The Impact of XBRL Adoption on the Information Environment
Evidence from Japan

ABSTRACTThis article investigates whether the Japanese Financial Services Agency’s mandatory useof XBRL affects investors in assessing the financial information. Although regulators
expect the introduction of XBRL to enhance the transparency and quality of business
reporting, given the non-trivial implementation and learning costs, whether the XBRLbased disclosure improves the information environment is an empirical question. As priorstudies in other countries provide mixed results, our analysis of the Japanese case should bequite insightful, because Japan is one of the few countries where thousands of listed firms
were forced to use the XBRL format all at once. By examining various measures in the preand post-XBRL periods, we provide evidence consistent with the notion that the adoptionof XBRL has helped to improve the information environment, as shown by the reduction of
event returns volatility, absolute cumulative abnormal returns, changes in the standard
deviation of returns, and abnormal bid-ask spread.

Keywords: XBRL; information asymmetry; capital market; event study
Data availability: All data used in this study is publicly available.

21. Introduction
The eXtensible Business Reporting Language (XBRL) is under fast transition fromthe vision phase to becoming a practical global standard for financial and business
reporting. XBRL is aimed to allow efficient compilation, distribution, and usage offinancial statements. It is an open standard that provides a way to model businessinformation and to articulate its semantic meaning. 1 Internationally, the XBRL data
standard is a platform for addressing the problem of data integrity, timeliness, andreusability. In Asia, stock exchanges in China, Japan, Singapore, and South Korea have all
mandated the use of XBRL data. 2 In April 2009, the U.S. Securities and Exchange
Commission (SEC) mandated that listed firms report their financial statement data using
XBRL, with the goal of using XBRL as a binding and exclusive format in the U.S. in the
future. As of 2011, there were 50 XBRL projects in 15 European countries that were
established or in progress.3
A considerable amount of research related to XBRL has been carried out over the last
decade, with a significant increase in related publications in recent years. Among them,
research on the effects of the adoption and diffusion of XBRL on capital markets is one of
the more emphasized areas. Several studies provide evidence that the introduction of XBRL
reduces information asymmetry (Tan and Shon 2009; Efendi et al. 2010; Yoon et al. 2011;
Kim et al. 2012), while more recent research provides contradicting results (Blankespoor etal. 2012).
As prior studies provide mixed results, the question of whether the use of XBRL helps
to improve the information environment in capital markets is left as an empirical question.
The present study aims to contribute to related literature by examining the Japanese data.
We believe that investigation of the Japanese case should provide useful empirical results,
because Japan is one of the few countries where approximately 5,000 listed firms and 3,000
funds were forced to use the XBRL format at the same time (Kobayashi 2008). 41

According to the U.S. SEC, the use of XBRL will allow investors and analysts to “capture and analyze
information more quickly and at less cost than is possible using the same financial information provided in astatic format” (SEC 2009).
2 China had the first capital market to adopt XBRL. In 2004, the China Securities Regulatory Commission,
the Shanghai Stock Exchange and the Shenzhen Stock Exchange, mandated all publicly traded firms to filetheir financial statement using XBRL as part of their effort to increase the overall attractiveness of theChinese equity markets (Peng et al. 2011).
3 Among the projects in Europe that involved mandatory filings are those by the U.K. Companies House,
Belgium’s National Bank, banking industry and government in Spain, Danish Commerce and Companies
Agency, and Italy Infocamere (ISARG 2011).
4 While the U.S. SEC employed a phase-in period to adopt XBRL, Japan’s EDINET introduced the official
XBRL mandate in 2008 after two pilot projects in which about 50 of the filing companies chosen by the FSA
tested the taxonomies in the 1st project and 1,223 firms voluntarily participated in the 2nd project. It is true thatthe phased adoption (such as in the US) enables researchers to have a control sample that do not employ
XBRL, while we cannot have such a control sample as all listed firms in Japan were forced to employ XBRL
all at once. However, the phrased adoption may cause difficulty in isolating the effects of the XBRL adoption

3

Compared to Japan, research on other countries covers a relatively small number ofobservations. For instance, the U.S. SEC required large accelerated filers (approximately
500 firms)5 to disclose their financial information in the XBRL format in 2009, althoughmore than 10,000 listed firms were required to do so after December 2011. The prior studyof the Korean implementation of XBRL use investigates 550 listed firms (Yoon et al. 2011),
while the study of the Chinese case covers about 1,300 listed firms per year (Peng et al.
2011).
Japan has been an early and active adopter of XBRL. 6 The XBRL Japan organization
was established as far back as April 2001 to help promote and create awareness of XBRL
within the country. Since the latter half of the 2000s, XBRL has been adopted and used in
production by key governmental and public organizations (Hoffman and Watson 2010),
including the Financial Systems and Bank Examination Department of the Bank of Japan(BOJ), the National Tax Agency of Japan (NTA), the Tokyo Stock Exchange (TSE), andthe Financial Services Agency (FSA).
The FSA has an Electronic Disclosure for Investors’ Network (EDINET) system andall listed firms and investment funds in Japan are required to file their disclosure documents
using the system. The FSA launched a new EDINET system in preparation for the adoptionof XBRL in 2008. All filers are mandated to submit the financial statements for fiscal years
starting in or after April 2008 in the XBRL format. The XBRL mandate is part of the
FSA’s continuing efforts to use XBRL to achieve multinational interoperability together
with the International Accountings Standards Committee Foundation and the U.S. SEC.
The adoption of XBRL in the new EDINET system is expected to enable investors to
download financial data and directly process and analyze the data. If the use of XBRL is
successful in reducing information processing costs and information asymmetry, theintroduction of XBRL may not only assist in enhancing the transparency of the securities
market but also improve the information structure in ways that will enhance the efficiencyof economic and social activities in general.
The purpose of this paper is to examine whether the FSA’s mandatory financial
reporting using XBRL has affected investors in assessing financial information.
Specifically, we investigate the effects of using XBRL on the information environment in
the Japanese market by assessing various measures in the pre- and post-XBRL periods. Our
results are consistent with the notion that the adoption of XBRL helps to improve theinformation environment in the Japanese market, as shown by the reduction of event returns
volatility, absolute cumulative abnormal return, change in standard deviation of return, andfrom the effects of other economic events, occurring at different times, while the one-time adoption requires
us to consider only the effects of the events occurring at the same time.
5
Specifically, the large accelerated filers here means those using U.S. GAAP with a worldwide public float
over $5 billion.
6
Detailed description on the XBRL adoption in Japan is provided in Appendix 1.

4

abnormal bid-ask spread.7
The rest of this article is organized as follows. Section 2 provides a literature review
and hypothesis development. Section 3 describes the data and methodology used in the
research. Sections 4 and 5 discuss the empirical results and robustness tests, respectively.
Lastly, Section 6 provides a conclusion for the paper.

