The Impact of XBRL Adoption on the
Information Environment: Evidence from Japan*
ZHENYANG BAIMANABU SAKAUEFUMIKO TAKEDA†

Department of Technology
Management for Innovation
Graduate School of Engineering,
THE UNIVERSITY OF TOKYO

Faculty of Business Administration,
HOSEI UNIVERSITY

Department of Technology
Management for Innovation
Graduate School of Engineering,
THE UNIVERSITY OF TOKYOABSTRACTThis article investigates whether and how the Japanese Financial Services Agency’s
mandatory use of XBRL affects investors in assessing financial information. Although
regulators expect the introduction of XBRL to enhance the transparency and quality ofbusiness reporting, given the non-trivial implementation and learning costs, whether
the XBRL-based disclosure reduces the information asymmetry between sophisticatedand less-sophisticated investors remains an empirical question. As prior studies inother countries employing mandatory XBRL adoption provide mixed results, ouranalysis of the Japanese case should be somewhat 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 pre- and post-XBRL periods, we
provide evidence consistent with the notion that the adoption of XBRL has helped to
improve the information environment, as indicated by the reduction of event returns
volatility, absolute cumulative abnormal returns, changes in the standard deviation of
returns, and the abnormal bid-ask spread.
JEL Classificatoin: M41; M48; D82; K22; G14
Key Words: XBRL; Information Asymmetry; Capital Market; Event Study
Data Availability: All data used in this study is publicly available.

* We would like to thank the anonymous reviewers and participants at the 2013 Annual Congress of the European
Accounting Association, the 2013 Annual Conference of the Asian Academic Accounting Association, and the 4th
The Japanese Accounting Review Conference for their useful comments and suggestions. All remaining errors are ourown.
† Corresponding author. Address 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 Japan. Telephone/fax +81-3-5841-1191
E-mail takeda@tmi.t.u-tokyo.ac.jp
Received August 7, 2013; accepted June 24, 2014; available Online September 11, 2014 (Advance publication by J-STAGE)
DOI: 10.11640/tjar.4.2014.03

Copyright©2014 Research Institute for Economics & Business Administration - Kobe University.

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The Japanese Accounting Review, 4 (2014), 49-74

1. Introduction
The eXtensible Business Reporting Language (XBRL) is undergoing a rapid transition fromthe vision phase to becoming a practical global standard for financial and business reporting.
XBRL is intended to allow the efficient compilation, distribution, and usage of financial
statements. It is an open standard that provides a way to model business information and toarticulate its semantic meaning.1 Internationally, the XBRL data standard is a platform foraddressing the problem of data integrity, timeliness and reusability. 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 reporttheir financial statement data using XBRL with the goal of using XBRL as a binding andexclusive 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 theeffects of the adoption and diffusion of XBRL on capital markets is one of the more emphasizedareas. Several studies provide evidence that the introduction of XBRL reduces information
asymmetry in both voluntary and mandatory XBRL reporting (Tan and Shon 2009; Efendi et al.
2010; Yoon et al. 2011; Kim et al. 2012), while more recent research provides contradicting
results in terms of mandatory adoption (Blankespoor et al. 2014).
Because prior studies provide mixed results, the question of whether the use of XBRL helps
to improve the information environment in capital markets remains an empirical question. The
present study aims to contribute to related literature by examining Japanese data. We believe thatinvestigation of the Japanese case should provide useful empirical results in two ways. First, Japan
employed mandatory XBRL adoption, which is more controversial than voluntary adoption, asprior studies provide mixed results on the effect of mandatory adoption. It should be more
important to investigate the effect of mandatory XBRL adoption compared to the effect ofvoluntary adoption.
Second, 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).4 Compared to1

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 a static format” (SEC2009).
2
China was the first capital market to adopt XBRL. In 2004, the China Securities Regulatory Commission, theShanghai Stock Exchange and the Shenzhen Stock Exchange mandated all publicly traded firms to file their financial
statement using XBRL as part of their effort to increase the overall attractiveness of the Chinese 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 thetaxonomies in the first project and 1,223 firms voluntarily participated in the second project. The XBRL-formatted
financial data under the pilot projects were disclosed temporarily from August 13 to 31, 2007, although it isquestionable how many investors noticed this disclosure.
It is true that while the phased adoption (such as in the U.S.) enables researchers to have a control sample that does
not employ XBRL, we cannot have such a control sample because all listed firms in Japan were forced to employ

Bai, Sakaue, and Takeda: The Impact of XBRL Adoption on the Information Environment

51

Japan, research on other countries covers a relatively small number of observations. For instance,
the U.S. SEC required large accelerated filers (approximately 500 firms) 5 to disclose theirfinancial information in the XBRL format in 2009, although more than 10,000 listed firms wererequired to do so after December 2011. The prior study of the Korean implementation of XBRL
investigates 550 listed firms (Yoon et al. 2011), while the study of the Chinese case covers
approximately 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 wasestablished 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), and the Financial Services Agency(FSA).
The FSA has an Electronic Disclosure for Investors’ Network (EDINET) system, and all
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 adoption of XBRLin 2008. All filers are mandated to submit the financial statements for fiscal years starting in orafter 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
Accounting 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, the introductionof 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 efficiency of economic activities
in general.
The purpose of this paper is to examine whether and how the FSA’s mandatory financial
reporting using XBRL has affected investors in assessing financial information. Specifically, weinvestigate the effects of using XBRL on the information environment in the Japanese market byassessing various measures in the pre- and post-XBRL periods. Our results are consistent with
the notion that the adoption of XBRL helps to improve the information environment in theJapanese market, as shown by the reduction of event returns volatility, absolute cumulative
abnormal return, change in the standard deviation of return, and abnormal bid-ask spread.7
XBRL all at once. However, the phrased adoption may cause difficulty in isolating the effects of the XBRL adoption
from the effects of other economic events occurring at different times, while the one-time adoption requires us toconsider 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
A detailed description on the XBRL adoption in Japan is provided in Appendix 1.
7
One may argue that the timing of XBRL adoption was so close to the world financial crisis in 2008 that it may makeit difficult to isolate the effects of XBRL from the effects of the world financial crisis. However, we can reasonably
assume that the financial crisis may increase uncertainty and information asymmetry, while XBRL adoption is
expected to reduce information asymmetry, which is consistent with our results. We also try to eliminate the effect of
the financial crisis in our sensitivity analysis. Another event that may have affected stock prices in the sample period isa change in financial reporting systems, including the beginning of quarterly reporting. However, prior studies reportthat this did not affect stock markets (Kagaya et al. 2011).

