
application due to proliferation of different views in literature; the proliferation decreases over time due to the
consolidation of a prevailing opinion, thereby reducing lack of clarity. All in all, we argue that investor reactions
increase over time:
H4: Investor reactions to error announcements become more severe over time.
4
4.1
Methodology
Market Reaction Tests
We conduct an event study in order to investigate the change in selected capital-market properties due to error
announcements in the German enforcement system. We focus on the effect on stock prices, trading volumes, and
bid-ask spreads within event windows of one, three and five trading days, with the center of the event window
being the date of the error announcement published in the federal gazette (see Hitz et al., 2012, p. 263, for
further fundamentals on measuring investor reactions).
Cumulative abnormal returns are calculated as the sum of daily abnormal returns in the respective event
window; abnormal returns in turn are defined as the difference between actual stock returns and expected stock
returns. We compute the latter with a stock-specific market model (MacKinlay, 1997) and regress daily stock
returns on daily returns of a weighted index of all German companies with equity listing on the regulated market
(CDAX).12 Since the CDAX comprises exactly the population from which our sample companies are drawn, we
regard it as the appropriate market proxy. The estimation period is 150 trading days prior to the event window.
12
In order to incorporate valuation effects due to dividend payments, we use continuously compounded prices of
company stocks and CDAX for estimating and calculating abnormal returns. While Hitz et al. (2012) employ an
equally-weighted market portfolio due to a more pronounced correlation of the single stock and market returns
(see Hitz et al., 2012, Fn. 4), we follow the conventional approach by using a value-weighted index (see e.g.
Dechow et al., 1996, and Karpoff et al., 2008).
13
Cumulative abnormal trading volumes are similarly computed as cumulative abnormal returns, with
expected trading volume being the mean daily trading volume of the 150 days that precede the event window
(Bamber, 1987). We pay attention to institutional and inside ownership by using the concept of relative trading
volume throughout our analyses. We therefore scale the number of daily traded shares with the number of daily
outstanding shares that are not closely held.
The average abnormal bid-ask spread is the mean of daily abnormal bid-ask spreads. We define
abnormal bid-ask spread as the difference between actual bid-ask spread and expected bid-ask spread, whereas
the latter is a linear function of stock price and trading volume at the same day. The abnormal bid-ask spread can
therefore be interpreted as the residual which cannot be explained by the independent variables stock price and
trading volume (Dechow et al., 1996). We do not use absolute values, but the natural logarithm of each capital
market property in our regressions. In line with the calculations of return and trading volume measures, we
employ a 150-day estimation window to compute the regression coefficients.
4.2
Multivariate Regression Model
Since all of our hypotheses, with the exception of the first, deal with the impact of selected factors on capital
market reactions, we employ multivariate regression analyses to test them. We use the three-day cumulative
abnormal return13 as dependent variable and various sets of regressors as explanatory variables. While we also
calculate reduced specifications similar to those of Hitz et al. (2012) for the sake of comparability, we use the
following extended model to investigate the hypotheses named above:
𝐶𝐴𝑅 = 𝛽0 + 𝛽1 𝐷𝐸𝐿𝑇𝐴𝑅𝑂𝐸 + 𝛽2 𝐷𝐸𝐿𝑇𝐴𝐹𝐼𝑁𝐿𝐸𝑉 + 𝛽3 𝑁𝑈𝑀𝐵𝐸𝑅𝑅𝑂𝑅𝑆 + 𝛽4 𝐿𝐸𝐺𝐴𝐿 + 𝛽5 𝐵𝐴𝐹𝐼𝑁 + 𝛽6 𝐶𝑂𝑅𝐸
+ 𝛽7 𝑃𝑅𝑂𝐹𝐽𝑈𝐷𝐺 + 𝛽8 𝐸𝐴𝑅𝐿𝑌 + 𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑠 + 𝜀
(1)
Table 2 provides a comprehensive overview on variables employed, definitions, and data sources. The
first five regressors DELTAROE, DELTAFINLEV, NUMBERRORS, LEGAL and BAFIN have already been
applied by Hitz et al. (2012) as variables of interest. In contrast to their study, we do not aggregate the first three
13
We choose the non-winsorized three-day cumulative abnormal returns as dependent variable for the following
reasons: Since it is also used by Hitz et al. (2012), our study design and in turn our findings are at least to some
extent comparable to their study. Additionally, it appears to be a good compromise between catching potential
investor reactions prior to and in the aftermath of error announcements and concentrating the event window to
reduce potential noise of simultaneous confounding events that have not been eliminated despite our thorough
sample adjustment process. Moreover, the three-day event window exhibits a pronounced stock price decline and
level of statistical significance.
14
variables to the compound measure ERROR SEVERITY, since we notice that the impact on profitability and
financial leverage is negatively correlated with the number of errors (see table 4), consequently a principal
component analysis does not seem appropriate for our dataset. Furthermore, our approach displays the
immediate association between the financial impact of accounting errors and the respective capital market
reactions, which provides the reader with to date unpublished information. The three variables are defined as
follows: DELTAROE assesses the errors’ impact on return on equity, whereas DELTAFINLEV assesses the
errors’ impact on financial leverage; both variables are denoted in percentage points. NUMBERRORS is the
number of errors which are contained in the respective error announcement. Following Hitz et al (2012), we
regard the three variables as indicators of error severity and therefore propose a negative association with
investor reactions, i.e. an enhancing effect on stock price declines.
The binary variable LEGAL assesses whether an error announcement relates to legal entity accounts,
which are the basis of tax and dividend payments in Germany. As a consequence, accounting errors in legal
entity accounts can yield lawsuits which might result in corresponding outflows of resources (Leuz, 2010; Hitz
et al., 2012). Therefore, we expect those error announcements to yield a more pronounced stock price decline.
The same holds for error announcements that have been published on behalf of the securities regulator BaFin
(BAFIN), which we also regard as a proxy of negative compliance within the enforcement process.
To investigate hypothesis H3a, we introduce the binary variable CORE which we code as one if the
error announcement affects the company’s core earnings. Following the definition of core earnings of Palmrose
et al. (2004, p. 65) and Penman (2007, p. 415 f.), CORE takes the value of one if the error announcement
includes corrections of revenue, cost of sales, or ongoing operating expenses. Conversely, this means that CORE
is set zero in case of erroneous accounting of special items, non-operating expenses, and merger-related items. In
line with the aforementioned literature, we expect core earnings accounting errors to yield more severe investor
reactions.
The impact of professional judgment (H3b) is assessed via the binary variable PROFJUDG, which is set
one if the error announcement contains information about management’s use of professional judgment that
FREP or BaFin do not consider as justifiable interpretation within the legal boundaries. Since this might reflect
discretionary accounting choices made by the management, we propose PRODJUDG to have an enhancing
effect on stock price declines. In order to investigate a potential effect of time on the severity of capital market
reactions (H4), we employ the binary variable EARLY that partitions our sample in the periods 2006-2009 and
15
2010-2013. It is noteworthy that the first interval is the time frame which has also been employed by Hitz et al.