2. Literature review and hypothesis development
2.1 Literature review
A considerable amount of research related to XBRL has already been carried out
over the last decade, with a significant increase in relevant publications in recent years.8
According to Mike Starr from the U.S. SEC, the main benefits of using XBRL are more
timely access and greater transparency for investors and enhanced efficiency in analyzing
the data by the regulator, both of which will result in a more effective regulatory process(Starr 2012). Both the market and companies stand to benefit from this gained transparency
as investors appreciate having more information about the company readily available.9 This
improved, transparent, and real-time financial reporting and disclosure of data in the XBRL
format is expected to reduce each participating firm’s cost of capital (Pinsker and Li2008).10
One drawback is that the large setup and learning costs of implementing XBRL
7 One may argue that the timing of XBRL-adoption was so close to the world financial crisis in 2008 that may

make it difficult to isolate the effects of XBRL from the effects of the world financial crisis. However, we canreasonably assume that the financial crisis may increase uncertainty and information asymmetry, while the
XBRL adoption is expected to reduce information asymmetry, which is consistent with our results.
8
The increased efforts related to the use of XBRL by the U.S. SEC together with the availability of the first
empirical data from real XBRL-based financial reports from 2008 may have been a significant driving force
to explain the increase in XBRL publications in recent years (Roohani et al. 2010).
9
The increased transparency by the use of the XBRL may also improve the corporate governance of filers.
For instance, Peng et al. (2011) examine the level of total accruals that firms report before and after the XBRL
adoption in China. Their results indicate lower level of total accruals in the post-XBRL period, with this effectmost prominent for high-growth firms, small firms, and firms in high-technology industries. They argue thatXBRL implementation decreases an investor’s information acquisitions costs and improves an investor’sability to detect earnings management. Accordingly, managers are pressured to reduce accruals. In addition,
Premuroso and Bhattacharya (2008) examine whether early and voluntary filers of financial information in
XBRL format demonstrate superior corporate governance and operating performance relative to their nonadopting peers. The results of their study suggest that early and voluntary XBRL adoptions are indeed
indicators of superior corporate transparency and related corporate governance that are expected to benefit
adopting firms in the long run.
10
Another advantage of using XBRL tags is that otherwise unused information in the notes becomes more
usable. Henselmann and Scherr (2012) develop an automated content analysis technique to assess thebankruptcy of companies using XBRL tags. A list of potential red flags based on the U.S. Generally Accepted
Accounting Principles (GAAP) taxonomy is developed based on the annual reports of 26 companies with
Chapter 11 bankruptcy filings and a control group. The empirical results reveal that the red flag item list has
predictive power of bankruptcy risk. Logistic regression results also showed that the predictive powerincreases as the bankruptcy filing date approaches .

5

reporting may deter both filers and investors from using XBRL. Because of its complicatedtechnology, the introduction of XBRL may involve many difficulties, resulting in an
increase of preparation cost. In fact, the U.S. committee on corporate reporting of Financial
Executives International also expressed its concerns that requiring filers to adopt XBRL
would result in increased costs with no improvements to internal processes (Dzinkowski2008). In addition, prior studies cast doubt on the reporting quality of XBRL, as errors orincompleteness of financial reporting are still observed (Zhu and Fu 2009; Debreceny et al.
2010; Bartley et al. 2011; Roohani and Zhen 2011).11
These shortcomings raise the issue of whether the information environment(asymmetry) is improved (reduced) by the introduction of XBRL. One of the importantfactors that will allow us to answer this question is whether the accessibility of financial
information is improved by the introduction of XBRL. From the viewpoint of investors, aunique aspect of the XBRL mandate is that it requires investors to incur significant setup
costs to achieve the intended benefits. Thus, those that perceive the costs to outweigh thebenefits may decide to forego or delay the use of XBRL. For example, investors withrelatively fewer resources may refrain from the use of XBRL until they have a better
understanding of the associated costs and it is likely that only a subset of investors will
choose to use XBRL in the initial year of mandate.
A couple of prior studies examine whether the use of XBRL can help investors toacquire financial information based on experimental methodology (Hodge et al. 2004;
Pinsker and Wheeler 2009). Hodge et al. (2004) employ experimental research and examine
whether using an XBRL-enhanced search engine helps nonprofessional financial statement
users acquire and integrate related financial information when making an investment
decision. Their results suggest that XBRL should be beneficial for the users by improving
the transparency of firms’ financial statement information, although nonprofessional users
may not automatically use XBRL, as half of their experimental participants with access to
XBRL technology do not use the technology.
Pinsker and Wheeler (2009) complement the findings of Hodge et al. (2004) byinvestigating the relationship between XBRL use and perception by surveying 61 MBA
students as proxies for nonprofessional investors. Their results indicate that nonprofessional
investors who use XBRL-formatted information have more accurate perceptions ofanalytical effectiveness and efficiency in utilizing XBRL-based information as compared topaper-based investors performing non-directed analysis.
Although the experimental studies suggest the improved accessibility of financial
information following the introduction of XBRL, other prior studies provide mixed results
regarding whether the introduction of XBRL is associated with the reduction of information
asymmetry (Tan and Shon 2009; Efendi et al. 2010; Yoon et al. 2011; Kim et al. 2012;
Blankespoor et al. 2012). Tan and Shon (2009) report that U.S. firms that voluntarily file in
the XBRL format enjoy reduced information asymmetries in the form of reduced bid-ask
11

The literature review on the reporting quality of XBRL is presented in Appendix 2.

6spreads. They also find that smaller firms, whose information environment may be poor,
are more likely to attract more analysts after they decide to file in the XBRL format.
Efendi et al. (2010) show significant price movement on the date of voluntary XBRL
filings in the U.S. They argue that XBRL conveys incremental value-relevant information
about the market due to ease of comparability and predictability, enhancing the relevance offinancial reports. They also document that smaller investors benefit more due to reduced
costs associated with information processing and analysis from the adoption of XBRL.
While Tan and Shon (2009) and Efendi et al. (2010) examine the effect of voluntary
disclosure, Kim et al. (2012) investigate the impact of mandatory XBRL reporting and findan increase in information efficiency, a decrease in event return volatility, and a reductionof change in stock returns volatility for 425 U.S. mandated filers in the post-XBRL
adoption period.
Yoon et al. (2011) deal with the question of whether the mandatory use of XBRL
may reduce the information asymmetry in the Korean Stock Market. Their results
demonstrate the presence of a significant and negative association between XBRL adoption
and information asymmetry, which implies that the adoption of XBRL may lead to the
reduction of information asymmetry. In addition, the effect of XBRL adoption on reducing
information asymmetry is more prominent for large firms than for small- and medium-sized
firms in Korea.
More recently, however, Blankespoor et al. (2012) examine the initial impact of theuse of XBRL on information asymmetry after the U.S. SEC mandate. They provide
evidence of a higher abnormal bid-ask spread, a reduction in abnormal liquidity, and adecrease in abnormal trading volume for XBRL-adopting firms around 10-K filings. As aconclusion, they claim that the SEC’s objective of improving the information playing fieldmight not have been met during the initial year of XBRL adoption.
2.2 Hypothesis development
As discussed above, XBRL is an internationally standardized computer language
aimed at allowing the efficient compilation, distribution, and usage of financial statements.
It is an open standard that provides a way to model business information and to articulate
its semantic meaning. The adoption of XBRL could enable investors to download financial
data and directly process and analyze the data. In addition, the introduction of XBRL may
not only assist in enhancing the transparency of the securities market but also improve theinformation structure in ways to enhance the efficiency of economic and social activities ingeneral.
However, given the non-trivial implementation and learning costs involved in the
adoption of XBRL, whether the XBRL-based disclosure improves (reduces) the
information environment (asymmetry) is left as an empirical question. We set the null
hypothesis as follows:
H0: The XBRL disclosure does not affect the information environment (asymmetry)7around annual securities filing dates.
As discussed in the previous subsections, prior studies provide mixed results onwhether the use of XBRL helps improve information environment in capital markets. We
believe that investigation of the Japanese case could contribute to the related literature byproviding useful empirical results based on the large number of sample firms that started todisclose financial statements by using the XBRL format all at once. In the following
sections, we investigate the effects of the mandatory XBRL requirement on the information
environment around the annual securities filing by using data on firms listed in the firstsection of the TSE.12