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The Japanese Accounting Review, 4 (2014), 49-74

The rest of this article is organized as follows. Section 2 provides a literature review andhypothesis 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 the conclusions 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 thelast decade, with a significant increase in relevant publications in recent years.8 According toMike Starr from the U.S. SEC, the main benefits of using XBRL are more timely access andgreater transparency for investors and enhanced efficiency in analyzing the data by regulators,
both of which will result in a more effective regulatory process (Starr 2012). Both the market andcompanies 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 are expected to reduce eachparticipating firm’s cost of capital (Pinsker and Li 2008).10
One drawback is that the large setup and learning costs of implementing XBRL reporting
may deter both filers and investors from using XBRL. Because of its complicated technology, theintroduction of XBRL may involve many difficulties, resulting in an increase in preparation costs.
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 (Dzinkowski 2008). In addition, prior studies cast doubt on
the reporting quality of XBRL, as errors or the incompleteness of financial reporting are stillobserved (Zhu and Fu 2009; Debreceny et al. 2010; Bartley et al. 2011; Roohani and Zheng2011).11
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 theincrease in XBRL publications in recent years (Roohani et al. 2010).
9
The increased transparency through 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 XBRL adoption in
China. Their results indicate a lower level of total accruals in the post-XBRL period, with this effect mostprominent for high-growth firms, small firms, and firms in high-technology industries. They argue that XBRL
implementation decreases an investor’s information acquisition costs and improves an investor’s ability 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 non-adopting peers. The
results of their study suggest that early and voluntary XBRL adoptions are indeed indicators of superior corporatetransparency 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 the bankruptcy ofcompanies 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 power increases as the bankruptcy filing dateapproaches.
11
The literature review on the reporting quality of XBRL is presented in Appendix 2.

Bai, Sakaue, and Takeda: The Impact of XBRL Adoption on the Information Environment

53

These shortcomings raise the issue of whether the information environment (asymmetry) is
improved (reduced) by the introduction of XBRL. One of the important factors 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, a unique aspect of the XBRL mandateis that it requires investors to incur significant learning costs to achieve the intended benefits.
Thus, those that perceive the costs to outweigh the benefits may decide to forego or delay the useof XBRL. For example, investors with relatively 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 asubset of investors will choose to use XBRL in the initial year of mandate.
Two prior studies examine whether the use of XBRL can help investors to acquire 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 XBRLenhanced search engine helps nonprofessional financial statement users acquire and integrate
related financial information when making an investment decision. Their results suggest thatXBRL should be beneficial for the users by improving the transparency of firms’ financial
statement information, although nonprofessional users may not automatically use XBRL, as halfof 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 of analytical effectiveness andefficiency in utilizing XBRL-based information compared to paper-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 examine whether the
introduction of XBRL is associated with the reduction of information asymmetry. Among them,earlier studies investigate the effect of voluntary filing in the XBRL format on the information
environment (Tan and Shon 2009; Efendi et al. 2010).
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 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 pricemovement 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 the ease of comparability andpredictability, enhancing the relevance of financial 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) report the reduction of information
asymmetry in voluntary disclosure, other studies examining the effect of mandatory XBRL
adoption provide mixed results (Yoon et al. 2011; Kim et al. 2012; Blankespoor et al. 2014). Kim
et al. (2012) investigate the impact of mandatory XBRL reporting and find an increase in
information efficiency, a decrease in event return volatility, and a reduction in the change in stock
returns volatility for 425 U.S. mandated filers in the post-XBRL adoption period. Yoon et al.
(2011) address 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

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The Japanese Accounting Review, 4 (2014), 49-74

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, theeffect 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. (2014) examine the initial impact of the use of
XBRL on information asymmetry after the U.S. SEC mandate. They provide evidence of ahigher abnormal bid-ask spread, a reduction in abnormal liquidity, and a decrease in abnormal
trading volume for XBRL-adopting firms around 10-K filings. As a conclusion, they claim that
the SEC’s objective of improving the information playing field might 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 atallowing the efficient compilation, distribution, and usage of financial statements. However,whether the use of XBRL reduces information asymmetry among investors is not so plausible. In
fact, there are several issues that need to be checked before answering this question.12 According
to the SEC (2009, 125-131), the benefits of using XBRL-based disclosure can be summarized asfollows: XBRL (1) allows investors to acquire financial statement data more timely and withlower costs and to analyze larger amounts of data,13 (2) provides richer sets of data than those
provided by existing data aggregators, and (3) enables investors to compare data across firms
more efficiently through a structured taxonomy. However, to enjoy the benefits of the XBRLbased disclosure, filers and investors are expected to incur setup and learning costs, respectively.14
The SEC (2009, 132-146) specifies the former as costs of submitting and posting interactive data.
Investors may pay either for the costs of learning the large taxonomy and tagging structure, or for
the use of XBRL-enabled software and services that read XBRL-tagged data. Accordingly,
investors are likely to compare costs to benefits and decide whether to use the new technology.
These costs and benefits may differ between large and small investors, generating two
possible impacts of XBRL adoption on information asymmetry. One possibility is that large
investors may already have similar technology and decide not to use the new technology. If this is
the case, whether small investors receive benefits from XBRL adoption is the key to observing
the reduction of information asymmetry. Unfortunately, there is no report to show how many
investors directly use XBRL-formatted data in Japan. However, as discussed in the previous subsection, two prior experimental studies suggest the improved accessibility of financial information
following the introduction of XBRL (Hodge et al. 2004; Pinsker and Wheeler 2009). In addition,
several new services have started to provide small investors with financial information based on
the XBRL-formatted data less expensively. These services include: kabupro.jp (http://ke.kabupro.jp/),12