(2012). In contrast to all other variables of interest, we expect EARLY to have an attenuating effect on investor
reactions.
In order to avoid omitted variable bias and to enhance the explanatory power of our regression models,
we furthermore include the following control variables in our multivariate regression model. Apart from minor
differences, they are the same as those of Hitz et al. (2012) who provide additional theoretical support of their
choice of control variables. OPPORTUNISM is the first factor of a principal component analysis which is based
on the following three measures: GCGCNumb that measure deviations from the German Corporate Governance
Code, a set of best-practices in Corporate Governance; VarComp, which is the ratio of variable to total
management compensation; and EM, which we measure as dummy variable according to Jansen et al. (2012). 14
The second compound metric RESOURCES is also derived via principal component analysis, consisting of the
following factors: Growth, which we calculate as the 5-year geometric mean in sales growth prior to the
erroneous financial statement; IFRSyears, which we consider being more meaningful than the pure stock market
listing in the context of enforcement of financial reporting;15 and Complexity, measured as the residual of an
industry-specific regression of business segments on firm size. The dummy variable CHANGE takes the value
one if there have been fluctuations of either the auditor or top managers between the issuance of the erroneous
financial statement and the error announcement, and TIME LAG denotes the number of days between these two
dates. SIZE is the natural logarithm of market capitalization at the beginning of the year of the error
announcement (in Mio. Euros), LEVERAGE is the ratio of book value of debt to book value of assets,
LIQUIDITY denotes the number of days with non-zero returns in the 150-day window prior to the error
announcement, and OWNERSHIP measures the proportion of non-closely held shares.
== Insert Table 2 here ==
14
Contrary to the industry- and firm-specific discretionary accruals model which is employed by Hitz et al.
(2012), we opt for this alternative approach since it does not depend on cross-sectional or time-series regressions
which in turn demand a certain sample size threshold, but simply relies on firm-specific developments of asset
turnover and profit margin for two periods. Keeping in mind that our multivariate regression sample comprises
79 firms from 9 different industries according to SIC codes and extends over 8 years, we consider the model of
Jansen et al. (2012) being an interesting alternative of assessing earnings management in this context. The fact
that Jansen et al. (2012) find even superior performance in detecting earnings management for this measure
compared to performance-adjusted discretionary accruals models additionally encourages us to follow this
approach.
15
We argue that a company’s capabilities in consistent and faithful IFRS application are rather determined by its
experience in preparing financial statements in accordance with IFRS than the duration of its stock market
listing. Recent literature hinting at such learning effects in the course of IFRS application provides additional
support for our approach (see Salewski et al., 2014).
16
4.3
Sample Selection
Our study is based on a sample of German firms which have been subject to error announcements by FREP or
BaFin, starting from 2005 to the end of 2013. Error announcements are obtained from the federal gazette
(Bundesanzeiger) and comprise 200 findings for the aforementioned time period. In order to avoid doublecounting, we merge 6 observations of companies that have published single error announcements for both
individual and consolidated accounts at the same day. Additional search in Germany’s highest-circulation
business newspaper and stock exchange gazette Handelsblatt confirms these results, as well as research in the
Börsenzeitung, a prestigious German newspaper that reports exclusively about financial markets.
Based on this starting point, we conduct the following adjustments in order to avoid potential biases in
our results. We exclude 20 companies that are not headquartered in Germany and 7 companies that have only
issued bonds. Since we aim to assess investor reactions for the very first publication of the error announcement
we exclude 15 observations that are redundant information of prior announcements. 53 observations must be
deleted due to pre-publication of the error announcements in the Handelsblatt, Börsenzeitung or online news
agencies, and 11 observations as a result of missing return data in Datastream. We additionally exclude 3 (12,
18) observations from further analyses since the respective companies are subject to company-specific
confounding events for the [0] ([-1;1],[-2;2]) event window, which leaves us with 88 (79, 73) companies in the
final sample.
5
5.1
Results
Data, Descriptive Statistics and Correlations
We collect the data for the variables in the multivariate regression model from the corresponding error
announcements and financial reports. Due to occasional constraints of data availability for financial reports prior
to 2006 we supplement the missing values with data from Datastream. We also use Datastream to obtain the
data for our capital market metrics.
Table 3 displays the descriptive statistics of the explanatory variables for all observations of the
multivariate regression analysis. The average profitability impact of error announcements is -24.5%, whereas the
average impact on financial leverage is 3.7%; furthermore, error announcements consist of 4.1 single errors on
average. 12.7% of the error announcements address accounting malfeasance in the companies’ legal entity
17
accounts, 20.3% are released by the BaFin, and 38.0% comprise erroneous figures of the companies’ core
earnings. In 48.1% of the error announcement we find evidence of professional judgment, and 59.5% have been
published prior to 12/31/2009. The average sample company violates against 8.7 numbers of the GCGC, offers
its managers variable compensation of some 30% and applies IFRS for 3.6 years. The average time lag and firm
size are a bit smaller than those of Hitz et al. (2012)’s sample; the financial leverage (67.1%) is more than twice
their number.By partitioning the sample in the years 2006-2009 and 2010-2013, as shown in Table 3, Panels B
gives an indication of how the characteristics of companies with erroneous financial statements have evolved
over time. While most of the variables do not exhibit material changes, the following exemptions are statistically
significant: The median of DELTAROE decreased (z-statistic of -1.90), while the mean of NUMBERRORS
declined from 4.75 to 3.06 (t-statistic of -1.95). The same holds for the mean of LEGAL, which decreased from
0.191 to 0.031 (t-statistic of -2.14). The increase of BAFIN and the decrease of PROFJUDG are only close to
conventional levels of significance (t-statistics of 1.44 and -1.56, respectively). Turning to the control variables,
IFRSyears significantly increased over time, as is logical, as well as the mean of LEVERAGE.
== Insert Table 3 here ==
Table 4 shows the matrix for both Pearson’s and Spearman’s bivariate measures of correlation. TIME
LAG and BAFIN are positively correlated, which makes sense given the review procedure in the German
enforcement system. The correlation of SIZE and LIQUIDITY is also in line with expectations; furthermore,
there is a strong association between CORE and PROFJUDG. Finally, OPPORTUNISM exhibits a strong
negative correlation with SIZE and LIQUIDITY, as well as the pair of PROFJUDG and DELTAFINLEV. Apart
from the named correlations, all associations are below the threshold of 0.4, thus we do not regard collinearity as
a matter for our dataset.
== Insert Table 4 here ==
5.2
Market Reaction Findings
Table 5 (Panel A) reports the results of market reactions to error announcements of FREP/BaFin. It is
noteworthy that the mean cumulative abnormal return at the event date is positive (0.58%), while the median is
slightly negative (-0.04%); both numbers lack any statistical significance. While the same holds for the five-day
event window (mean: 0.60%; median: -0.30%), the stock price decline of the three-day event window (mean: -
18
0.69%; median: -0.57%) is quite close to conventional levels of statistical significance or even significant (pvalue of 0.17 and 0.08, respectively).