3. Research design and data
3.1 Information environment measures
To examine the impact of the XBRL mandate on the information environment in theJapanese market, we employ several measures that are used in prior studies. Specifically,
we focus on five measures, including event returns volatility (ERV), absolute cumulative
abnormal returns (ACAR), the change in standard deviation of daily stock returns(ΔSTDDEVRET), abnormal bid-ask spread (ASPREAD), and abnormal trading volume(AVOL). A summary of variable definitions is presented in Appendix 3.
The first two measures are calculated by using abnormal returns (ARs) based on theevent study. To estimate ARs, we employ the standard event study methodology based on
MacKinlay (1997). The event here is defined as the day when the firm submits its annual
securities reports. AR is defined as the actual ex post return of the security over the event
window, the period in which the market is considered to be affected by the event, minus the
expected return of the security over the event window, as follows:‫ܴܣ‬௜௧ = ܴ௜௧ − ‫ܴ(ܧ‬௜௧|ܺ௧)(1)

where ARit, Rit, and E(Rit|Xt) are the abnormal, actual, and expected (normal) returns,
respectively, for firm i at period t. Xt is the conditioning information for the expected return.
To calculate the expected return, we estimate the standard market model over theestimation window, the period in which the market is not affected by the event. The market
model assumes a stable linear relation between the return of any given security and thereturn of the market portfolio. Denoting Rmt as the market return, the market model isexpressed as follows:ܴ௜௧ = ߙ௜ + ߚ௜ܴ௠ ௧ + ߝ௜௧
12‫ߝ(ܧ‬௜௧ = 0)(2)

‫ߝ(ݎܽݒ‬௜௧) = ߪఌଶThe TSE has two sections. Larger and more established firms are supposed to be listed in the first section.

8

where εit is the zero mean disturbance term. Here, αi and βi are the parameters of the market
model. We use the Tokyo Stock Price Index (TOPIX) as our market portfolio, because
TOPIX tracks all firms listed on the first section of the TSE. Our estimation window is set
at one year before the annual securities report filing dates, namely, 200 trading days fromday t = -255 to day t = -55 (Figure 1). Our event window is set around the annual securities
filing date from day t= -1 to day t= +1.
(Figure 1 here)
We then calculate the five measures that capture the information environment. The
first measure is ERV. Following prior studies (Baily et al. 2003; Heflin et al. 2003; Francis
et al.; 2006; Kim et al. 2012),13 ERV is defined as an arithmetic sum of the absolute valuesof ARs over the event window, as follows:‫∑ = ܸܴܧ‬ଵ௧ୀିଵ |‫ܴܣ‬௧|.

(4)

Because AR indicates the gap between the full information, post-event stock return and the
expected return based on parameters estimated by using pre-event returns, ERV is also
regarded as the information gap, which measures the value of information that is notreflected in the stock price prior to the filing date. A smaller ERV implies a market
environment with lower information asymmetry.
The second measure is ACAR, which is calculated by the Cartesian product of ARs
in the event window, following Heflin et al. (2003) and Kim et al. (2012), as follows:‫∏| = ܴܣܥܣ‬ଵ௧ୀିଵ[1 + ‫ܴܣ‬௧] − 1|.

(5)

Similar to ERV, ACAR captures the gap between the full information, post-event stock
price and a pre-event price, by taking the absolute deviation between the actual return andthe expected return. A higher ACAR indicates a larger information gap and higher
information asymmetry between informed and uninformed investors. Conversely, a smaller
ACAR demonstrates a smaller deviation, which implies improved information environment.
The third measure is ΔSTDDEVRET. Following Kim et al. (2012), ΔSTDDEVRETis defined as the change in the standard deviation of daily stock returns before and after thefirm’s annual securities report filing dates. The construction of this variable is carried out
by first calculating the standard deviation of returns using daily return data for 30 days
before the annual securities report filing dates followed by calculating the standard
deviation of returns for 30 days after the annual securities report filing dates, as follows:∆ܵܶ‫ = ܶܧܴܸܧܦܦ‬ට

ଵ

ଷ଴

ത ଶ
∑ଷ଴
௧ୀ଴(ܴ௧ − ܴ௧) − ටଵଷ଴∑଴௧ୀିଷ଴(ܴ௧ − ܴത௧)ଶ.

(6)

13 Baily et al. (2003), Heflin et al. (2003), and Francis et al. (2006) investigate the impact of the Regulation

Fair Disclosure (Reg FD) on the information environment. Reg FD prohibits firms from privately disclosing
value-relevant information to professional investors without disclosing the same information to the public at
the same time.

9

ΔSTDDEVRET indicates the frequency of information reaching the market and the extent
of information asymmetry among investors. As a result, we expect a reduction in thechange in the standard deviation if the adoption of XBRL decreases the information
asymmetry.
The fourth measure is ASPREAD, which is defined as the difference between the
average daily bid-ask spread between the event window (post-event period) and theestimation window (pre-event period). Following prior studies (Yoon et al. 2011;
Blankespoor et al. 2012), ASPREAD is expressed as follows:14‫݀ܽ݁ݎ݌ݏݕ݈݅ܽ݀݁݃ܽݎ݁ݒܣ = ܦܣܧܴܲܵܣ‬ா௩௘௡௧௪ ௜௡ௗ௢௪− ‫݀ܽ݁ݎ݌ݏݕ݈݅ܽ݀݁݃ܽݎ݁ݒܣ‬ா௦௧௜௠ ௔௧௜௢௡௪ ௜௡ௗ௢௪ .

(7)

The bid price is quoted by buyers and the ask price is quoted by sellers for a given security.
Information asymmetry increases together with bid-ask spread. Thus, if the XBRL mandate
decreases information asymmetry, we predict a smaller bid-ask spread in the event window
than that in the estimation window. In other words, the adoption of XBRL is likely todecrease ASPREAD.
The last measure is AVOL. Following prior studies (Baily et al. 2003; Francis et al.
2006; Miller 2010; Blankespoor et al. 2012), AVOL is defined as the difference between
the mean daily trading volume during the event window and the mean daily trading volume
during the estimation window, divided by the standard deviation (STDDEV) of daily
trading volume during the estimation window, as follows:‫ܮܱܸܣ‬‫݁ ݉ݑ݈݋ݒݕ݈݅ܽ݀݁݃ܽݎ݁ݒܣ‬ா௩௘௡௧௪ ௜௡ௗ௢௪ − ‫݁ ݉ݑ݈݋ݒݕ݈݅ܽ݀݁݃ܽݎ݁ݒܣ‬ா௦௧௜௠ ௔௧௜௢௡௪ ௜௡ௗ௢௪
=ܵܶ‫݁ ݉ݑ݈݋ݒݕ݈݅ܽ݀ܸܧܦܦ‬ா௦௧௜௠ ௔௧௜௢௡௪ ௜௡ௗ௢௪

(9)Investors are not able to fully process all available information due to limited time andresources. As such, information to be processed is selectively chosen. If the XBRL filings
are relatively less difficult to process, investors will find more incentives to process them,as the costs required may be less than the benefits of obtaining the information. In otherwords, investors should be more willing to trade in such an environment. Thus, if the
adoption of XBRL decreases information processing costs, we expect larger average dailyvolume in the event window than that in the estimation window. In other words, we expect
the increase of AVOL in the post-XBRL period.
3.2 Multivariable regression models
Using variables described above, we estimate the following regression models toassess the effects of XBRL adoption on the information environment in Japan:
14

To calculate the bid-ask spread, we follow Corwin and Schultz (2012) and use a bid-ask estimator
developed from daily high and low prices.