Blanskespoor et al. (2014) discuss similar issues.
Prior to the electronic disclosure system, the financial statements (yuka shoken houkokusho) cost approximately
1,500-2,000 yen per firm, resulting in 5 million yen and spaces for files for 3,000 firms. In addition, users had tocopy and paste financial data, which made individual investors difficult to handle the data easily.
14
We agree that investors do not always need to know about XBRL because they can analyze the data read andprovided by XBRL-enabled software and services. However, investors can reduce the costs of payments for the use of
XBRL-enabled software and services if they have sufficient knowledge about XBRL to use XBRL-formatted data.
In other words, investors, who do not want to pay for the use of XBRL-enabled software and services, will have to
pay learning costs to know about XBRL.
13

Bai, Sakaue, and Takeda: The Impact of XBRL Adoption on the Information Environment55

Ufo On-demand (http://www.ufo-on-demand.com/), Ufo Catcher (http://www.ufocatch.com/),minna no zaimu data (http://www.in4mx.com/), and so on. These new services could enable
small investors with fewer resources to access a richer set of financial data, benefitting indirectlyfrom the new technology. If this is the case, the XBRL adoption may decrease information
asymmetry between large and small investors.
Another possibility is that large investors may enjoy greater benefits from XBRL adoption
than do small investors, widening the information asymmetry between them. For instance, the
XBRL enables large database companies to collect financial data automatically and to provide
their services immediately and more accurately than before when they typed financial data into
the computer system.15 If this is the case, the XBRL adoption may increase the information
asymmetry because only large investors with rich resources are likely to benefit from such
improved services provided by large database companies.
Considering these issues, whether the XBRL adoption reduces information asymmetry
among investors remains an empirical question. To conduct our empirical analysis, we set thefollowing null hypothesis:
H0: The XBRL disclosure does not affect the information environment (asymmetry) around
annual securities filing dates.
As discussed in the previous subsection, prior studies report the reduction of information
asymmetry among investors in the case of voluntary XBRL adoption but provide mixed results inthe case of mandatory XBRL adoption. This mixed result suggests the possibility that mandatory
XBRL adoption requires larger setup and learning costs to filers and users than voluntary
adoption.16 Our use of the Japanese setting can contribute to the literature because the Japanesecase provides evidence based on a large number of Japanese listed firms that were mandated todisclose financial statements by using the XBRL format all at once. In the following sections, weinvestigate 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.17

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 were 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 ofvariable definitions is presented in Appendix 3.

15This case is reported in the workshop involving a Japanese branch of the world large financial database company.
In general, mandatory XBRL adoption is expected to require larger setup and learning costs to filers and users than
voluntary adoption because of the limited resources (time, money, human resources, etc.) available both inside andoutside of them.
17
The TSE has two sections. Larger and more established firms are supposed to be listed in the first section. We focus
on the first section of the TSE because this is the most liquid market in Japan, where the most stocks are traded
every day, while the other markets are less liquid and thus a nontrivial number of stocks are not traded every day,causing difficulty in obtaining stock price data throughout the sample period.
16

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The Japanese Accounting Review, 4 (2014), 49-74

FIGURE 1: TIMELINE OF THE EVENT STUDY(a) Pre-XBRL period (January 1, 2008 - June 30, 2008)
Event Window

Estimation Windowt=-255t=-55

t=-1 t=0

t=+1(b) Post-XBRL period (January 1, 2009 - June 30, 2009)
Estimation Window

Event Windowt=-255t=-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.

The first two measures are calculated by using abnormal returns (ARs) based on the event
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 inwhich the market is considered to be affected by the event, minus the expected return of the
security over the event window, as follows:|(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 the estimation
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 the return of the market
portfolio. Denoting Rmt as the market return, the market model is expressed as follows:

0(2)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 from day 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.

Bai, Sakaue, and Takeda: The Impact of XBRL Adoption on the Information Environment

57

We then calculate the five measures that capture the information environment. The firstmeasure is ERV. Following prior studies (Baily et al. 2003; Heflin et al. 2003; Francis et al. 2006;
Kim et al. 2012),18 ERV is defined as an arithmetic sum of the absolute values of ARs over the
event window, as follows:
= ∑

|

|.

(3)

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 not reflected 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 theevent window, following Heflin et al. (2003) and Kim et al. (2012), as follows:
= ∏

1+

−1 .

(4)

Similar to ERV, ACAR captures the gap between the full information, post-event stock priceand a pre-event price by taking the absolute deviation between the actual return and the expected
return. A higher ACAR indicates a larger information gap and higher information asymmetry
between informed and uninformed investors. Conversely, a smaller ACAR demonstrates asmaller deviation, which implies an improved information environment.
The third measure is ΔSTDDEVRET. Following Kim et al. (2012), ΔSTDDEVRET isdefined as the change in the standard deviation of daily stock returns before and after the firm’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:
∆

=

∑

(

−

) −

∑

(

−

) .

(5)

ΔSTDDEVRET indicates the frequency of information reaching the market and the extent of
information asymmetry among investors. As a result, we expect that a reduction in the change inthe 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 the estimation window(pre-event period). Following prior studies (Yoon et al. 2011; Blankespoor et al. 2014),
ASPREAD is expressed as follows:19
18

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 valuerelevant information to professional investors without disclosing the same information to the public at the same time.
19
To calculate the bid-ask spread, we follow Corwin and Schultz (2012) and use a bid-ask estimator developed fromdaily high and low prices.