== Insert Table 5 here ==
In line with Hitz et al. (2012), our study confirms that the magnitude of stock price decline in the event
windows is much less pronounced than in previous US studies which measure cumulative abnormal returns of up
to -20.2% (Beneish, 1999), -10.0% (Feroz et al., 1991) and -9.2% (Hribar and Jenkins, 2004), to name but a few.
However, this comparison might be misleading according to the institutional differences between the US and
Germany as shown by Leuz (2010). In compliance with his analysis, Germany is labeled as a relationship-based
financial system that relies on close ‘insider relationships’ with privileged access to information. In this context,
Hitz et al. (2012) argue that possible drivers of the weak stock returns in the German setting are information
leakage (a similar phenomenon can be observed in the US, cf. Hribar and Jenkins, 2004), a weaker litigation
environment and an attenuated negative signaling character of error announcements, presumably due to a higher
frequency compared to the number of examinations and unintentional errors. The latter are regarded as a result
of the recent IFRS adoption in Germany in 2005 and the lack of pre clearance until 2009. We tackle this
argumentation in our multivariate regression analysis.
Apart from the prevailing difference to US study results, our findings are quite striking in comparison to
the predecessor study by Hitz et al. (2012) who find statistically significant stock price declines for the one- and
three-day event window (mean: -1.15%; median: -1.67%). By partitioning our sample in two periods (2006-2009
and 2010-2013, see Table 5, Panels B and C), we investigate whether the results of the first period are
comparable to the study of Hitz et al. (2012). Indeed our findings exhibit comparably strong capital market
reactions in the period 2006-2009 for the [-1;1] event window (mean: -1.05%; median: -0.83%), which are very
close to significance (mean) or significant at the 5% level (median). In contrast, the observations of the period
2010-2013 yield consistently insignificant results and even a positive median of cumulative abnormal returns in
[-1;1]. Notwithstanding this, we obtain deviating findings even in the period 2006-2009 in comparison to Hitz et
al. (2012), which might be the result of a slightly different methodology of calculating abnormal returns.16 In
summary, our results highlight the sensitivity of the findings to a slightly different methodology and dataset.
16
While Hitz et al. (2012) use an equally-weighted market portfolio, we employ the CDAX total return index
which weights the stocks according to the market capitalization of the included listed companies. Furthermore it
19
While we do not find any changes in the mean cumulative abnormal trading volume, the median
abnormal trading volume at the day of the error announcement is significantly negative (p-value: 0.01). Partially
congruent with these findings, the mean (median) average bid-ask spread is significantly positive for the threeand five-day event windows (all three event windows). Contrary to the trading volume metrics, which become
more pronounced over time, while still lacking any significant mean in both periods, the bid-ask spread metrics
indicate highly significant results in the period 2006-2009, in contrast to the subsequent four years. These results
are mainly in line with the findings of Hitz et al. (2012), who also provide evidence for a decrease in trading
activities, arguably as a result of increased investor uncertainty.
In the bottom line we find weak support for our first hypothesis of negative investor reactions to error
announcements. However we emphasize that these results are mainly driven by the observations of the sample
years 2006-2009 which are also the basis of Hitz et al. (2012)’s study. While the latter infer empirical evidence
for the postulated sanctioning function of the German enforcement system from their findings, we can only
partially support this statement.
5.3
Multivariate Regression Analyses
Table 6 shows the results of the multivariate regression analyses. We estimate the following six different
models: Model 1.1 is similar to the basic model of Hitz et al. (2012) and explains cumulative abnormal returns
by the factors DELTAROE, DELTAFINLEV, NUMBERRORS, LEGAL and BAFIN, extended by control
variables in model 1.2. In line with our predictions from section 4.2 we find a highly significant negative
coefficient of DELTAROE and DELTAFINLEV, thereby clearly showing that error announcements with more
severe economic impact lead to enhanced stock price declines. Contrary to this finding, NUMBERRORS is
significantly positive. While this runs counter to our prediction, Peterson (2012) offers a potential explanation by
stating that accounting complexity attenuates investor reactions. In this context, accounting complexity increases
with number of errors established. As proposed in the hypotheses development, the coefficient of LEGAL is
significantly negative, while BAFIN lacks statistical significance. Going in line with these findings, the
disentangling of the compound control variables OPPORTUNISM and RESOURCES in model 1.3 does not lead
to materially different results. The explanatory power of the three models is highly significant, nevertheless
model 1.3 exhibits a comparably low adjusted R2 of 10.7%.
is uncertain whether the sample of Hitz et al. (2012) comprises exactly the same companies as ours, keeping in
mind that the adjustment process can also be subject to some degree of discretion and judgment.
20
== Insert Table 6 here ==
Models 2.1-2.3 contain all variables of interest to test our hypotheses. We can basically confirm the
findings of model 1.1, with LEGAL losing some significance, but DELTAFINLEV getting even more
significant. Contrary to our prediction that accounting malfeasance of core earnings results in more severe
investor reactions (H3a), we take notice of a consistently insignificant coefficient (one-tailed t-Test) of CORE in
all of our models. For the two-tailed test, however, the positive coefficient of CORE exhibits statistical
significance on the 10% level in the relevant model specifications with control variables. This means that error
announcements with information about non-core earnings violations yield a more severe stock price decline.
While this finding runs counter to empirical evidence of the US (see Palmrose et al., 2004; Files et al., 2009), we
interpret it as a potential bias in information processing of investors, giving current period’s abnormal earnings
more weight in the valuation model than future recurring earnings.
Our results also highlight the fact that the coefficient of professional judgment is not statistically
significant, leading us to reject our hypothesis H3b. One reason for this could be that investors do not view the
second-guessing of professional judgment as managerial misuse but as an adjustment to the complexity
management faces due to principles-based IFRS (Peterson, 2012). The same holds for the variable EARLY,
which is – opposed to our prediction – negative, but only partially statistically significant (model 2.1). This is in
line with our findings from section 5.2 that do not support hypothesis H4 of more severe investor reactions over
time, but rather the opposite. The insignificant coefficient of EARLY however cautions us to infer a change in
investors’ perception of error announcements over time. Since the estimated coefficients of EARLY are more
pronounced for the model specifications without control variables, the declined severity of investor reactions
over time seems to be rather driven by some of the control variables as e.g. TIME LAG, SIZE, and
OWNERSHIP. Except for EARLY, all variables of interest do not materially change when including control
variables (models 2.2 and 2.3).
It is noteworthy that model 2.1 exhibits a superior adjusted R2 of 23.1%, which is only slightly
attenuated for the models 2.2 and 2.3. Consequently, our additional variables of interest arguably improve the
statistical fit and explanatory power, compared to the model of Hitz et al. (2012).