10‫ߙ = ܸܴܧ‬଴ + ߙଵܺ‫ ܮܴܤ‬+ ߙ௜∑௡௞ୀ௜‫ ܵܮܱܴܱܶܰܥ‬+ ߳(10)∆ܵܶ‫ߙ = ܶܧܴܸܧܦܦ‬଴ + ߙଵܺ‫ ܮܴܤ‬+ ߙ௜∑௡௞ୀ௜‫ ܵܮܱܴܱܶܰܥ‬+ ߳(12)‫ߙ = ܮܱܸܣ‬଴ + ߙଵܺ‫ ܮܴܤ‬+ ߙ௜∑௡௞ୀ௜‫ ܵܮܱܴܱܶܰܥ‬+ ߳(14)‫ߙ = ܴܣܥܣ‬଴ + ߙଵܺ‫ ܮܴܤ‬+ ߙ௜∑௡௞ୀ௜‫ ܵܮܱܴܱܶܰܥ‬+ ߳(11)‫ߙ = ܦܣܧܴܲܵܣ‬଴ + ߙଵܺ‫ ܮܴܤ‬+ ߙ௜∑௡௞ୀ௜‫ ܵܮܱܴܱܶܰܥ‬+ ߳(13)where XBRL is a dummy variable, which takes one if the annual securities filing date is in
the post-XBRL period and zero otherwise. CONTROLS presents control variables that arelikely to be associated with the information environment. XBRL is our primary variable ofinterest. If the adoption of XBRL improves the information environment, we expect XBRL
to be negatively correlated with ERV, ACAR, △ STDDEVRET, and ASPREAD, andpositively correlated with AVOL, as explained in the previous sub-section.
CONTROLS are included to ensure that our results are not driven by other firmspecific characteristics that may affect the information environment. Specifically,
CONTROLS include firm size (SIZE), market-to-book ratio (MB), earnings to profit ratio(EPRATIO), leverage (LEV), loss indicator (LOSS), the IT & Services sector (TECH),
percentage of shares held by foreign shareholders (GOV), return volatility for theestimation window (RETVAR), indicator of the negative sign of the cumulative AR(NEGCAR), and cumulative absolute AR for the corresponding quarter (CAAR).
Prior research has shown that there is a positive correlation between a firm’s extent of
disclosure and its size (Ajinkya et al. 2005). We include SIZE, which is the natural log ofmarket capitalization at the end of the corresponding fiscal year. Considering larger firms’
ability to sustain a comparative advantage through superior financial and human resources
available, we expect negative correlation between SIZE and information asymmetry.
MB is defined as the ratio of the firm’s market capitalization to the total equity at theend of the fiscal year. MB is included to capture the perceived potential growth in themarket. We also include EPRATIO of the firm at the end of the fiscal year as a proxy for
expected growth in earnings, as prior research argues that growth expectations increase
stock price responses to earnings (Skinner and Sloan 2002). Because high-growth firms arelikely to have higher information asymmetry, we expect positive correlation between
MB/EPRATIO and information asymmetry.
Firms operating at higher leverage generally have higher financial risks, and theytend to be inclined to disclose more financial information to provide assurance to creditorsand confidence to the public (Kothari et al. 2009). As a result, we include LEV, which isdefined as the ratio of long-term debt divided by total assets at the end of the fiscal year. In
addition, prior research has shown that financial reporting errors are negatively correlatedwith performance and that the presence of a net income loss impedes the market’s ability toaccurately forecast the firm’s earnings (Heflin et al. 2003). To control this negative effect,11

we employ a dummy variable LOSS, which is equal to one if the net income in thecorresponding fiscal year is negative and zero otherwise. Based on the explanations above,
we expect negative (positive) correlation between LEV (LOSS) and information asymmetry.
We also include an industry dummy variable TECH for technology firms, which isequal to one for firms that are classified under IT & Services and zero otherwise.
Technology firms usually exhibit financial statements that are more difficult to interpretdue to financial reporting models that may not properly capture value-relevant information
related to intellectual property and R&D activities. We expect positive correlation between
TECH and information asymmetry. GOV is a percentage of shares held by foreign
shareholders as a proxy for corporate governance. As foreign shareholders are expected tobe more demanding for disclosure in Japan than domestic shareholders, firms with a highforeign shareholders ratio are expected to face less information asymmetry.
The last three variables are related to price variability, which may increase
information asymmetry. RETVAR controls for firm-specific inherent price variability,
which is defined as the standard deviation of the firm’s ARs during the market model
estimation window for the corresponding annual securities filing date. We expect RETVAR
to be positively related to information asymmetry (Beaver 1968). NEGCAR is a dummy
variable, which takes 1 if cumulative ARs during the estimation window are negative and 0
otherwise. NEGCAR controls for another dimension of price variability. Prior studies
suggest that downward price movements are greater than upwards price movements (Heflin
et al. 2003). Lastly, CAAR, which is a cumulative absolute AR around the filing date fromt=-30 to t=+30, is included. CAAR measures the total information flow, and firms withlarger information flow are expected to have larger information gaps at any given time. We
expect this variable to be positively correlated with information asymmetry (Heflin et al.
2003).
3.3 Sample Selection
We download all of the firms listed in the first section of the TSE from the TSE’s
homepage to obtain 1,685 domestic firms as of May 31, 2012. The FSA has its own
EDINET code to uniquely label firms found in its EDINET database. The corresponding
EDINET code is first located from the FSA’s homepage. Then, a list of filing dates for theannual securities reports is downloaded from the Ullet website (http://www.ullet.com)15 forall the firms between January 1, 2007, and June 30, 2009. (This is similar to Form 10-K
required by the U.S. SEC.) Data related to financial statements, such as net income, total
long-term debt, total assets, and total debt are also downloaded from the Ullet website. Datawith respect to stocks, which include high price, low price, closing price, opening price andvolume, are downloaded from Yahoo! Finance Japan. Lastly, the number of shares
outstanding for each firm is obtained from Nikkei NEEDS-Financial Quest.

15

Ullet is a web service by Businesswire (a Berkshire Hathaway company), which organizes financial data of
about 4,000 companies listed on the stock markets in Japan. The information provided by Ullet is compiled
from the Japanese FSA EDINET database.