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The Japanese Accounting Review, 4 (2014), 49-74

=

−

.
(6)

The bid price is quoted by buyers and the ask price is quoted by sellers for a given security.
Information asymmetry increases together with the bid-ask spread. Thus, if the XBRL mandate
decreases information asymmetry, we predict a smaller bid-ask spread in the event window thanthat in the estimation window. In other words, the adoption of XBRL is likely to decrease
ASPREAD.
The last measure is AVOL. Following prior studies (Baily et al. 2003; Francis et al. 2006;
Miller 2010; Blankespoor et al. 2014), AVOL is defined as the difference between the mean daily
trading volume during the event window and the mean daily trading volume during theestimation window divided by the standard deviation (STDDEV) of daily trading volume during
the estimation window, as follows:
−

=

(7)
Investors are not able to fully process all available information due to limited time and resources.
As such, information to be processed is selectively chosen. If the XBRL filings are relatively lessdifficult 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 other words, investors should be more
willing to trade in such an environment. Thus, if the adoption of XBRL decreases information
processing costs, we expect a larger average daily volume in the event window than that in theestimation 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 to assess theeffects of XBRL adoption on the information environment in Japan:
=
=

+
+

∆
=

=
+

=
+

+ ∑
+ ∑
+
+
+ ∑
+ ∑

∑

+
+
+
+
+

(8)
(9)
(10)
(11)
(12)

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 are likely to
be associated with the information environment. XBRL is our primary variable of interest. If the
adoption of XBRL improves the information environment, we expect XBRL to be negatively
correlated with ERV, ACAR, △STDDEVRET, and ASPREAD and positively correlated with
AVOL, as explained in the previous sub-section.
CONTROLS are included to ensure that our results are not driven by other firm-specificcharacteristics that may affect the information environment. Specifically, CONTROLS includeBai, Sakaue, and Takeda: The Impact of XBRL Adoption on the Information Environment

59

firm size (SIZE), the market-to-book ratio (MB), the earnings-to-price ratio (EPRATIO),
leverage (LEV), a loss indicator (LOSS), the IT & Services sector (TECH), the percentage ofshares held by foreign shareholders (GOV), the return volatility for the estimation window(RETVAR), an indicator of the negative sign of the cumulative AR (NEGCAR), and thecumulative 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 of market
capitalization at the end of the corresponding fiscal year. Considering larger firms’ ability tosustain 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 the end of
the fiscal year. MB is included to capture the perceived potential growth in the market. We also
include EPRATIO of the firm at the end of the fiscal year as a proxy for expected growth inearnings, as prior research argues that growth expectations increase stock price responses toearnings (Skinner and Sloan 2002). Because high-growth firms are likely to have higher
information asymmetry, we expect a positive correlation between MB/EPRATIO andinformation asymmetry.
Firms operating at higher leverage generally have higher financial risks, and they tend to be
inclined to disclose more financial information to provide assurance to creditors and confidence
to the public (Kothari et al. 2009). As a result, we include LEV, which is defined 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 correlated with performance and that thepresence of a net income loss impedes the market’s ability to accurately forecast the firm’s
earnings (Heflin et al. 2003). To control this negative effect, we employ a dummy variable LOSS,
which is equal to one if the net income in the corresponding fiscal year is negative and zerootherwise. Based on the explanations above, we expect a negative (positive) correlation between
LEV (LOSS) and information asymmetry.
We also include an industry dummy variable TECH for technology firms, which is equal to
one for firms that are classified under IT & Services and zero otherwise. Technology firms
usually exhibit financial statements that are more difficult to interpret due to financial reporting
models that may not properly capture value-relevant information related to intellectual property
and R&D activities. We expect a positive correlation between TECH and information
asymmetry. GOV is a percentage of shares held by foreign shareholders as a proxy for corporate
governance. Because foreign shareholders are expected to be more demanding for disclosure in
Japan than domestic shareholders, firms with a high foreign shareholder ratio are expected to faceless 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 thestandard deviation of the firm’s ARs during the market model estimation window for thecorresponding 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 upward price movements (Heflin et al. 2003). Lastly, CAAR, which is acumulative absolute AR around the filing date from t=-30 to t=+30, is included. CAAR measures

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The Japanese Accounting Review, 4 (2014), 49-74

TABLE 1: SAMPLE SELECTION
Firms listed in the first section of the TSE as of May 31, 2012(less) Firms with incomplete data and those do not exist between January 1, 2007, and June 30, 2009
Sample firms

No. of firms
1,685
547
1,138

Weight100.0%
32.5%
67.5%

the total information flow, and firms with a larger information flow are expected to have larger
information gaps at any given time. We expect this variable to be positively correlated withinformation asymmetry (Heflin et al. 2003).

3.3 Sample selection
We downloaded 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 (Table 1). 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)20 for all thefirms between January 1, 2007, and June 30, 2009. (This is similar to the 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. Data with respect tostocks, which include the high price, low price, closing price, opening price and volume, aredownloaded from Yahoo! Finance Japan. Lastly, the number of shares outstanding for each firm
is obtained from Nikkei NEEDS-Financial Quest.
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 for each of the
1,138 firms to allow a more accurate comparison between the pre-XBRL and post-XBRL period(1,138 annual securities filing dates for the pre-XBRL period and the post-XBRL period). We
set the period between January 1, 2008, and June 30, 2008 as the pre-XBRL period and theperiod between January 1, 2009, and June 30, 2009 as the post-XBRL period. Because mostJapanese firms have their fiscal year ending on March 31, the majority of annual securities filingdates of the sample are found in June.
Table 2 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, and 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 specific industry.

20

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

Bai, Sakaue, and Takeda: The Impact of XBRL Adoption on the Information Environment

61

TABLE 2: DISTRIBUTION OF SAMPLE BY TOPIX-17 SERIES SECTOR CODE

Code
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17

Category
Foods
Energy Resources
Construction & Materials
Raw Materials & Chemicals
Pharmaceutical
Automobiles & Transportation Equipment
Steel & Nonferrous Metals
Machinery
Electric Appliances & Precision Instruments
IT & Services, Others
Electric Power & Gas
Transportation & Logistics
Commercial & Wholesale Trade
Retail Trade
Banks
Financials excluding Banks
Real Estate
Total

No.
49
10
111
123
20
53
50
87
124
157
11
47
91
95
57
24
29
1,138

%
4.31
0.88
9.75
10.81
1.76
4.66
4.39
7.64
10.9
13.8
0.97
4.13
8.00
8.35
5.01
2.11
2.55
100.004. Empirical Results
4.1 Descriptive statistics
Table 3 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 mean differences of thefive 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 theinformation environment through the reduction of information asymmetry.

