Initial Evidence on the Market Impact of the XBRL Mandate

Elizabeth Blankespoor Graduate School of Business

Stanford University

Brian P. Miller Kelley School of Business

Indiana University

Hal D. White Ross School of Business University of Michigan

August 2012

ABSTRACT In an effort to reduce information asymmetry, the Securities and Exchange Commission (SEC) has mandated that financial statements be filed using eXtensible Business Reporting Language (XBRL). The SEC contends that this new search-facilitating technology will reduce informational barriers that separate smaller, less sophisticated investors from larger, more sophisticated investors, thereby reducing information asymmetry. However, the SEC also points out that larger investors are likely to gain significant benefits from XBRL as well. If larger investors are able to leverage their superior resources and abilities to garner greater benefits from XBRL than smaller investors, information asymmetry is likely to increase. Using a difference-in-difference design, we examine the impact of XBRL on information asymmetry. We find evidence of higher abnormal bid-ask spreads for XBRL adopting firms around 10-K filings after the mandate, consistent with increased concerns of adverse selection. We also find a reduction in abnormal liquidity and a decrease in abnormal trading volume, particularly for small trades. These findings are robust to numerous falsification tests, alternate specifications, alternate control groups, and alternate control variables. Collectively, our evidence suggests that a reduction in investors’ data aggregation costs may not serve its intended purpose of leveling the informational playing field, if more sophisticated investors are able to leverage their superior resources and processing abilities to gain further trading advantages.

We are grateful to Salman Arif, Daniel Beneish, John Core, Anna Costello, Paul Fischer, Max Hewitt, Charles Lee, Laureen Maines, Greg Miller, Mike Minnis, Joe Piotroski, Marlene Plumlee, Tianshu Qu, Cathy Schrand, Cathy Shakespeare, Nemit Shroff, Dan Taylor, Jim Vincent, Chris Williams, Teri Yohn and workshop participants at Indiana University, University of Chicago, University of Florida, University of Miami, University of Michigan, University of Pennsylvania (Wharton), University of Southern California, University of Utah and the 2011 Stanford Summer Camp for helpful discussions. We would also like to thank Bob Rand at the SEC for assistance with identifying XBRL filings. Elizabeth Blankespoor gratefully acknowledges financial support from the Deloitte Doctoral Fellowship, and Hal White gratefully acknowledges financial support from Ernst and Young.

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“If [XBRL] serves to lower the data aggregation costs as expected, then it is further expected that smaller investors will have greater access to financial data than before. In particular, many investors that had neither the time nor financial resources to procure broadly aggregated financial data prior to interactive data will have lower cost access than before interactive data…Hence, smaller investors will have fewer informational barriers that separate them from larger investors with greater financial resources.” (emphasis added) - SEC (2009) 1. Introduction

In April 2009, the SEC mandated that companies report their financial statement data using

eXtensible Business Reporting Language (XBRL). As suggested in the above quote, the SEC

contends that this new search-facilitating technology will reduce information asymmetry

between smaller, less sophisticated investors and larger, more sophisticated investors by

improving smaller investors’ access to data (SEC 2009).1 However, the SEC has also pointed out

that even larger investors can benefit from gaining ‘cheaper and easier’ access to financial data

using XBRL (SEC 2009). In particular, larger investors can shift a significant amount of their

resources from data collection to analysis.2 In this study, we investigate the impact of the XBRL

mandate on information asymmetry, and thus market liquidity.

Many argue that XBRL fundamentally changes the way financial data is communicated to,

and processed by, investors. For example, Corey Booth, SEC Chief Information Officer, states,

"Interactive data represents the logical next step in the evolution of company disclosure … this

move will usher in a quantum leap in helping companies explain their business to investors"

(SEC 2009). In particular, XBRL can (i) reduce acquisition costs through computer automated

data collection, thereby allowing investors to analyze more data, (ii) provide a richer set of data

than those provided by data aggregators (e.g., Factset, Compustat, Capital IQ), which are

typically used by larger investors, and (iii) allow investors to directly compare data across firms 1 Although there is a broad spectrum of investor ‘types,’ in this paper, we characterize investors as either large or small for parsimony, where small investors are relatively less sophisticated—i.e., they have relatively fewer resources and/or abilities—as compared to larger investors. 2 As Christopher Whalen (2004), co-founder of Institutional Risk Analytics, states, "Putting financial data in ready-to-crunch condition might seem a trivial detail until you consider that Wall Street currently spends 80% of its time and money in data mining and 20% on actual analysis – what data pros call the ‘80-20 rule.’” Whalen adds that XBRL could flip the 80-20 rule, allowing much more time to be spent on analysis.