12

After accounting for incomplete data and matching requirements, we have 2,276
annual securities filing dates from 1,138 firms. These securities filing dates are matched foreach of the 1,138 firms to allow a more accurate comparison between the pre-XBRL andpost-XBRL period (1,138 annual securities filing dates for the pre-XBRL period and thepost-XBRL period). We set the period between January 1, 2008, and June 30, 2008, as the
pre-XBRL period, and the period between January 1, 2009, and June 30, 2009, as the postXBRL period. As most Japanese firms have their fiscal year ending on March 31, themajority of annual securities filing dates of the sample are found in June.
Table 1 shows the number of the firms in our sample broken down by TOPIX Sector
Code. The sample has a large representation from Construction & Materials, Raw Materials& Chemicals, Electrical Appliances & Precision Instruments, and IT & Services, Others.
However, these industries each account for only approximately 10 percent of the total
sample. Overall, the sample is well distributed across several broad industry groups. This
broad representation should alleviate concerns that the results are driven by any specificindustry.
(Table 1 here)
4. Empirical results
4.1 Descriptive statistics
Table 2 presents the descriptive statistics of the sample firms’ characteristics before
and after the adoption of XBRL. This table provides the median, mean, and t-stat meandifferences of the five measures for the information environment (ERV, ACAR, △STDDEVRET, ASPREAD, and AVOL) and CONTROLS. If the adoption of XBRL allows
users to compare XBRL-tagged data across various organizations more effectively than
before, we predict an improvement in the information environment through the reduction of
information asymmetry.
(Table 2 here)
Among five measures, we find a significant decrease in ΔSTDDEVRET and ASPREAD
after the adoption of XBRL. These results are consistent with the notion that the
introduction of XBRL tends to decrease information asymmetry, although we find no
significant difference in ERV, ACAR, or AVOL.
Among the control variables, SIZE, MB, EPRATIO, LEV, LOSS, GOV, RETVAR,
and CAAR are significantly different between the pre-XBRL and post-XBRL periods at the
1 percent level. We note that the means of the variables related to the financial health of thefirms deteriorate in the post-XBRL period, as can be seen from the lower EPRATIO, andhigher LEV and LOSS. Similarly, SIZE, MB, and GOV decrease in the post-XBRL period
and differ at the 1 percent significance level. The deterioration of financial health andgovernance of firms may be partially attributed to the 2008 financial crisis, which had ahuge and negative impact on firms and the stock markets. Lastly, RETVAR and CAAR
increase in the post-XBRL period and differ at the 1 percent significance level.
13

Table 3 provides the Pearson correlation matrix among the variables used in the
regression. The highest correlation among the independent variables is 0.622, which is thecorrelation between SIZE and GOV. The second highest correlation is 0.525, which is thecorrelation between XBRL and RETVAR. Despite the relatively high correlations in these
two areas, the rest of the correlations among the independent variables are below 0.5. The
Variance Inflation Factor (VIF) is also calculated to confirm the absence ofmulticollinearity (Table 4).16 A general guide is that multicollinearity is high when VIF > 5.
As all the calculated VIF values are below 2, the absence of multicollinearity could be
confirmed.
(Tables 3 and 4 here)
4.2 Regression results
Table 5 shows the multivariate regression results for the effects of XBRL on theinformation environment in the Japanese market. Models 1 to 5 use ERV, ACAR,ΔSTDDEVRET, ASPREAD, and AVOL as dependent variables, respectively, based onequations (10) to (14). For Models 1 to 4, the coefficients of XBRL are significantly
negative at the 1 percent level, although the coefficient of XBRL is not significantly
different from zero for Model 5. The significantly negative correlation between XBRL andthe four measures (ERV, ACAR, ΔSTDDEVRET, and ASPREAD) are consistent with thenotion that XBRL adoption improves the information environment by reducing information
asymmetry.
(Table 5 here)
Our result for Models 1 to 4 shows similar benefits from the adoption of XBRL as
those reported in prior studies, including those by Tan and Shon (2009), Yoon et al. (2011),
and Kim et al. (2012). However, our result for Model 5 supports neither the notion thatinvestors have incentives to trade more actively in an XBRL environment, nor the results ofBlankespoor et al. (2012), which show a decrease of AVOL after the adoption of XBRL. 17
The results for control variables are mixed, however. First, the coefficients of SIZE
are significantly negative for Models 1 and 4, significantly positive for Model 3, andinsignificant for Models 2 and 5. In other words, our results are mixed with regard to the16This factor is calculated with a three-step process. The first step involves running an ordinary least
regression on each control variable as a function of all other explanatory variables to obtain a coefficient ofdetermination of the regression equation. The second step involves calculating the VIF factor for each control
ଵvariable using the formula ܸ‫=ܨܫ‬. The final step is to analyze the magnitude of multicollinearity byଵିோ మ
considering the size of the VIF values.
17 We also note that the adoption of XBRL brings about benefits similar to those of the Regulation Fair
Disclosure (Reg FD) mandate enacted in the U.S. in 2000. Our results show that the XBRL mandate in Japan
helps to reduce ACAR and ERV in the post-XBRL period, as seen from the negative coefficient of XBRL in
both regressions. This is consistent with the results for the impact of the mandate of Reg FD (Heflin et al.
2003; Bailey et al. 2003).

14

size effect, because the results for Models 1 and 4 are consistent with our prediction thatlarger firms tend to have lower information asymmetry, while those for Model 3 are not.
As for the effect of potential growth on information asymmetry, the results for MB
are also mixed, with significantly positive coefficient for Model 1, a significantly negative
coefficient for Model 3, and insignificant coefficients for Models 2, 4, and 5. The result for
Model 1 is consistent with our prediction, while those of other results are not. In addition,
the coefficients of EPRATIO are not different from zero for any of the models.
The results for LEV are mixed, with significantly positive coefficients for Models 1
and 2, significantly negative coefficient for Model 3, and insignificant coefficients for
Models 4 and 5. The result for Model 3 is consistent with our expectations, while the otherresults are not consistent with our expectations. Likewise, the coefficient of LOSS issignificantly positive for Model 1, while it is insignificant for the other four models.
With regard to the industry effect, the coefficient of TECH is significantly negative
only for Model 4, which is not consistent with our prediction. Regarding the effect of theshareholder composition, the coefficient of GOV is significantly negative only for Model 5,
which is not consistent with our prediction, while it is insignificant for the other fourmodels.
The remaining variables also provide mixed results: The coefficients of RETVAR are
significantly positive for Models 1 and 2, insignificant for Model 3, and significantly
negative for Models 4 and 5; the coefficients of ACAR are significantly negative forModels 1, 2, and 4, and insignificant for Models 3 and 5; and the coefficients of CAAR are
significantly positive for Models 1, 2, 4, and 5, and insignificant for Model 3.
Although we do not obtain consistent results for control variables, all models in our
regression analysis show the reduction of information asymmetry and then improvement of
the information environment in the Japanese market. In the next section, we conduct two
sensitivity analyses to guarantee robustness of our main results regarding measures of thefinancial information environment.