62

The Japanese Accounting Review, 4 (2014), 49-74

TABLE 3: 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

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
0.000
1.000
1.000
24.721
1.489
24.27424.451
1.306
1.107
0.881
1.088
0.042
0.137
0.028
-0.051
0.160
0.136
0.140
0.176
0.093
0.291
0.000
0.335
0.138
0.345
0.000
0.138
0.139
0.118
0.084
0.117
0.026
0.008
0.037
0.037
0.435
0.496
0.000
0.437
0.137
0.129
0.122
0.160
Note: *** indicates significance at the 1% level.

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 ***

Among the 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.21
Among the control variables, SIZE, MB, EPRATIO, LEV, LOSS, GOV, RETVAR, andCAAR 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 the firms
deteriorate in the post-XBRL period, as observed from the lower EPRATIO and the higher
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 the financial health and governance offirms may be partially attributed to the 2008 financial crisis, which had a huge negative impact onfirms and stock markets. Lastly, RETVAR and CAAR increase in the post-XBRL period anddiffer at the 1 percent significance level.
Table 4 provides the Pearson correlation matrix among the variables used in the regression.
The highest correlation among the independent variables is 0.622, which is the correlation
between SIZE and GOV. The second highest correlation is 0.525, which is the correlation21

It is noteworthy that the standard deviation of AVOL in the pre-adoption period is too high compared to thatreported in the previous studies, including Miller (2010). We regard this high S.D. as caused by the market turmoil
related to the subprime loan problem in 2008. One might argue that not only AVOL but other market variables may
also have been affected by subprime loan problems. However, we think that it is natural to believe that the volumedata would have been affected by the turmoil to a greater extent than the other variables, because the former reflects
both buying and selling behavior while the latter reflects only the net behavior (return or spread). We will discuss theeffect of this high S.D. in subsection 4.2.

Bai, Sakaue, and Takeda: The Impact of XBRL Adoption on the Information Environment

63

TABLE 4: PEARSON CORRELATION MATRIX FOR EXPLANATORY VARIABLES
XBRL
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

SIZE

MB

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

EPRATIO-0.085-0.5480.034
0.071-0.295-0.022-0.169LEV

0.103-0.063-0.0150.126
0.085-0.008LOSS-0.044-0.120
0.299
0.008
0.142

TECH

-0.012-0.069-0.056
0.012

GOV

0.074
0.036
-0.052RETVAR NEGCAR-0.021
0.234

0.104

TABLE 5: CORRELATION WITH DEPENDENT VARIABLES
XBRL
SIZE
MB
EPRATIO
LEV
LOSS
TECH
GOV
RETVAR
NEGCAR
CAAR

ERV
0.013
-0.0950.133-0.121
0.047
0.142
0.042
-0.0100.251-0.0960.281

ACAR-0.007-0.0550.062-0.097
0.054
0.075
0.008
0.001
0.163
-0.0900.256

ΔSTDDEVRET ASPREAD-0.128-0.2490.136-0.060-0.027
0.006
0.088
0.011
-0.037-0.034-0.107-0.023-0.044-0.0340.109-0.037-0.082-0.179-0.024-0.053-0.1430.093

AVOL-0.020-0.062-0.004-0.030
0.038
0.071
0.013
-0.053-0.004-0.032
0.140

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

between XBRL and RETVAR. Despite the relatively high correlations in these two areas, therest of the correlations among the independent variables are below 0.5. The Variance Inflation
Factor (VIF) is also calculated to confirm the absence of multicollinearity (Table 5).22 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.

4.2 Regression results
Table 6 shows the multivariate regression results for the effects of XBRL on the information
environment in the Japanese market. Models 1 to 5 use ERV, ACAR, ΔSTDDEVRET,ASPREAD, and AVOL as dependent variables, respectively, based on equations (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

22This factor is calculated with a three-step process. The first step involves running an ordinary least regression oneach control variable as a function of all other explanatory variables to obtain a coefficient of determination of theregression equation. The second step involves calculating the VIF factor for each control variable using the formula. The final step is to analyze the magnitude of multicollinearity by considering the size of the VIF=
values.

2

Adjusted R
No. of Obs.

F-testConstant
XBRL
SIZE
MB
EPRATIO
LEV
LOSS
TECH
GOV
RETVAR
NEGCAR
CAAR

－/+
+
+
+
－
+
+
－
+
+
+

Expectedsign
2.314 **
-5.123 ***-1.2590.642-1.0502.179 **
0.455
0.324
0.468
6.582 ***
-5.621 ***5.788 ***

-3.373 ***-4.545 ***3.444 ***
-1.807 *0.549-1.792 *-1.482-1.311
1.152
1.162-1.568-2.358 **

2,276

0.051

12.126 ***-0.015-0.0020.001-0.0010.001-0.003-0.001-0.0010.003
0.030-0.001-0.007

Model 3 (ΔSTDDEVRET)
t-statCoefficient

2,276

0.087

20.665 ***0.860-0.334-0.0270.012-0.043-0.0350.076-0.110-0.048-7.512-0.0590.616

2.651 ***
-8.94 ***
-1.937 *0.54-0.644-0.3361.513-2.365 **-0.281-4.178 ***-1.957 *4.604 ***

Model 4 (ASPREAD)
t-statCoefficient

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

2,276

2,276

23.165 ***0.023-0.006-0.0010.001-0.003
0.009
0.001
0.001
0.003
0.377
-0.005
0.036

0.097

4.537 ***
-6.435 ***-3.259 ***2.150 ***
0.360
1.775 *
2.893 ***
1.524
0.703
10.673 ***-6.504 ***5.312 ***

Model 2 (ACAR)
t-statCoefficient

0.162

40.984 ***0.058-0.010-0.002
0.003
0.001
0.008
0.007
0.003
0.005
0.779
-0.0080.044