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much more efficiently via a structured taxonomy.3 Thus, although XBRL-formatted (interactive)

financial statements provide no additional data relative to the HTML filings, investors can obtain

more data than they would have otherwise, as a result of more efficient processing.4

To receive these benefits, however, investors must incur nontrivial costs, such as learning the

large and growing U.S. GAAP XBRL taxonomy (with over 15,000 unique tags) and tagging

structure. Investors must also develop or modify software to incorporate the XBRL data into

their specific valuation processes, which includes understanding how best to convert the data into

usable information for their trading models. That is, XBRL can help reduce data aggregation

costs, but investors must then understand how to derive informational benefits from the new

data. Accordingly, investors must weigh these costs against the benefits when deciding whether

to incorporate the new technology.

Given the costs and benefits can vary across investors with different resources, it is a priori

unclear what impact, if any, XBRL will have on information asymmetry among investors. On

one hand, it is possible that larger investors already have similar (proprietary) technology, so

they may receive little to no benefit from implementing XBRL. As such, to the extent smaller

investors adopt the technology and receive informational benefits, we should observe a reduction

in information asymmetry.

On the other hand, larger investors may garner greater benefits from XBRL than do smaller

investors, particularly given their superior resources and abilities. Specifically, while XBRL may

be able to reduce the potential for differential data aggregation costs across investor groups,

3 The power of XBRL comes both from its ability to help computer software ‘understand’ the meaning of each number or disclosure within the financial statements and the uniformity in the definition of financial terms across firms’ financial statements. See the appendix for more discussion and detailed examples of the benefits of XBRL. 4 In this paper, we assume investors are constrained in their abilities and resources, so they cannot fully process all publicly available information (Merton 1987; Hirshleifer and Teoh 2003). As such, processing efficiencies allow investors to process more information, such as broader peer comparisons or more detailed fundamental analyses.

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small investors may still not have the ability to process and integrate the new data in the same

advantageous manner as larger investors. To the extent larger investors are able to extend their

information advantage over smaller investors either by leveraging their superior resources and

abilities to take advantage of the technology or because smaller investors are reluctant to adopt

the technology or both, we should observe an increase in information asymmetry.

An alternative possibility is that XBRL has no impact on trading behavior. First, investors

may simply decide not to implement the new technology and ignore its potential effects because

they believe the net incremental benefits are insignificant. Second, the trading impact from

investors using XBRL (or investors’ concerns about others using XBRL) may be insignificant.

Thus, the extent to which the XBRL mandate impacts the market is an empirical question.

To conduct our analyses, we collect the interactive 10-K filings of firms required to adopt

XBRL during the first phase-in year (fiscal periods ending between June 15, 2009 and June 14,

2010) of the three-year phase-in period. During the initial phase-in period, only a subset of firms

are required to comply with the regulation, which allows us to use a difference-in-difference

design to examine the change in information asymmetry from the pre-XBRL adoption period to

the post-XBRL adoption period relative to a set of matched non-XBRL adopting control firms.5

Restricting our tests to the first year of XBRL adoption provides the cleanest and arguably

most powerful setting to examine the initial impact of XBRL. First, firms had little time to

undertake significant changes in their disclosure policies by the time they filed their initial

XBRL filings, as the mandate was announced in the same year as the first phase-in period. This

alleviates potential concerns related to the impact of disclosure changes on investor trading

5 The XBRL mandate includes a three-year phase-in period, which is essentially based on firm size. Given that the initial set of firms adopting XBRL are large firms, we use two sets of non-adopting control firms: (1) largest firms in the same 3-digit SIC industry and (2) firms with the largest analyst coverage in the same 3-digit SIC industry. See Section 4 for more details regarding the matching approach.