21
5.4
Robustness Tests
We conduct several robustness tests to validate our results. We test the sensitivity of our capital market metrics
by shifting the event windows within the interval [-2;2] compared to the date of the error announcement. Our
(untabulated) findings suggest that the abnormal return is significantly negative in [-1] (mean: -0.95%; median: 0.31%), providing us with p-values of 0.03 each. We interpret this finding as evidence of other pre-publication
of information using relationship-based communication channels, since all publicly available pre-publication is
intended to be eliminated by our thorough sample adjustment process. However it is remarkable that an
extension of the event window to [-2;0] and [-3;1] does not yield significant results. Consequently, our findings
support the hypothesis of a capital market reaction; however it still appears questionable whether clear empirical
evidence of a sanctioning function in the German enforcement system can be inferred from it. Similar to the
return measure, the median abnormal trading volume in [-1] is significantly negative, as well as most intervals in
the range [-3;2] and the mean in [2]. The latter date is also characterized by a significantly positive median of
abnormal bid-ask spreads.
Applying different estimation windows of 100 and 200 (instead of 150 as in the original computation,
see section 4.1) trading days prior to the error announcement does not have a material impact on the findings of
section 5.2, although the findings are slightly attenuated. Moreover, we calculate an alternative measure of
abnormal returns, which is simply the difference of company return and market return (Palmrose et al., 2004;
Hitz et al., 2012). The results that we gain by this method are even weaker, e.g. the mean abnormal return (-0.72)
in [-1] is only slightly significant at the 10% level, while the median is even positive.
6
Conclusion
This paper contributes to the growing literature investigating the two-tiered German enforcement system. We
investigate determinants of investor reactions to error announcements, thereby providing further evidence on the
adverse disclosure mechanism as sole sanctioning function within this setting. Unlike Hitz et al. (2012), we can
only partially find significantly negative investor reactions to error announcements published in the federal
gazette, while we obtain some indications of private pre-publication. By distinguishing early (2006-2009) and
current years (2010-2013) of enforcement we can also provide evidence for more pronounced investor reactions
within the early years. Multivariate regression analyses however caution us to clearly infer a changed investor
22
perception of error announcements over time. In line with prior research, we can show that investor reactions are
mainly driven by the error announcements’ impact on return on equity and financial leverage. It is however
noteworthy that the magnitude of investor reactions and number of single errors are negatively associated, which
also holds true for the impact of errors that affect core earnings.
Certainly our results do not provide any evidence about the overall effectiveness of the German
enforcement system but solely on the effectiveness of the underlying sanctioning mechanism. Still, by using a
more detailed assessment of information contained in error announcements we provide further insights on
drivers of investor reactions. All in all, partially contrasting prior research, our findings provide only weak
support for the existence of the ‘name and shame’ mechanism for the two-tiered German enforcement setting.
Nevertheless, a survey by the German Issuer Association (DAI; Deutsches Aktieninstitut) and
PricewaterhouseCoopers indicates that companies still fear a loss of reputation due to error announcements
(DAI, 2013). Hence, using alternative measures of market reactions might provide further insights on the
German ‘name and shame’ mechanism. In the light of firms’ fear of reputational losses future research might
eventually replace capital market based variables, approximating investor reactions, with reputation measures.
Another interesting aspect might be that error announcements in the German enforcement setting rather state
than explain the established errors, especially when compared to 8-K filings of US firms (see Plumlee and Yohn,
2010, for some representative examples). It appears possible that extended explanations referring to the nature
and magnitude of the established error would additionally enhance investors’ perception of error announcements
and thereby positively affect the efficiency of the ‘name and shame’ mechanism. While we have not tackled this
subject within this study, it might constitute a promising avenue for future research.
References
Anderson, K. & Yohn, T. (2002). The effect of 10-K restatements on firm value, information asymmetries, and
investors’ reliance on earnings. Working Paper.
Ball , R., Kothari, S., & Robin, A. (2000). The effect of international institutional factors on properties of
accounting earnings. Journal of Accounting and Economics, 29, pp. 1-51.
Ball, R. (2006). International Financial Reporting Standards (IFRS): pros and cons for investors. Accounting and
Business Research, 36, pp. 5-27.
23
Bamber, L. (1987). Unexpected earnings, firm size, and trading volume around quarterly earnings
announcements. The Accounting Review, 62, pp. 510-532.
Beneish, M. (1999). Incentives and penalties related to earnings overstatements that violate GAAP. The
Accounting Review, 74, pp. 425-457.
Berger, A. (2010). The Development and Status of Enforcement in the European Union. Accounting in Europe,
7, pp. 15-35.
Böcking, H.-J., Gros, M., & Worret, D. (2015). Enforcement of Accounting Standards – How effective is the
German Two-Tier System in detecting Earnings Management? Review of Managerial Science, 9, pp.
431-485.
Brown, P., Preiato, J., & Tarca, A. (2014). Measuring Country Differences in Enforcement of Accounting
Standards: An Audit and Enforcement Proxy. Journal of Business Finance and Accounting, 41, pp. 152.
Brüggemann, U., Hitz, J.-M., & Sellhorn, T. (2013). Intended and unintended consequences of IFRS adoption: a
review of extant evidence and suggestions for future research. European Accounting Review, 22, pp. 137.
Burgstahler, D., Hail, L., & Leuz, C. (2006). The importance of reporting incentives: earnings management in
European private and public firms. The Accounting Review, 81, pp. 983-1017.
Bushman, R., & Piotroski, J. (2006). Financial reporting incentives for conservative accounting: the influence of
legal and political institutions. Journal of Accounting and Economics, 42, pp. 107-148.
Callao, S., & Jarne, J. (2010). Have IFRS Affected Earnings Management in the European Union? Accounting in
Europe, 7, pp. 159–189.
Callen, J., Livnat, J., & Segal, D. (2006). Accounting restatements: are they always bad news for investors? The
Journal of Investing, 15, pp. 57-68.
Carmona, S., & Trombetta, M. (2008). On the global acceptance of IAS/IFRS accounting standards: The logic
and implications of the principles-based system. Journal of Accounting and Public Policy, 27, pp. 455461.
CESR (2003). Standard No. 1 on financial information: enforcement of standards on financial information in
Europe. Available at www.esma.europa.eu.
24
CESR (2004). Standard No. 2 on financial information: coordination of enforcement activities. Available at
www.esma.europa.eu.
Christensen, H., Hail, L., & Leuz, C. (2013). Mandatory IFRS reporting and changes in enforcement. Journal of
Accounting and Economics, 56, pp. 147-177.
DAI (2013). Enforcement of Financial Information 2013. Available at www.dai.de.
Daske, H., Hail, L., Leuz, C., & Verdi, R. (2008). Mandatory IFRS reporting around the world: early evidence
on the economic consequences. Journal of Accounting Research, 46, pp. 1085-1142.
Dechow, P., Sloan R., & Sweeney, A. (1996). Causes and consequences of earnings manipulation: an analysis of
firms subject to enforcement action by the SEC. Contemporary Accounting Research, 13, pp. 1-36.
Desai, H., Hogan, C., & Wilkins, M. (2006). The reputational penalty for aggressive accounting: earnings
restatements and management turnover. The Accounting Review, 81, pp. 151-161.
Elliot, J., & Hanna, J. (1996). Repeated accounting write-offs and the information content of earnings. Journal of
Accounting Research, 34, pp. 135-155.