Model 1 (ERV)t-statCoefficient

1.520-1.196-1.643-0.244
1.267
1.458
1.616
0.537
-2.037 **-2.610 ***-1.3682.761 ***

2,276

0.026

6.412 ***

2.153
-0.533-0.093-0.017
0.746
2.212
1.284
0.232
-1.555-20.416-0.367
6.738

Model 5 (AVOL)
t-statCoefficient

TABLE 6: REGRESSION RESULTS FOR THE INFORMATION ENVIRONMENT AROUND FILING DATES64The Japanese Accounting Review, 4 (2014), 49-74

Bai, Sakaue, and Takeda: The Impact of XBRL Adoption on the Information Environment

65

TABLE 7: REGRESSION RESULTS WITHOUT OUTLIERS
Expected
Model 1 (ERV)
sign
Coefficientt-statConstant
XBRL
SIZE
MB
EPRATIO
LEV
LOSS
TECH
GOV
RETVAR
NEGCAR
CAAR

－/+
+
+
+
－
+
+
－
+
+
+

F-test2

Adjusted R
No. of Obs.

0.040
-0.009-0.001
0.001
0.004
0.004
-0.002
0.003
0.006
0.729
0.028
-0.006

3.893 ***
-7.274 ***-2.099 **1.371
2.334 **
0.971
-0.6252.089 **
1.081
12.479 ***5.908 ***
-6.068 ***Model 2 (ACAR)
Coefficientt-stat0.022-0.005-0.000-0.0000.000
0.006-0.004-0.001
0.005
0.354
0.024
-0.0052.453 **
-5.148 ***-1.094-0.4540.169
1.949 *
-1.632-0.414
1.111
6.813 ***
5.970 ***
-5.705 ***Model 3 (ΔSTDDEVRET)
Coefficientt-stat-0.011-0.0020.000-0.000-0.001-0.0020.001-0.0000.004
0.026-0.003-0.000-3.380 ***-4.863 ***3.138 ***
-0.856-2.034 **-1.737 *0.772-0.4742.034 **
1.185
-1.820 *-1.124Model 4 (ASPREAD)
Coefficientt-stat0.505-0.291-0.014-0.0010.018-0.050-0.079-0.106-0.035-5.8200.427-0.044

1.681 *
-8.651 ***-1.109-0.0720.451-0.524-1.249-2.694 ***-0.224-3.583 ***4.050 ***
-1.603Model 5 (AVOL)
Coefficientt-stat0.314
0.064-0.023
0.030
0.109
0.385
0.042
-0.024-0.287-3.4361.268-0.10536.658 ***19.096 ***10.513 ***17.669 ***

0.150

0.082

0.045

0.076

0.046

2,230

2,230

2,230

2,230

2,230

0.731
1.311
-1.2561.004
1.588
2.750 ***
0.393
-0.406-1.269-1.454
6.634 ***
-2.624 ***10.789 ***Notes: 1: The regression is conducted by using White heteroskedasticity-consistent standard
errors and covariance.
2: *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

significantly negative correlation between XBRL and the four measures (ERV, ACAR,ΔSTDDEVRET, and ASPREAD) are consistent with the notion that XBRL adoption
improves the information environment by reducing information asymmetry.
Our result for Models 1 to 4 shows similar benefits from the adoption of XBRL as thosereported 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 that investors have
incentives to trade more actively in an XBRL environment nor the results of Blankespoor et al.
(2014), which show a decrease of AVOL after the adoption of XBRL.23
There are possibly three reasons to explain why our result for AVOL is not consistent withthat of prior studies. First, as reported in Table 3, the standard deviation of AVOL is quite large
in the pre-adoption period. This could have been caused by the market turmoil related to thesubprime loan problems. This high volatility in AVOL may have caused the insignificant resultsin our study. Second, AVOL may not always reflect information asymmetry among investors but
a diversified interpretation of the news (Kim and Verrecchia 1991, 1997; Bamber et al. 2011). In
other words, it may not be appropriate to use AVOL as a measure of information asymmetry, as
investors’ interpretation of the news may differ largely during the period of financial turmoil.
Third, outliers may have caused the negative and insignificant coefficient on AVOL.
To check the third possibility, we follow Kim et al. (2012) and Blankespoor et al. (2014) by
winsorizing all five dependent variables at 1% and 99% to remove the effect of outliers andconduct the same regressions. The results are shown in Table 7; as may be seen, they are quite
similar to those reported in Table 6. However, we note that the coefficient on AVOL becomes23

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. Thisis consistent with the results for the impact of the mandate of Reg FD (Heflin et al. 2003; Bailey et al. 2003).

66

The Japanese Accounting Review, 4 (2014), 49-74

positive. Although it is not significant, Table 7 indicates the possibility that outliers may havecaused negative coefficient on AVOL.
The results for control variables are mixed. First, the coefficients of SIZE are significantly
negative for Models 1 and 4, significantly positive for Model 3, and insignificant for Models 2
and 5. In other words, our results are mixed with regard to the size effect because the results for
Models 1 and 4 are consistent with our prediction that larger firms tend to have lower
information asymmetry, while those of other results are not. We note that Blankespoor et al.
(2014) also provide insignificant results for the ASPREAD used in Model 2. They also report asignificantly negative size effect on AVOL using a large trade volume but insignificant size effecton AVOL using a small trade volume. This may suggest that our insignificant result for Model 5
comes from the fact that we combine both large and small trading volumes.
As for the effect of potential growth on information asymmetry, the results for MB are also
mixed, with a 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 isconsistent with our prediction, while those of other results are not. We note that Kim et al.
(2012) provide insignificant results for ΔSTDDEVRET. Blankespoor et al. (2014) also report asignificantly negative size effect on AVOL using a large trade volume but an insignificant size
effect on AVOL using a small trade volume. Thus, the relationship between MB and
ΔSTDDEVRET or AVOL is not stable in prior studies, either. 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, asignificantly 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 other results are notconsistent with our expectations. This result is not very surprising considering the fact that Kim
et al. (2012) reports only one significantly negative coefficient of EV among five regression
models. The coefficient of LOSS is significantly positive for Model 1, while it is insignificant forthe 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 the shareholder
composition, the coefficient of GOV is significantly negative only for Model 5, which is notconsistent with our prediction, while it is insignificant for the other four models. In other words,
we do not find consistent industry effects.
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 forModels 4 and 5. The results for Models 1 to 3 are consistent with those of Kim et al. (2012),
which report significantly positive coefficients for ERV and ACAR but insignificant coefficientsfor ΔSTDDEVRET. The coefficients of NEGCAR are significantly negative for Models 1, 2,
and 4 and insignificant for Models 3 and 5. This result is not surprising, as Kim et al. (2012)
report insignificant coefficients on NEGCAR for four regression models. The coefficients of
CAAR are significantly positive for Models 1, 2, 4, and 5 and insignificant for Model 3. This
result is not surprising, as Kim et al. (2012) report only one significantly positive coefficient on
CAAR among four regression models.
As in the case of prior studies, we do not obtain consistent results for the control variables.
However, four of the five models in our regression analysis show the reduction of information
asymmetry and then the improvement of the information environment in the Japanese market.