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behavior during the period investigated. Second, shifting the analysis to the second phase-in year

would result in control firms during the initial year being classified as treatment firms in the

second year, and would shift the control groups to the smallest firms in the market, which are

often fundamentally different than large, accelerated filers. Finally, examining the third phase-in

year would eliminate our ability to use a control group of non-adopters, which is critical given

the economic environment during our sample period.

Using a firm’s bid-ask spread as a proxy for information asymmetry following prior research

(Leuz and Verrecchia 2000; Yohn 1998), we find evidence of higher abnormal bid-ask spreads

around 10-K filings for adopting firms after the mandate, as compared to those of our matched

samples of non-adopting firms.6 Our tests control for factors likely to affect trading behavior

and/or to account for differences in the information environments between XBRL and non-

XBRL firms, such as the presence of information intermediaries, market characteristics,

information content and timing of the filing, and firm-specific characteristics. Our finding is

consistent with increased investor concerns of adverse selection around 10-K filings after firms

adopt XBRL.

We then examine the impact of XBRL on liquidity (using a price impact of trade measure)

and trading volume. Following the same difference-in-difference design used in the bid-ask

spread analysis, we find that XBRL adopting firms experience lower abnormal liquidity and

lower abnormal trading volume, as compared to non-adopters. We also attempt to identify the

type of investor impacted most by the mandate. Using trade size to infer investor type, we find

significantly lower abnormal small trade volume. In fact, the abnormal trade reduction is roughly

two to four times greater for small trades, as compared to that of large trades. Combined with our

6 As described in Section 4, we create abnormal measures of trading behavior, using a control period to difference out ‘normal’ trading behavior.

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information asymmetry results, this evidence is consistent with increased adverse selection

concerns for smaller investors leading to reduced liquidity as a result of the new technology.

To rule out alternative explanations related to differences across XBRL and non-XBRL firms

that are unrelated to XBRL adoption, we conduct our main analyses around a different, but

related information event that does not incorporate XBRL: earnings announcements. If there is

something systematic to either the XBRL firms or the non-XBRL control firms that drives the

difference other than XBRL, then we should observe a similar pattern around this alternate

information event during the same time period. However, we find no differences in abnormal

spreads, abnormal price impact, and abnormal volume (including large and small trades) between

the XBRL and non-XBRL firms around earnings announcements, consistent with XBRL

adoption driving our main results.

We further substantiate our findings by conducting several additional robustness tests.

Specifically, our additional analyses include controlling for potential changes in the quantity and

complexity of information in the financial statements, using various cut-offs on size and a size-

based falsification test, alternate information asymmetry proxies, market depth, alternate design

specifications including fixed effects and clustering standard errors by filing date, and tests to

mitigate potential concerns related to the financial crisis.

Despite our numerous empirical tests, we acknowledge that we cannot definitively identify

the type of traders in the market, nor can we speak directly to their trading intent, as these items

are unobservable. However, we can and do provide evidence that suggests that the XBRL

mandate is associated with an increase in trading frictions, and thus a reduction in market

liquidity, around 10-K filings, which appears to run counter to the SEC’s objective of leveling

the information playing field.

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This study provides initial insights into the capital-market effects around the introduction of

XBRL-formatted financial statements. An early empirical analysis of this significant regulatory

shock to financial reporting is particularly important given the recent focus that the SEC has

placed on information overload and investor protection.7 Our evidence suggests that reducing

data acquisition costs may not be a sufficient condition to level the informational playing field,

given heterogeneity in investors’ sophistication.