Elliot, J., Hanna, J., & Shaw, W. (1988). Write-off’s as accounting procedures to manage perceptions. Journal of
Accounting Research, 26, pp. 91-119.
Ernstberger, J., Stich, M., & Vogler, O. (2012a). Economic Consequences of Accounting Enforcement Reforms:
The Case of Germany. European Accounting Review, 21(2), pp. 217-251.
Ernstberger, J., Hitz, J.-M., & Stich, M. (2012b). Why do firms produce erroneous IFRS financial statements.
Working Paper.
ESMA (European Securities and Markets Authority) (2014a). Final Report – ESMA Guidelines on enforcement
of financial information. Available at www.esma.europa.eu.
ESMA (European Securities and Markets Authority) (2014b). Report - Activities of the IFRS Enforcers in
Europe in 2013. Available at www.esma.europa.eu.
Feroz, E., Park, K., & Pastena, V. (1991). The financial and market effects of SEC’s accounting and auditing
enforcement releases. Journal of Accounting Research, 29, pp. 107-142.
Files, R., Swanson, E., & Tse, S. (2009). Stealth Disclosure of Accounting Restatements. The Accounting
Review, 84, pp. 1495-1520.
FREP (2009). Principles of random-based investigations. Available at www.frep.info.
FREP (2014). Annual activity report 2013. Available at www.frep.info.
25
GAO (General Accounting Office) (2002). Financial Statement Restatements: Trends, Market Impact,
Regulatory Responses, and Remaining Challenges. Available at http://www.gao.gov/.
Glaum, M., Baetge, J., Grothe, A., & Oberdörster, T. (2013). Introduction of International Accounting
Standards, Disclosure Quality and Accuracy of Analysts' Earnings Forecasts. European Accounting
Review, 22, pp. 79–116.
Hennes, K., Leone, A., & Miller, B. (2008). The Importance of Distinguishing Errors from Irregularities in
Restatement Research: The Case of Restatements and CEO/CFO Turnover. The Accounting Review, 83,
pp. 1487-1519.
Hitz, J.-M., Ernstberger, J., & Stich, M. (2012). Enforcement of Accounting Standards in Europe: CapitalMarket-Based Evidence for the Two-Tier Mechanism in Germany. European Accounting Review, 21,
pp. 253-281.
Hoffmann, S., & Höltken, M. (2014). Deutschland und Österreich als Beispiele der Implementierung hybrider
Enforcement-Systeme in Europa. Die Wirtschaftsprüfung, 67, pp. 730-736.
Hope, O.-C. (2003). Disclosure practice, enforcement of accounting standards and analysts’ forecast accuracy:
an international study. Journal of Accounting Research, 41, pp. 235-272.
Hribar, P., & Jenkins, N. (2004). The effect of accounting restatements on earnings revisions and the estimated
cost of capital. Review of Accounting Studies, 9, pp. 337-356.
Karpoff, J., Lee, S., & Martin, G. (2008). The cost to firms of cooking the books. Journal of Financial and
Quantitative Analysis, 43, pp. 581-611.
Jackson, H., & Roe, M. (2009). Public and private enforcement of securities laws: Resource-based evidence.
Journal of Financial Economics, 93, pp. 207-238.
Jansen, I., Ramnath, S., & Yohn, T. (2012). A Diagnostic for Earnings Management Using Changes in Asset
Turnover and Profit Margin. Contemporary Accounting Research, 29, pp. 221-251.
Jensen, M., & Ruback, R. (1983). The market for corporate control: The scientific evidence. Journal of
Financial Economics, 11, pp. 5-50.
La Porta, R., Lopez-de-Silanes, F., Shleifer, A., & Vishny, R. (1998). Law and finance. Journal of Political
Economy, 106, pp. 1113-1155.
La Porta, R., Lopez-de-Silanes, F., & Shleifer, A. (2006). What works in securities law? Journal of Finance, 61,
pp. 1-31.
26
Leuz, C. (2010). Different approaches to corporate reporting regulation: how jurisdictions differ and why.
Accounting and Business Research, 40, pp. 229-256.
Leuz, C., & Wüstemann, J. (2004). The role of accounting in the German financial system. In: J. Krahnen und
R.H. Schmidt (Eds), The German Financial System (Oxford: Oxford University Press).
Leuz, C., Nanda, D., & Wysocki, P. (2003). Earnings management and investor protection: an international
comparison. Journal of Financial Economics, 69, pp. 505-527.
Leuz, C., & Verecchia, R. (2000). The economic consequences of increased disclosure. Journal of Accounting
Research, 38, pp. 91-124.
MacKinlay, A. (1997). Event Studies in Economics and Finance. Journal of Economic Literature, 35, pp. 13-39.
Mattheus, D., & Schwab, M. (2004). Fehlerkorrekturen nach dem Rechnungslegungs-Enforcement: Private
Initiative vor staatlicher Intervention, Betriebs-Berater, 59, pp. 1099-1106.
Nobes, C. (2005). Rules-based standards and the lack of principles in accounting. Accounting Horizons, 19(1),
pp. 25-34.
Nourayi, M. (1994). Stock price responses to the SEC’s enforcement actions. Journal of Accounting and Public
Policy, 13, pp. 333-347.
Palmrose, Z.-V., Richardson, V., & Scholz, S. (2004). Determinants of market reactions to restatement
announcements. Journal of Accounting and Economics, 37, pp. 59-89.
Palmrose. Z.-V., & Scholz, S. (2004). The circumstances and legal consequences of non-GAAP reporting:
evidence from restatements. Contemporary Research, 21, pp. 139-180.
Penman, S. (2007). Financial Statement Analysis and Security Valuation. McGraw-Hill Higher Education, 3rd
edition, Boston.
Peterson, K. (2012). Accounting complexity, misreporting, and the consequences of misreporting. Review of
Accounting Studies, 17, pp. 72-95.
Plumlee, M., & Yohn, T. (2010). An analysis of the underlying causes attributed to restatements. Accounting
Horizons, 24, pp. 41-64.
Preiato, J., Brown, P, & Tarca, A. (2013). Mandatory IFRS and properties of analysts’ forecasts: how much does
enforcement matter? Working Paper.
Richardson, S., Tuna, I., & Wu, M. (2002). Predicting earnings management: the case of earnings restatements.
Working Paper.
27
Salewski, M., Teuteberg, T., & Zülch, H. (2014). Short-Term and Long-Term Effects of IFRS Adoption on
Disclosure Quality and Earnings Management. Working Paper.
Salterio, S. (2014). We don’t replicate accounting research – or do we? Contemporary Accounting Research,
31(4), pp. 1134-1142.
Schipper, Katherine (2003). Principles-based accounting standards. Accounting Horizons, 17), pp. 61-72.
Srinivasan, S. (2005). Consequences of financial reporting failure for outside directors: evidence from
accounting restatements and audit committee members. Journal of Accounting Research, 43, pp. 291334.
Strohmenger, M. (2014). Enforcement releases, firm characteristics, and earnings quality: insights from
Germany’s two-tiered enforcement system. Journal of International Financial Management and
Accounting, 25, pp. 271-304.