5. Sensitivity analysis
In this last section, we conduct two sensitivity analyses. We note that the stock
market was very volatile for the period including the implementation of XBRL because of
the financial crisis in 2008. The inclusion of firms in the financial industry may bias our
results, because those firms are likely to have suffered a great deal during our test period.
We rerun the regression of all the models in Table 6 after eliminating Banks and Financial
Industry firms (codes 15 and 16 in Table 1). After eliminating these firms, our results are
qualitatively similar to those in Table 5. Our main coefficients of XBRL remain negativeand significant at the 1 percent level for Models 1 to 4.
(Table 6 here)15

Second, we try different periods for comparison. Specifically, we collect data for 2
years prior to the adoption of XBRL by the FSA. We use the same research design to carryout the multiple regression analyses to verify whether the results are the same. In thiscontext, we use January 1, 2007, to June 30, 2007, as the pre-XBRL period and January 1,
2008, to June 30, 2008, as the post-XBRL period. Table 7 shows the results of the multiple
regression analysis. The coefficients of XBRL are significantly positive for Models 2 and 4,
insignificant for Models 1 and 5, and significantly negative for Model 3. These results arecontrary to our original results in Table 5, which indicates that XBRL adoption reduces
information asymmetry. Thus, we can conclude that no consistent market-wide effects areobserved that help to reduce information asymmetry in the TSE during this different period.
This sensitivity analysis supports the notion that the improvement of the information
environment can be attributed to the adoption of XBRL.
(Table 7 here)
6. Concluding remarks
In 2008, the Japanese FSA mandated that firms be required to submit their annual
securities reports to the EDINET in the XBRL format. The FSA believes that this new
search-facilitating technology will enhance the transparency and quality of business
reporting, and improve the information environment in the Japanese market. However,given the non-trivial implementation and learning costs involved in the adoption of XBRL,
whether the XBRL-based disclosure improves the information environment is basically anempirical question. In the present study, we investigate the effects of the mandatory XBRL
requirement on the information environment around the annual securities filing by using
data on firms listed in the first section of the TSE.
Our regression results provide evidence consistent with the notion that XBRL
adoption improves the information environment in the Japanese market. This can be seenfrom the highly significant and negative associations between XBRL adoption and event
returns volatility, absolute cumulative abnormal returns, changes in the standard deviationof returns, and abnormal bid-ask spread after controlling for firm-specific characteristics.
Our findings suggest that XBRL disclosures have the potential to reduce information
asymmetry through improved accessibility and transparency. These findings are robust to asensitivity analysis excluding financial firms.

16

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20

Appendix 1: Japan setting
Japan was an early and active adopter of the XBRL. One of the earliest adopters of
XBRL was the NTA, which announced an e-Tax filing system using XBRL 2.0 as the filingformat in 2003 and began accepting XBRL 2.1 filings in 2008 (Sakuta 2008). The
introduction of XBRL by the NTA also allowed unlisted companies to utilize XBRL. For
example, small and medium-sized companies can reuse their XBRL-based financial
statements, which they used for tax filings online to take loans from banks. In February
2006, the Financial Systems and Bank Examination Department of the BOJ developed a
new data transfer scheme based upon the latest XBRL technology, Formula Link, toimprove the efficiency of gathering data from financial institutions (Wada 2008).
According to the BOJ, the introduction of XBRL for monthly balance sheet information has
led to a significant reduction in the burden of data validation and other gains in efficiency.
In July 2008, the TSE introduced the third generation TDnet server for XBRL productionuse, at which time provisions of financial data in the XBRL format commenced. Earnings
reports and corporate governance reports were submitted in the XBRL format under thetimely disclosure policy, which was applicable to about 4,000 listed companies at stock
exchanges in Japan (Yoshida 2008).
The FSA has an EDINET system, which started its operations in June 2001. The
EDINET is an electronic corporate disclosure system under the Financial Instruments andExchange Act, and all listed companies and investment funds in Japan are required to filetheir disclosure documents using the system. These submitted corporate disclosure
documents are prepared in HTML format and publicly available on the Internet through
EDINET. Within this system, a series of procedures ranging from submission, receipt, andpublic viewing of annual securities reports (yukashoken houkokusho in Japanese) and otherdisclosure documents are carried out online. These procedures were implemented on paper
prior to the introduction of the EDINET system. Thus, EDINET serves as a platform for
information disclosure, enabling the prompt disclosure of critical information for investors
and issuers, and allowing the general public to instantly access corporate information thathas been submitted (Takeda 2008).
On March 17, 2008, the FSA launched a new EDINET system in preparation for the
adoption of XBRL. All filers are mandated to submit the financial statements included intheir annual securities reports, semiannual securities reports, quarterly securities reports andsecurities registration statements for fiscal years starting in or after April 2008 in the XBRL
format. Quarterly securities reports for the first fiscal quarter ending in June 2008
comprised the first filing in the XBRL format. In other words, financial statements in the
XBRL format can be retrieved from the EDINET as of the end of June, 2008.18 These
financial statements in the XBRL format are to be prepared using “EDINET Taxonomy”(JP-GAAP taxonomy). Since autumn 2009, the FSA has been conducting research and
18 Precisely speaking, financial statements in the XBRL format became available at the EDINET in June 29,

2008. The number of the filers in June, 2008 was nine.

21

testing to expand the XBRL’s scope of application and upgrade EDINET’s functionality.
EDINET is currently scheduled to be upgraded in fiscal 2013.
Appendix 2: Literature review on the reporting quality of XBRLThe reporting quality of XBRL-based financial reports is vital for its success. It is
expected that human errors will be minimized as XBRL reduces the need for repetitive data
entry (Morgan 2009), which will help to improve both data migration efficiency andaccuracy. However, the literature provides evidence that the XBRL reporting still contains
deficiencies and incompleteness (Zhu and Fu 2009; Debreceny et al. 2010; Bartley et al.
2011; Roohani and Zhen 2011).
Zhu and Fu (2009) develop metrics to empirically evaluate financial reports createdusing the XBRL data standards. The use of standard elements and company-specificelements is found to vary substantially across companies. An average company uses 128
elements from the XBRL standard, which defines about 2,000 elements, and introduces 64
elements of its own. Completeness and relevancy of a data standard affect theinteroperability of data created. However, they show that the XBRL standard has low
completeness and relevancy from an individual company’s perspective. To the averagecompany, the completeness of the XBRL standard is 66.67 percent, and the relevancy ofthe standard is 6.4 percent.
Debreceny et al. (2010) study one aspect of data quality errors in the computation ofrelated monetary facts in the XBRL filings. Out of 400 filers, they discover one quarter
have computational errors, half of which are due to inappropriate treatment in the instance
documents of the underlying debit/credit assumptions in the taxonomy. An additional
quarter of the errors are due to missing or extraneous values in a calculation relationship.
Bartley et al. (2011) also find that all 22 companies made errors during the first year ofvoluntary XBRL filings by examining 22 companies' initial voluntary XBRL 10-K filings.
To find determinants of the deficiency of XBRL mandatory filings, Roohani and Zheng(2011) investigate all the SEC filings from July 2009 to December 2010. They conclude
that XBRL deficient filings tend to have a higher percentage of extensions and are filed bybigger and more complex firms.19 In addition, firms that have considerable experience in
XBRL filings are less likely to have major errors. On the contrary, they are more likely tohave minor errors.
The reporting quality of XBRL is also related to the issue of whether XBRL
taxonomies, which have a crucial role in digital financial reporting by providing a unique
set of computer-readable tags to allow the interchange of interactive information, areconsistent with the reporting practices (Bonson et al. 2009; Valentinetti and Rea 2011).
19

Sakaue (2011) investigates the XBRL data of early adopters of International Financial Reporting Standards(IFRS) taxonomy in Japan and finds that company-specific extended items accounts for more than 60% of thewhole in financial statements.