Because of the recency of the XBRL mandate, there is limited empirical evidence on the

implications of XBRL. In particular, these studies focus on voluntary filer characteristics, such as

their corporate governance (Premuroso and Bhattacharya 2008), the accuracy of their initial

XBRL filings (Bartley, Chen, Taylor 2010), and the information content of their filings (Efendi,

Park, Subramaniam 2010). Hodge, Kennedy, and Maines (2004) provide experimental evidence

on search facilitating technology. We add to this literature by investigating the initial market

impact of the XBRL mandate.8

We also contribute to the large literature on investor processing costs and trading behavior

(e.g., Grossman and Stiglitz 1980; Bloomfield 2002; Miller 2010; Kalay 2011).9 Most relevant to

our study, Asthana, Balsam, and Sankaraguruswamy (2004) document that the shift to EDGAR

resulted in increased abnormal trading volume around 10-K filings for small investors, but had

no significant impact on large investors. The authors suggest this switch reduced information

acquisition costs for small investors, and thus the information gap between large and small

7 See for example the opening statement from the Open Meeting on the Use of Technology to Improve Financial Reporting (http://www.sec.gov/news/speech/2008/spch051408cc.htm) and the 21st Century Disclosure Initiative Staff Report (http://www.sec.gov/spotlight/disclosureinitiative/report.pdf). 8 Note that we do not examine voluntary filers for several reasons, including self-selection, the SEC’s explicit discouragement of their use by investors and the large timing differences between the HTML and XBRL filings. We provide detailed discussions of these issues in the sample selection section (Section 4.1). 9 Several recent empirical studies use information complexity, accounting comparability, and disclosure format to provide evidence on processing costs on both analyst behavior (e.g. Plumlee 2003; Bradshaw, Miller, and Serafeim 2011; DeFranco, Kothari, and Verdi 2011) and investor trading behavior (e.g. You and Zhang 2009; Miller 2010; Li, Ramesh, and Shen 2011; Cohen and Lou 2012).

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investors. Unlike the switch to EDGAR, however, where filings were readily accessible to large

investors before EDGAR, XBRL provides significant benefits to both small and large investors.

That is, our setting allows us to examine the impact of a change in data aggregation costs for

investors with differing levels of sophistication. Our evidence suggests that although

heterogeneity in investors’ data acquisition costs may contribute to information asymmetry,

investor sophistication may also play a role.

Finally, we contribute to the burgeoning literature on the market impact of technology related

to firms’ communications with investors. For example, Bushee, Matsumoto and Miller (2003)

find that open webcast conference calls lead to more small trading and price volatility.

Blankespoor, Miller and White (2012) document that managers can increase their firms’ market

liquidity by using Twitter to disseminate disclosures. Technology has increasingly become an

integral part of firm-investor communications. We contribute to this research by exploring the

impact of firms’ interactive financial statement data on investor trading behavior.

The remainder of the paper is organized as follows. Section 2 provides some background and

motivation. Section 3 outlines the variable definitions, and the sample selection. The research

design and results are provided in Section 4. Section 5 provides additional analyses, Section 6

discusses the robustness of our results, and Section 7 concludes.

2. Background and Motivation

The SEC has long recognized “the vital role of the Internet and electronic communications in

modernizing the disclosure system under the federal securities laws and in promoting

transparency, liquidity and efficiency in our trading markets” (SEC 2008, p.6). Consistent with

this belief, in 1994, the SEC required filers to make their filings available on the EDGAR

system. More recently, the SEC initiated the XBRL Voluntary Filing Program in 2005 to

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examine the benefits of using interactive data for financial filings. Based on feedback from that

program, on August 19, 2008, the SEC announced plans to fully replace the current EDGAR

filing system, which relies on static electronic data, with a new filing system known as an IDEA

(Interactive Data Electronic Applications) system that utilizes solely XBRL interactive data.10

The overhaul is to take place in stages. Initially, EDGAR will contain both static (HTML)

documents and XBRL interactive data. Then, it will shift to a solely interactive data system at

some point in the future, yet to be determined.

In April 2009, the SEC took its first step in this overhaul by enacting the Interactive Data to

Improve Financial Reporting Rule, which mandates that companies file XBRL documents in

addition to their current filings. The rule institutes a phase-in period from 2009 to 2011. The

initial phase-in period begins with filings for fiscal periods ending on or after June 15, 2009 and

relates solely to large accelerated filers that have a public common equity float over $5 billion.

The second phase-in period relates to all other large accelerated filers (i.e. public common equity

float over $700 million) and begins with filings for fiscal periods ending on or after June 15,

2010. All remaining filers must begin filing XBRL statements for fiscal periods on or after June

15, 2011.