Strong, J., & Meyer, J. (1987). Asset writedowns: managerial incentives, and securities returns. The Journal of
Finance, 43, pp. 643-661.
Swanson, E., Tse, P., & Wanalda, R. (2007). Does the format of a press release announcing a restatement effect
the market response and litigation? Working Paper.
van Tendeloo, B., & Vanstraelen, A. (2005). Earnings Management under German GAAP versus IFRS.
European Accounting Review, 14, pp. 155–180.
Verriest, A., Gaeremynck, A., & Thornton, D. (2012). The Impact of Corporate Governance on IFRS Adoption
Choices. European Accounting Review, 22, pp. 39–77.
Vishny, R., & Shleifer, A. (1997). A Survey on Corporate Governance. Journal of Finance, 52, pp. 737-783.
28
Appendix
Table 1. FREP examinations and errors established (2005-2013)
2005
2006
2007
2008
2009
2010
2011
2012
2013
Total
Proportion
Total
7
109
135
138
118
118
110
113
110
958
100%
Sampling-based
4
98
118
118
103
106
90
110
98
845
88%
Indication-based
3
10
15
19
14
8
6
2
6
83
9%
Mandatory request by BaFin
0
1
2
1
1
4
14
1
6
30
3%
207
21.61%
Examinations by FREP
Error findings by FREP/BaFin
Total
Proportion
2
19
35
37
23
31
27
18
15
28.57%
17.43%
25.93%
26.81%
19.49%
26.27%
24.55%
15.93%
13.64%
Notes: Number of examinations and error announcements are taken from the annual activity reports of the FREP (2005-2013). Error findings are published with some time lag
and therefore regularly do not correspond with completed examinations stated for the respective year.
29
Table 2. Variable definitions and data sources
Panel A
Variable
Abbreviation
Definition
Data Source
CAR
Cumulative abnorman returns are calculated on a stockspecific market model (MacKinlay, 1997) with reference to a
weighted index of all firms listed on a regular German market.
Datastream
Cumulative abnormal trading volume
CATV
Daily abnormal trading volumes are calculated as the
difference of daily trading volume and expected trading
volume (Bamber, 1987). Only shares that are not closely held
are considered.
Datastream
Abnormal bid-ask-spread
ABAS
The abnormal bid-ask spread is measured as the residual
which cannot be explained by the independent variables
stock price and trading volume (Dechow et al., 1996).
Datastream
Impact on return-on-equity
DELTAROE
The difference in return-on-equity is measured on the basis
of effects on net income and book value of equity. The
difference is calculated as the RoE of the erroneous financial
statement minus the RoE of the corrected financial statement
according to the error announcement. This means that
DELTAROE takes positive values if profitability has been
overstated, which can be regarded as a negative signal to
equity investors. DELTAROE is assessed in percentage
points.
Datastream
Impact on financial leverage
DELTAFINLEV
The difference in financial leverage is measured on the basis
of effects on both book value of assets and debt. The
difference is calculated as the financial leverage
(debt/assets) of the corrected financial statement minus the
financial leverage of the erroneous financial statement. This
means that DELTAFINLEV takes positive values if financial
leverage has increased, which can be regarded as a negative
signal to equity investors. DELTAFINLEV is assessed in
percentage points.
Datastream
Total numbers of single errors
NUMBERRORS
Total number of single errors established within one error
announcement.
handcollected
Risk of legal litigation
LEGAL
Dummy variable that is coded as one if error announcement
dircetly affects legal entity accounts.
handcollected
Involvement of BaFin
BAFIN
Dummy variable that is coded as one if error is established
by BaFin.
handcollected
Effect on core earnings
CORE
Dummy variable that is coded as one if error affects current
or future core earnings. Core earnings are identified in
compliance with Penman (2007).
handcollected
PROFJUDG
Dummy variable that is coded as one if error is established
due to second-guessing of professional judgement.
handcollected
EARLY
Dummy variable that is coded as one if error is published in
the federal gazette between 2006 and 2009.
handcollected
Dependent variables
Cumulative abnormal returns
Variables of interest
Employment of professional judgment
Effects related to changes over time
30
Table 2. Variable definitions and data sources
Panel B
Variable
Abbreviation
Definition
Data Source
Control variables
Quality of corporate governance
Number of GCGC violations
Proportion of variable compensation
Earnings management
Resources of firm under investigation
Company growth
OPPORTUNISM
First factor of principal component analysis (PCA).
GCGCNumb
Listed firms are required to disclose their compliance with
the German Corporate Governance Codex (GCGC). Otherwise
they have to explain violated requirements. Violations are
measured as total numbers of violated requirements.
handcollected
VarComp
German firms are required to disclose different components
of executive compensation. The proportion of variable
compensation indicates the existence short-term-motivated
(opportunistic) motives.
handcollected
EM
Dummy variable that is coded as one in case of (upward or
downward) earnings management, following a proposed
model of Jansen et al. (2012). Upward earnings management
is established if the firm's profit margin increases and asset
turnover decreases compared to the prior year (downward
earnings management vice versa), furthermore it must not be
a reversal of prior year's figures.
handcollected
RESOURCES
First factor of principal component analysis (PCA).
Growth
Accounting resources are positively associated with a firm's
maturity and indicates thus a lack of resources if a firm is in a
growth status. Growth is calculated as the five-year
geometric mean in sales prior to the error announcement.
handcollected
Years of IFRS experience
IFRSyears
The change from German Commercial Code to IFRS was a
major change in terms of accounting and disclosure rules for
German firms (Leuz and Verecchia, 2000). IFRS knowledge
thus increases over time and is measured in absolute years
since the firm has adopted IFRS for the first time.
handcollected
Firm complexity
Complexity
Firm complexity is measured as the residual of an industryspecific regression of business segments on firm size.
handcollected
Change in executive management and auditors
CHANGE
Dummy variable that is coded as one if executive
management or auditor was changed between erroneous
financial statement and error announcement.
handcollected
Time that information is available to investors
TIME LAG
Number of days between balance sheet date and error
announcement.
handcollected
SIZE
The firms' size is measured as the natural logarithm of market
capitalization at the beginning of the year of the error
announcement (in Mio. Euros).
Datastream
Financial leverage
LEVERAGE
Financial leverage is calculated as ratio of book value of
debt to book value of assets.
Datastream
Liquidity of equity shares
LIQUIDITY
The liquidity of firms' shares is measured as the number of
days with non-zero returns in the 150-day window prior to
the error announcement.
Datastream
OWNERSHIP
Ownership is used as an indicator for the usage of
information that is not publicly available. Hence, it is
measured as the proportion of non-closely held shares.