Bai, Sakaue, and Takeda: The Impact of XBRL Adoption on the Information Environment

67

TABLE 8: REGRESSION RESULTS FOR THE INFORMATION ENVIRONMENT AROUND FILINGDATES (WITHOUT FIRMS IN THE FINANCIAL INDUSTRY)
Expected
Model 1 (ERV)
sign
Coefficientt-statConstant
XBRL
SIZE
MB
EPRATIO
LEV
LOSS
TECH
GOV
RETVAR
NEGCAR
CAAR

－/+
+
+
+
－
+
+
－
+
+
+

F-test2

Adjusted R
No. of Obs.

0.043
-0.011-0.001
0.003
0.002
0.004
0.008
0.003
-0.0020.796-0.0080.045

3.018 ***
-6.560 ***-1.801 *1.940 *
0.446
0.848
3.152 ***
1.367
-0.212
10.565 ***-6.289 ***5.255 ***

37.984 ***
0.161
2,114

Model 2 (ACAR)
Coefficientt-stat0.014-0.0060.000
0.001-0.003
0.006
0.001
0.000
-0.001
0.374-0.0050.037

1.233-5.005 ***-0.2290.619-1.126
1.313
0.787
0.191
-0.172
6.345 ***
-5.472 ***5.746 ***

21.676 ***
0.097
2,114

Model 3 (ΔSTDDEVRET)
Model 4 (ASPREAD)
Coefficient
Coefficientt-statt-stat-0.018-0.0020.001-0.0010.001-0.004-0.001-0.0010.001
0.034-0.001-0.008-3.729 ***-4.034 ***3.757 ***
-1.760 *0.677-2.227 **-1.143 ***-1.4390.366
1.279-1.603-2.315 **10.787 ***
0.048
2,114

0.609
-0.333-0.0140.005-0.039-0.1640.106-0.121-0.145-7.804-0.0560.660
19.812 ***
0.089
2,114

1.732 *
-8.610 ***-0.9470.237-0.559-1.426
1.967 **
-2.572 **-0.775-4.220 ***-1.777 *4.805 ***

Model 5 (AVOL)
Coefficientt-stat2.086-0.533-0.091-0.016
0.857
2.299
1.395
0.227
-1.655-21.628-0.3616.941

1.235-1.146-1.300-0.2201.343
1.336
1.649 *
0.525
-2.221 **-2.576 **-1.2792.749 ***

6.096 ***
0.026
2,114

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

Although the coefficient on AVOL is negative and insignificant, this could have been due tooutliers. In the next section, we conduct two sensitivity analyses to guarantee the robustness of
our main results regarding measures of the financial information environment.

5. Sensitivity Analysis
In this last section, we conduct two sensitivity analyses. We note that the stock market wasvery 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 themodels in Table 8 after eliminating Banks and Financial Industry firms (codes 15 and 16 in
Table 2). After eliminating these firms, our results are qualitatively similar to those in Table 5.
Our main coefficients of XBRL remain negative and significant at the 1 percent level for Models
1 to 4.
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 carry out the multiple
regression analyses to verify whether the results are the same. In this context, we use January 1,
2007 to June 30, 2007 as the pre-XBRL period and January 1, 2008 to June 30, 2008 as the postXBRL period. Table 9 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, andsignificantly negative for Model 3. These results are contrary 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 are observed 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.

68The Japanese Accounting Review, 4 (2014), 49-74

TABLE 9: REGRESSION RESULTS FOR THE INFORMATION ENVIRONMENT AROUND FILINGDATES BASED ON ALTERNATIVE PRE-AND POST-XBRL PERIODS
Model 1 (ERV)
Expected
t-statsign
Coefficient
Constant
XBRL
SIZE
MB
EPRATIO
LEV
LOSS
TECH
GOV
RETVAR
NEGCAR
CAAR

－/+
+
+
+
－
+
+
－
+
+
+

F-test2

Adjusted R
No. of Obs.

0.021
0.003
-0.001
0.003
0.000
0.006
0.006
-0.002-0.0090.885-0.004
0.071

1.441
1.430
-0.9372.123 **
-1.0231.027
1.484-0.720-1.1436.365 ***
-3.118 ***5.405 ***

40.549 ***
0.206
1,678

Model 2 (ACAR)
t-statCoefficient
0.001
0.004
0.000
0.002
0.000
0.008
0.003
-0.0010.000
0.389-0.0040.055

0.100
1.943 *
0.038
1.552
0.360
1.540
1.026
-0.556-0.0263.372 ***
-3.541 ***4.336 ***

26.307 ***
0.142
1,678

Model 3 (ΔSTDDEVRET)
t-statCoefficient-0.026-0.0020.001-0.0010.000-0.003-0.002-0.001
0.002
0.090-0.001-0.001-5.500 ***-4.054 ***5.744 ***
-2.524 **-1.650 *-1.506-2.354 **-1.4560.841
2.031 **
-1.120-0.141