22

Bonson et al. (2009) argue that multiple XBRL taxonomies, based on different accounting
principles, contradict the objectives of standardization, comparability, and reusability of the
XBRL information. They evaluate the effectiveness of the IFRS-GP taxonomy that could
establish a common ground for international firms and create a platform that would
enhance the benefits of XBRL. Valentinetti and Rea (2011) also attempt to verify whether
the IFRS taxonomy released by the IFRS Foundation adequately reflects the reporting
practices of Italian listed companies. The results reveal a general discrepancy between thefinancial items disclosed by the firms and the taxonomy tags. Specifically, they find that thefinancial statements items are more disaggregated than the taxonomy tags and as such aloss of detailed information may occur if the taxonomy is applied.

23

Appendix 3: Variable definitions
ERV
Arithmetic sum of the absolute values of ARs over the event windowଵ

ACAR

ΔSTDDEVRETASPREAD

AVOL

XBRL
SIZE
MB
EPRATIO
LEV
LOSS
TECH
GOV
RETVAR
NEGCAR
CAAR

෍

௧ୀିଵ

|‫ܴܣ‬௧|Cartesian product of ARs in the event window: | ∏ଵ௧ୀିଵ[1 + ‫ܴܣ‬௧] − 1|
Change in the standard deviation of daily stock returns before and after
the firm’s annual securities report filing dates
ଷ଴

଴

௧ୀ଴

௧ୀିଷ଴

1
1
ඩ ෍ (ܴ௧ − ܴത௧)ଶ − ඩ෍3030(ܴ௧ − ܴത௧)ଶ

Difference between the average daily spread during the event window
and the average daily spread during the estimation window‫݀ܽ݁ݎ݌ݏ ݕ݈݅ܽ݀݁݃ܽݎ݁ݒܣ‬ா௩௘௡௧௪ ௜௡ௗ௢௪− ‫݀ܽ݁ݎ݌ݏ ݕ݈݅ܽ݀݁݃ܽݎ݁ݒܣ‬ா௦௧௜௠ ௔௧௜௢௡ ௪ ௜௡ௗ௢௪

Difference between the mean daily trading volume during the event
window and the mean daily trading volume during the estimation
window, divided by the standard deviation of the daily trading volume
during the estimation window‫݁ ݉ݑ݈݋ݒݕ݈݅ܽ݀݁݃ܽݎ݁ݒܣ‬ா௩௘௡௧௪ ௜௡ௗ௢௪−‫݁ ݉ݑ݈݋ݒ ݕ݈݅ܽ݀݁݃ܽݎ݁ݒܣ‬ா௦௧௜௠ ௔௧௜௢௡ ௪ ௜௡ௗ௢௪
ܵܶ‫݁ ݉ݑ݈݋ݒ ݕ݈݅ܽ݀ ܸܧܦܦ‬ா௦௧௜௠ ௔௧௜௢௡ ௪ ௜௡ௗ௢௪
Dummy variable that is equal to 1 in the post-XBRL period and 0
otherwise
Natural log of market capitalization
Market to book ratio
Earnings to price ratio
Long-term debt divided by total assets
Dummy variable that is equal to 1 if the fiscal year’s net income isnegative and 0 otherwise.
Dummy variable that is equal to 1 for firms that are classified under IT& Services and 0 otherwise
Percentage of shares held by foreign shareholders
Standard deviation of the firm’s ARs during the estimation window
Dummy variable that is 1 if cumulative ARs during the estimation
window are negative and 0 otherwise
Cumulative absolute AR around the filing date from t=-30 to t=+30

24

Figure 1: Timeline of the event study(a) Pre-XBRL period (January 1, 2008 – June 30, 2008)

Estimation Windowt=-255

Event Window

t=-55

t=-1 t=0 t=+1(b) Post-XBRL period (January 1, 2009 – June 30, 2009)
Estimation Windowt=-255

Event Window

t=-55

t=-1 t=0 t=+1

Note: t = 0 is the event date when the firm’s annual securities report is filed in EDINET.

25

Table 1: Distribution of sample by TOPIX-17 series sector code
Code
Category
No.
%
1
Foods
49
4.31
2
Energy Resources
10
0.88
3
Construction & Materials
111
9.75
4
Raw Materials & Chemicals
123
10.81
5
Pharmaceutical
20
1.76
6
Automobiles & Transportation Equipment
53
4.66
7
Steel & Nonferrous Metals
50
4.39
8
Machinery87
7.64
9Electric Appliances & Precision Instruments
124
10.9
10
IT & Services, Others
157
13.8
11
Electric Power & Gas
11
0.97
12
Transportation & Logistics
47
4.13
13
Commercial & Wholesale Trade
91
8.00
14
Retail Trade
95
8.35
15
Banks
57
5.01
16
Financials excluding Banks
24
2.11
17
Real Estate
29
2.55
Total
1,138
100.00
Table 2: Descriptive statistics
ERV
ACAR
ΔSTDEVRETASPREAD
AVOL
XBRL
SIZE
MB
EPRATIO
LEV
LOSS
TECH
GOV
RETVAR
NEGCARCAAR

Pre-XBRL (n=1,138)
Median
Mean
S.D.
0.036
0.043
0.033
0.018
0.025
0.025
0.000
0.000
0.009
-0.0880.059
0.882-0.392
0.392
9.418
0.000
24.504
1.038
0.058
0.130
0.000
0.000
0.108
0.025
0.000
0.100

0.000
24.721
1.306
0.042
0.160
0.093
0.138
0.139
0.026
0.435
0.137

Post-XBRL (n=1,138)
Median
Mean
S.D.
0.037
0.044
0.031
0.018
0.024
0.024
-0.002-0.0030.010-0.410-0.3200.554-0.292
0.115
2.920

0.000
1.489
1.107
0.137
0.136
0.291
0.345
0.118
0.008
0.496
0.129

1.000
24.274
0.881
0.028
0.140
0.000
0.000
0.084
0.037
0.000
0.122

Note: *** indicates significance at the 1 % level.

26

1.000
24.4511.088-0.051
0.176
0.335
0.138
0.117
0.037
0.437
0.160

0.000
1.474
0.952
0.304
0.146
0.472
0.345
0.109
0.010
0.496
0.157

Post - Pre (Mean)t-value
0.630-0.333-6.264 ***-12.267 ***-0.947-4.353 ***-5.031 ***-9.462 ***2.628 ***
14.705 ***-4.625 ***30.118 ***0.085
3.751 ***

Table 3: Pearson correlation matrix for explanatory variables
XBRL
SIZE
MB
EPRATIO
LEV
1.000
SIZE
MB
EPRATIO
LEV
LOSS
TECH
GOV
RETVAR
NEGCARCAAR-0.091-0.105-0.195
0.055
0.295
0.000
-0.097
0.525
0.002
0.079

0.237
0.155
0.148
-0.174-0.0770.622-0.0230.073-0.1740.062
0.058-0.110
0.183
0.144
0.102
0.050
0.067