Datastream
Firm size
Non-closely held shares
31
Table 3. Descriptive statistics
Panel A: 2006-2013
Mean
Std. Deviation
Lower Quartile
Median
Upper Quartile
N
DELTAROE
-0.130
1.960
0.000
0.000
0.057
79
DELTAFINLEV
0.038
0.094
0.000
0.000
0.029
79
NUMBERRORS
3.838
1.000
2.000
5.000
79
LEGAL
4.066
0.127
BAFIN
0.203
79
CORE
0.380
79
PROFJUDG
0.481
79
EARLY
0.595
79
Components of OPPORTUNISM
GCGCNumb
79
8.651
13.294
0.000
6.000
10.000
79
VarComp
0.301
0.196
0.214
0.301
0.301
79
EM
0.215
79
Components of RESOURCES
Growth
8.201
28.981
-5.840
4.680
14.160
79
IFRSyears
3.606
2.861
1.499
3.249
5.003
79
Complexity
0.019
0.939
-0.526
0.019
0.545
79
CHANGE
79
0.494
TIME LAG
600.874
237.647
455.000
543.000
700.000
79
SIZE (non-log definition)
939.878
2607.469
13.743
61.400
725.360
79
LEVERAGE
0.670
0.327
0.543
0.654
0.792
79
OWNERSHIP
45.627
22.455
30.390
45.627
47.700
79
LIQUIDITY
117.625
24.573
104.000
123.000
140.000
79
32
Table 3. Descriptive statistics
2006-2009
Panel B: Difference over time
2010-2013
Difference over time
Mean
Median
Mean
Median
Mean
DELTAROE
-0.245
0.000
0.037
0.000
0.282
0.534
0.000
0.057
DELTAFINLEV
0.037
0.000
0.041
0.000
0.005
0.834
0.000
0.785
NUMBERRORS
4.749
3.000
3.063
2.000
-1.687
0.055
-1.000
0.188
LEGAL
0.191
0.031
-0.160
0.036
BAFIN
0.149
0.281
0.132
0.155
CORE
0.426
0.313
-0.113
0.316
PROFJUDG
0.553
0.375
-0.178
0.123
Components of OPPORTUNISM
GCGCNumb
8.638
0.032
0.992
1.000
0.628
0.000
0.893
VarComp
0.297
EM
0.234
6.000
0.301
8.670
0.306
7.000
0.301
0.188
p-value
0.009
0.837
-0.047
0.627
Median
p-value
Components of RESOURCES
7.922
3.980
8.612
6.875
0.690
0.918
2.895
0.968
IFRSyears
2.555
2.000
5.149
4.251
2.594
0.000
2.251
0.000
Complexity
-0.003
0.019
0.051
0.019
0.054
0.804
0.000
0.692
0.063
0.587
Growth
CHANGE
0.468
0.531
TIME LAG
623.938
581.000
567.000
512.000
-56.938
0.299
-69.000
0.199
SIZE (non-log definition)
767.940
61.166
1192.413
125.018
424.472
0.443
63.852
0.466
LEVERAGE
0.720
0.653
0.596
0.662
-0.125
0.097
0.009
0.542
OWNERSHIP
46.745
45.627
43.984
45.627
-2.761
0.595
0.000
0.605
0.993
0.500
0.753
LIQUIDITY
117.603
123.000
117.656
123.500
0.053
Notes: Panel A shows the descriptive statistics for the years 2006-2013, while Panel B provides a separated analysis for the periods 2006-2009 and 2010-2013. The underlying
dataset has been winsorized at the 1st and 99th percentile. The denoted p-values indicate two-tailed statistical significance.
33
Table 4. Correlations
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
0.06
-0.22
-0.07
(11)
0.04
0.06
-0.14
-0.11
-0.07
0.00
-0.01
0.08
0.07
-0.17
0.07
0.23
-0.02
0.16
-0.14
-0.17
0.18
-0.05
0.25
0.35
0.22
0.09
-0.07
-0.01
-0.10
-0.06
0.17
0.24
0.18
-0.01
0.06
0.08
-0.16
-0.12
0.11
0.55
0.11
0.05
-0.16
-0.20
0.18
0.06
-0.15
-0.09
0.01
-0.26
-0.06
-0.11
0.01
0.04
(12)
(13)
(14)
(15)
0.13
-0.02
0.03
0.02
0.02
-0.15
-0.24
0.06
0.11
-0.01
0.20
-0.15
0.17
0.06
-0.14
0.16
0.25
-0.18
0.36
-0.12
-0.09
-0.12
0.47
0.13
-0.15
-0.02
-0.24
0.18
0.09
-0.09
0.10
-0.06
0.21
-0.03
0.12
0.20
-0.07
0.12
-0.09
0.19
0.06
0.00
0.11
-0.60
0.15
-0.19
-0.35
-0.15
0.12
-0.11
-0.11
0.03
0.10
0.01
0.06
-0.19
0.07
0.16
-0.19
-0.27
-0.14
0.07
0.42
-0.16
-0.24
DELTAROE
(1)
DELTAFINLEV
(2)
0.26
NUMBERRORS
(3)
0.13
LEGAL
(4)
-0.09
0.18
0.17
BAFIN
(5)
-0.12
-0.15
-0.11
-0.10
CORE
(6)
0.23
0.29
0.34
-0.06
0.06
PROFJUDG
(7)
0.26
0.50
0.38
0.17
0.08
0.55
EARLY
(8)
0.22
0.03
0.15
0.24
-0.16
0.11
0.18
OPPORTUNISM
(9)
0.02
0.29
0.12
0.11
-0.17
0.02
0.04
-0.04
RESOURCES
(10)
0.16
0.03
0.00
-0.01
0.10
-0.16
-0.13
-0.32
-0.01
CHANGE
(11)
-0.16
-0.12
-0.05
0.16
-0.12
-0.20
-0.09
-0.06
0.04
0.08
TIME LAG
(12)
0.08
-0.09
0.16
0.20
0.40
0.21
0.21
0.15
-0.03
-0.18
0.08
SIZE
(13)
-0.12
-0.26
-0.15
-0.19
0.14
0.11
-0.01
-0.08
-0.60
-0.06
-0.01
-0.08
LEVERAGE
(14)
0.04
-0.13
0.14
0.26
-0.11
-0.02
0.08
0.07
-0.01
-0.08
0.12
0.19
OWNERSHIP
(15)
0.23
0.15
0.18
-0.07
-0.03
0.08
0.22
0.06
-0.10
-0.05
-0.14
-0.23
0.01
-0.21
LIQUIDITY
(16)
-0.01
-0.13
-0.13
-0.08
-0.14
-0.06
-0.09
-0.04
-0.40
0.00
0.07
-0.32
0.53
-0.23
0.14
-0.13
-0.10
(16)
0.34
0.31
Notes: The table shows Pearson correlations (above the diagonal) and Spearman correlations (below the diagonal) for all variables included in the multivariate regressions
employed in this study (79 oberservations), covering the investigation period 2006-2013. Variables are defined in Table 2 and winsorized at the 1st and 99th percentile. Marked
correlations indicate two-tailed significance at the 5% level.