8.978 ***
0.050
1,678

Model 4 (ASPREAD)
t-statCoefficient
0.564
0.291
-0.028
0.005
0.000
0.134
0.161
-0.0950.044-10.653-0.0350.983
9.148 ***
0.051
1,678

1.302
6.100 ***
-1.527
0.182
0.143
0.913
1.615
-1.6150.193-3.402 ***-0.8984.542 ***

Model 5 (AVOL)
t-statCoefficient-0.530-0.091
0.033
0.009
0.000
0.481
0.895
-0.359-1.372-29.159-0.2076.158-0.641-0.5300.935
0.218-1.2861.447
0.985-2.644 ***-2.246 **-1.798 *-1.5412.138 **

8.682 ***
0.048
1,678

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

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 theinformation environment in the Japanese market. However, given the non-trivial implementationand learning costs involved in the adoption of XBRL, whether the XBRL-based disclosure
improves the information environment is an empirical question. In the present study, weinvestigate 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 observed from thehighly significant and negative associations between XBRL adoption and event returns volatility,
absolute cumulative abnormal returns, changes in the standard deviation of 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 a sensitivity analysis excluding financial firms.
We acknowledge that our study is not exempt from limitations. In particular, we focus on
the effect of XBRL adoption on the information asymmetry among investors. However, XBRL
should influence other stakeholders including filers and regulators. Future research should beneeded to investigate the more comprehensive impacts of XBRL adoption on various
stakeholders.

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69

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Appendix 1: Setting of Japan
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 filing format in2003 and began accepting XBRL 2.1 filings in 2008 (Sakuta 2008). The introduction of XBRLby the NTA also allowed unlisted companies to utilize XBRL. For example, small- and mediumsized 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, to improve the efficiency of gathering data from financial institutions(Wada 2006). 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 inefficiency. In July 2008, the TSE introduced the third generation TDnet server for XBRL
production use, 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
the timely disclosure policy, which was applicable to approximately 4,000 listed companies atstock 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 and Exchange Act,and all listed companies and investment funds in Japan are required to file their disclosure
documents using the system. These submitted corporate disclosure documents are prepared inHTML format and publicly available on the Internet through EDINET. Within this system, aseries of procedures ranging from submission, receipt, and public viewing of annual securities
reports (yukashoken houkokusho in Japanese) and other disclosure 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 disclosureof critical information for investors and issuers and allowing the general public to instantly accesscorporate information that has 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 in theirannual securities reports, semiannual securities reports, quarterly securities reports and securities
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 2008.24 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 testing to expand the XBRL’s scope of
application and upgrade EDINET’s functionality. EDINET was scheduled to be upgraded infiscal 2013.

24

Precisely speaking, financial statements in the XBRL format became available at the EDINET on June 29, 2008.
The number of the filers in June 2008 was nine.

Bai, Sakaue, and Takeda: The Impact of XBRL Adoption on the Information Environment

73

Appendix 2: Literature review on the reporting quality of XBRLThe reporting quality of XBRL-based financial reports is vital to its success. It is expectedthat 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 and accuracy. However, theliterature provides evidence that the XBRL reporting still contains deficiencies andincompleteness (Zhu and Fu 2009; Debreceny et al. 2010; Bartley et al. 2011; Roohani andZheng 2011).
Zhu and Fu (2009) develop metrics to empirically evaluate financial reports created using the
XBRL data standards. The use of standard elements and company-specific elements is found tovary substantially across companies. An average company uses 128 elements from the XBRL
standard, which defines approximately 2,000 elements, and introduces 64 elements of its own.
The completeness and relevancy of data standards affect the interoperability of the data created.
However, they show that the XBRL standard has low completeness and relevancy from anindividual company’s perspective. To the average company, the completeness of the XBRL
standard is 66.67 percent and the relevancy of the standard is 6.4 percent.
Debreceny et al. (2010) study one aspect of data quality errors in the computation of relatedmonetary facts in the XBRL filings. Out of 400 filers, they discover that one-quarter havecomputational errors, half of which are due to inappropriate treatment in the instance documentsof 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 findthat all 22 companies made errors during the first year of voluntary XBRL filings by examining
22 companies' initial voluntary XBRL 10-K filings. To find the determinants of the deficiency of
XBRL mandatory filings, Roohani and Zheng (2011) investigate all the SEC filings from July2009 to December 2010. They conclude that XBRL-deficient filings tend to have a higher
percentage of extensions and are filed by bigger and more complex firms.25 In addition, firms thathave considerable experience in XBRL filings are less likely to have major errors. On the contrary,
they are more likely to have 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 computerreadable tags to allow the interchange of interactive information, are consistent with thereporting practices (Bonson et al. 2009; Valentinetti and Rea 2011). Bonson et al. (2009) argue
that multiple XBRL taxonomies based on different accounting principles contradict theobjectives of the standardization, comparability, and reusability of the XBRL information. Theyevaluate the effectiveness of the IFRS-GP taxonomy that could establish a common ground forinternational 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 IFRSFoundation adequately reflects the reporting practices of Italian listed companies. The results
reveal a general discrepancy between the financial items disclosed by the firms and the taxonomy
tags. Specifically, these findings demonstrate that the financial statement items are more

25

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 account for more than 60 percent of the whole infinancial statements.

74

The Japanese Accounting Review, 4 (2014), 49-74

disaggregated than the taxonomy tags, and as such, a loss of detailed information may occur if thetaxonomy is applied.
Appendix 3: Variable definitions
ERV

Arithmetic sum of the absolute values of ARs over the event window
|

ACAR

|

Cartesian product of ARs in the event window: | ∏

1+

− 1|

Change in the standard deviation of daily stock returns before and after thefirm’s annual securities report filing dates
ΔSTDDEVRETASPREAD

1
30

(

−

) −

1
30

(

−

)

Difference between the average daily spread during the event window andthe average daily spread during the estimation window−

AVOL

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
window
−

XBRL
SIZE
MB
EPRATIO
LEV
LOSS
TECH
GOV
RETVAR
NEGCAR
CAAR

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 is negative
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