-0.085-0.5480.034
0.071-0.295-0.022-0.1690.103-0.063-0.0150.126
0.085-0.008LOSS-0.044-0.120
0.299
0.008
0.142

Table 4: Correlation with dependent variables
ERV
ACAR
ΔSTDDEVRET ASPREAD
AVOL
XBRL
0.013
-0.007-0.128-0.249-0.020SIZE-0.095-0.0550.136-0.060-0.062
MB
0.133
0.062
-0.0270.006-0.004EPRATIO-0.121-0.0970.088
0.011-0.030
LEV
0.047
0.054
-0.037-0.034
0.038
LOSS
0.142
0.075
-0.107-0.023
0.071
TECH
0.042
0.008
-0.044-0.0340.013
GOV-0.0100.001
0.109-0.037-0.053RETVAR
0.251
0.163-0.082-0.179-0.004
NEGCAR-0.096-0.090-0.024-0.053-0.032
CAAR
0.281
0.256
-0.1430.093
0.140
Table 5: Regression results for the information environment around filing dates

27

TECH

GOV

-0.012-0.069-0.056
0.012

VIF
1.130
1.296
1.025
1.121
1.006
1.144
1.005
1.220
1.200
1.001
1.011

0.074
0.036
-0.052RETVAR NEGCAR-0.021
0.234

0.104

Model 1 (ERV)
Coefficientt-stat-0.010-6.435 ***-0.002-3.259 ***0.003
2.150 ***
0.001
0.360
0.008
1.775 *
0.007
2.893 ***
0.003
1.524
0.005
0.703
0.779
10.673 ***-0.008-6.504 ***0.044
5.312 ***

XBRL
SIZE
MB
EPRATIO
LEV
LOSS
TECH
GOV
RETVAR
NEGCARCAAR
F-test40.984 ***2Adjusted R
No. of Obs.

Model 2 (ACAR)
Coefficientt-stat-0.006-5.123 ***-0.001-1.2590.001
0.642-0.003-1.0500.009
2.179 **
0.001
0.455
0.001
0.324
0.003
0.468
0.377
6.582 ***
-0.005-5.621 ***0.036
5.788 ***
23.165 ***Model 3 (ΔSTDDEVRET)
Coefficientt-stat-0.002-4.545 ***0.001
3.444 ***
-0.001-1.807 *
0.001
0.549
-0.003-1.792 *-0.001-1.482-0.001-1.311
0.003
1.152
0.030
1.162
-0.001-1.568-0.007-2.358 **12.126 ***Model 4 (ASPREAD)
Coefficientt-stat-0.334-8.94 ***
-0.027-1.937 *0.012
0.54-0.043-0.644-0.035-0.3360.076
1.513-0.110-2.365 **-0.048-0.281-7.512-4.178 ***-0.059-1.957 *0.616
4.604 ***
20.665 ***Model 5 (AVOL)
Coefficientt-stat-0.533-1.196-0.093-1.643-0.017-0.244
0.746
1.267
2.212
1.458
1.284
1.616
0.232
0.537
-1.555-2.037 **-20.416-2.610 ***-0.367-1.3686.738
2.761 ***
6.412 ***

0.162

0.097

0.051

0.087

0.026

2,276

2,276

2,276

2,276

2,276

Notes:
1: The regression is conducted by using White heteroskedasticity-consistent standard errors & covariance2: *, **, and *** indicates significance at the 10%, 5%, and 1% levels, respectively.

28

Table 6: Regression results for the information environment around filing dates (without firms in financial industry)
Model 1 (ERV)
Coefficientt-stat-0.011-6.560 ***-0.001-1.801 *0.003
1.940 *
0.002
0.446
0.004
0.848
0.008
3.152 ***
0.003
1.367
-0.002-0.2120.796
10.565 ***-0.008-6.289 ***0.045
5.255 ***

XBRL
SIZE
MB
EPRATIO
LEV
LOSS
TECH
GOV
RETVAR
NEGCARCAAR
F-test2

Adjusted R
No. of Obs.

37.984 ***
0.161
2,114

Model 2 (ACAR)
Coefficientt-stat-0.006-5.005 ***0.000-0.2290.001
0.619-0.003-1.126
0.006
1.313
0.001
0.787
0.000
0.191
-0.001-0.1720.374
6.345 ***
-0.005-5.472 ***0.037
5.746 ***
21.676 ***
0.097
2,114

Model 3 (ΔSTDDEVRET)
Model 4 (ASPREAD)
Coefficientt-statCoefficientt-stat-0.002-4.034 ***-0.333-8.610 ***0.001
3.757 ***
-0.014-0.947-0.001-1.760 *
0.005
0.237
0.001
0.677
-0.039-0.559-0.004-2.227 **-0.164-1.426-0.001-1.143 ***0.106
1.967 **
-0.001-1.439-0.121-2.572 **
0.001
0.366
-0.145-0.7750.034
1.279-7.804-4.220 ***-0.001-1.603-0.056-1.777 *-0.008-2.315 **0.660
4.805 ***
10.787 ***
0.048
2,114

0.089 ***
19.812
2,114

Notes
1: The regression is conducted by using White heteroskedasticity-consistent standard errors & covariance.
2: *, **, and *** indicates significance at the 10%, 5%, and 1% levels, respectively.

29

Model 5 (AVOL)
Coefficientt-stat-0.533-1.146-0.091-1.300-0.016-0.220
0.857
1.343
2.299
1.336
1.395
1.649 *
0.227
0.525
-1.655-2.221 **-21.628-2.576 **-0.361-1.2796.941
2.749 ***
6.096 ***
0.026
2,114

Table 7: Regression results for the information environment around filing dates based on alternative pre- and post-XBRL
periods
Model 1 (ERV)
Coefficientt-stat0.003
1.430-0.001-0.9370.003
2.123 **
0.000
-1.023
0.006
1.027
0.006
1.484
-0.002-0.720-0.009-1.1430.885
6.365 ***
-0.004-3.118 ***0.071
5.405 ***

XBRL
SIZE
MB
EPRATIO
LEV
LOSS
TECH
GOV
RETVAR
NEGCARCAAR
F-test2

Adjusted R
No. of Obs.

40.549 ***
0.206
1,678

Model 2 (ACAR)
Coefficientt-stat0.004
1.943 *
0.000
0.038
0.002
1.552
0.000
0.360
0.008
1.540
0.003
1.026
-0.001-0.5560.000-0.0260.389
3.372 ***
-0.004-3.541 ***0.055
4.336 ***

Model 3 (ΔSTDDEVRET)
Coefficientt-stat-0.002-4.054 ***0.001
5.744 ***
-0.001-2.524 **0.000-1.650 *-0.003-1.506-0.002-2.354 **-0.001-1.456
0.002
0.841
0.090
2.031 **
-0.001-1.120-0.001-0.14126.307 ***
0.142
1,678

Model 4 (ASPREAD)
Coefficientt-stat0.291
6.100 ***
-0.028-1.527
0.005
0.182
0.000
0.143
0.134
0.913
0.161
1.615
-0.095-1.6150.044
0.193-10.653-3.402 ***-0.035-0.8980.983
4.542 ***

8.978 ***
0.050
1,678

Notes
1: The regression is conducted by using White heteroskedasticity-consistent standard errors & covariance.
2: *, **, and *** indicates statistical significance at the 10%, 5%, and 1% levels, respectively.

30

9.148 ***
0.051
1,678

Model 5 (AVOL)
Coefficientt-stat-0.091-0.530
0.033
0.935
0.009
0.218
0.000
-1.286
0.481
1.447
0.895
0.985
-0.359-2.644 ***-1.372-2.246 **-29.159-1.798 *-0.207-1.541
6.158
2.138 **
8.682 ***
0.048
1,678