34
Table 5. Investor reactions to error announcements
PANEL A: 2006-2013
CAR
Event window
CATV
ABAS
[0]
[-1;1]
[-2;2]
[0]
[-1;1]
[-2;2]
[0]
[-1;1]
[-2;2]
Mean
0.582
-0.693
0.599
0.029
0.097
0.006
8.769
11.706
14.971
p-value
0.867
0.169
0.725
0.766
0.874
0.522
0.115
0.036
0.019
Standard deviation
4.873
6.385
8.517
0.318
0.641
0.782
56.860
47.170
50.203
First quartile
-0.768
-3.445
-4.059
-0.029
-0.076
-0.078
-21.580
-18.594
-17.820
Median
-0.037
-0.565
-0.295
-0.008
-0.003
-0.006
7.397
6.702
9.742
p-value
0.336
0.075
0.243
0.014
0.429
0.330
0.089
0.093
0.034
Third quartile
1.248
1.417
2.018
0.004
0.094
0.064
52.335
35.831
40.607
88
79
73
66
58
54
62
55
51
Number of observations
PANEL B: 2006-2009
CAR
Event window
CATV
ABAS
[0]
[-1;1]
[-2;2]
[0]
[-1;1]
[-2;2]
[0]
[-1;1]
[-2;2]
Mean
0.674
-1.048
0.398
0.058
0.146
0.054
10.206
19.950
21.499
p-value
0.867
0.114
0.606
0.839
0.912
0.696
0.174
0.018
0.027
Standard deviation
4.447
5.875
9.614
0.364
0.608
0.579
65.280
50.598
57.782
First quartile
-0.758
-4.369
-4.821
-0.031
-0.039
-0.073
-30.532
-11.887
-11.972
Median
-0.051
-0.833
-0.786
-0.008
-0.002
-0.005
7.785
11.672
11.207
p-value
0.319
0.016
0.158
0.131
0.302
0.396
0.120
0.028
0.035
Third quartile
1.225
0.613
1.999
0.021
0.249
0.138
68.972
40.270
52.658
55
47
43
40
33
31
37
31
29
Number of observations
PANEL C: 2010-2013
CAR
Event window
CATV
ABAS
[0]
[-1;1]
[-2;2]
[0]
[-1;1]
[-2;2]
[0]
[-1;1]
[-2;2]
Mean
0.428
-0.171
0.888
-0.016
0.033
-0.058
6.641
1.058
6.367
p-value
0.669
0.446
0.760
0.364
0.595
0.391
0.222
0.450
0.218
Standard deviation
5.581
7.134
6.790
0.232
0.689
1.005
42.628
40.921
37.587
First quartile
-0.832
-1.594
-3.048
-0.031
-0.100
-0.108
-20.511
-26.592
-25.632
Median
0.082
0.320
0.269
-0.009
-0.003
-0.007
-0.691
-9.439
0.398
p-value
0.511
0.687
0.601
0.013
0.184
0.158
0.736
0.645
0.296
Third quartile
1.483
2.689
3.479
0.001
0.029
0.053
35.438
18.227
32.297
33
32
30
26
25
23
25
24
22
Number of observations
Notes: This table displays descriptive statistics for (cumulative) abnormal returns [CAR], (cumulative) abnormal relative trading volumes [CATV],
and (mean) abnormal relative bid-ask-spreads [ABAS] for all available error announcements, covering the years 2006-2013, 2006-2009 and 20102013. All observations have been winsorized at the 1st and 99th percentile. The descriptive statistics, which are denoted in percent, are provided
for the event day [0] as well as for the event windows [-1;1] and [-2;2] surrounding the error announcement day. P-values denote statistical
significance for one-tailed tests. In line with section 3.2, we expect CAR and CATV (ABAS) to take negative (positive) values.
35
Table 6. Drivers of cumulative abnormal returns
Model 1.1
Expected
sign
Intercept
Model 1.2
Model 1.3
Model 2.1
Model 2.2
Model 2.3
Coeff.
t-Stat.
Coeff.
t-Stat.
Coeff.
t-Stat.
Coeff.
t-Stat.
Coeff.
t-Stat.
Coeff.
t-Stat.
-0.014
-1.70*
0.011
0.21
0.003
0.05
-0.009
-0.86
0.020
0.38
0.006
0.11
Variables of interest
DELTAROE
(-)
-0.007
-5.70***
-0.006
-4.75***
-0.006
-4.30***
-0.006
-4.71***
-0.005
-3.44***
-0.005
-2.87***
DELTAFINLEV
(-)
-0.161
-1.59*
-0.147
-1.41*
-0.141
-1.25
-0.194
-1.98**
-0.199
-2.25**
-0.199
-2.15**
NUMBERRORS
(-)
0.005
2.45
0.006
2.62
0.006
2.56
0.004
1.85
0.004
1.85
0.005
1.86
LEGAL
(-)
-0.034
-1.71**
-0.032
-1.62*
-0.032
-1.61*
-0.025
-1.25
-0.025
-1.36*
-0.026
-1.32*
BAFIN
(-)
-0.014
-0.72
-0.002
-0.11
-0.001
-0.06
-0.022
-1.15
-0.001
-0.05
0.002
0.10
CORE
(-)
0.025
1.44
0.037
1.88
0.037
1.80
PROFJUDG
(-)
0.008
0.41
0.012
0.60
0.016
0.78
EARLY
(+)
-0.021
-1.67
-0.015
-0.99
-0.013
-0.81
-0.007
-0.86
Control variables
OPPORTUNISM
GCGCNumb
-0.003
-0.30
0.000
-0.24
0.000
-0.48
VarComp
0.015
0.29
0.047
0.90
EM
0.011
0.63
0.008
0.43
-1.16
RESOURCES
0.005
0.86
0.006
0.95
Growth
0.000
-0.59
0.000
IFRSyears
0.003
1.01
0.002
0.79
Complexity
-0.003
-0.49
-0.004
-0.65
CHANGE
0.011
0.82
0.011
0.81
0.015
1.04
0.017
1.18
TIME LAG
0.000
-1.09
0.000
-0.96
0.000
-1.71*
0.000
-1.54
SIZE
-0.002
-0.50
-0.003
-0.55
-0.006
-1.35
-0.007
-1.29
LEVERAGE
-0.004
-0.22
-0.003
-0.19
0.006
0.32
0.004
0.21
OWNERSHIP
0.000
-0.88
0.000
-0.90
0.000
-1.25
0.000
-1.40
LIQUIDITY
0.000
0.16
0.000
0.10
0.000
0.42
0.000
0.41
2
Unadjusted R
0.247
0.287
0.301
0.310
0.379
Adjusted R2
0.196
0.144
0.107
0.231
0.219
0.189
73.68***
36.53***
21.41***
30.48***
18.00***
12.02***
79
79
79
79
79
79
F-statistic
Number of observations
0.397
Notes: This table shows results from linear regressions of the three-day cumulative abnormal return in [-1;1] on several variables of interest and control variables. All observations have been winsorized at the 1st and 99th
percentile. The levels of significance have been calculated using White heteroscedasticity-robust standard errors. The untabulated regression results with non-robust standard errors do not lead to materially different
findings. *, ** and *** denote statistical significance on the 10%, 5% and 1% level (one-tailed for all variables of interest, two-tailed for the intercept and the control variables).
36
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