The XBRL mandate requires firms to ‘tag’ financial statement elements. Tagging is the

process of identifying each financial statement element and linking it to descriptive information,

such as the name (Product Sales), year (2009), currency (USD), and denomination (millions), as

well as more detailed definitions and the relationships between items (Product Sales + Service

Sales = Total Sales). That is, unlike textual analysis software, such as PERL, that simply

captures textual data in defined locations, XBRL allows computers to actually ‘understand’ what

10 See Plumlee and Plumlee (2008) for background information on the SEC's efforts to incorporate XBRL into its filing process.

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a given financial statement number represents and how it relates to other numbers in the

statements. Absent XBRL, financial statement users, including data aggregators, must incur

significant costs to collect and organize data from all relevant (comparison) filings. By using the

XBRL tagging structure, computer software applications can easily access pertinent information

about data items and process the data with little human intervention. See the appendix for more

discussion of XBRL and its benefits.

In the first year of adoption, the mandate requires firms to tag each financial statement

element (i.e. quantitative amount) in the basic financial statements, as well as tag footnotes as

blocks of text. In subsequent years, firms must also individually tag all quantitative amounts in

footnotes.11 The XBRL documents are made available on EDGAR, and firms are required to post

them on their corporate websites no later than the same day that their statements are filed with

the SEC to ensure that investors are aware of the XBRL filing.

The SEC argues that XBRL can increase investors’ information sets by reducing their data

aggregation costs, providing a richer set of data than those provided by data aggregators, and

allowing investors to directly compare data across firms much more efficiently via a uniform

taxonomy. To receive these benefits, however, investors must have the resources and abilities to

learn the large XBRL taxonomy, develop or modify the appropriate software to take advantage

of XBRL, and understand how to incorporate this information into their valuation processes.12

11 Since investors’ processing costs are generally higher for acquiring information in footnotes than they are for obtaining information from the face of the financial statements, one may argue that the benefits of XBRL are somewhat muted in the initial year of XBRL adoption. However, according to the SEC and the Committee on Corporate Reporting (which performed extensive outreach on XBRL implementation issues), even after detailed tagging was made available, financial statement users were “primarily interested in tagged information in the basic financial statements” (FEI 2011), providing support for the importance of tagged data in the first year of adoption. 12 The cost of developing or modifying the appropriate software to take advantage of XBRL is currently not trivial because of the shortage of quality XBRL analysis software (SEC 2009; XBRL 2011). Although it is likely that the availability of viewers will increase over time enabling investors to reduce their collection costs, investors will still need to be capable of continually modifying their valuation procedures to process the increasing volumes of data available through the XBRL mandate.

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Given the potential that the costs and benefits may differ greatly across investor groups, it is

ex ante unclear how XBRL will impact information asymmetry. For example, one may argue

that larger investors already have similar (proprietary) technology, so the benefits of

implementing XBRL may be minimal for these investors. To the extent smaller investors are

able to adopt the technology and receive informational benefits, we should observe a reduction in

information asymmetry. The intuition is similar to that developed in Diamond and Verrecchia

(1991), where disclosure reduces information asymmetry by providing uninformed investors

access to information they would not have had otherwise. In our setting, although 10-K filings

are publicly available, any given investor cannot fully process all available information, as

investors have limited attention and processing abilities (Hirshleifer and Teoh 2003). As such,

investors must be selective as to which information they acquire. Since smaller investors

generally have relatively fewer resources and/or abilities than larger investors, they likely

process less information than do larger investors. However, after the mandate, smaller investors

may be able to use XBRL to process information more efficiently, thereby enabling them to

more closely align their information sets with larger investors.

Conversely, larger investors may be able to leverage their superior sophistication to obtain

greater benefits from XBRL than can smaller investors. To the extent larger investors are able to

extend their information advantage over smaller investors, we should observe an increase in

information asymmetry. This intuition is similar to that developed in Kim and Verrecchia (1994),

where public disclosure can increase information asymmetry, as a subset of better processing

investors gain an information advantage from their superior processing ability and the less

informed investors (less capable processors) protect themselves from trading losses by increasing

spreads, or in the case of discretionary liquidity traders, by even refraining from trading around

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news events. In our setting, XBRL serves as a technological tool that allows larger investors to

increase their information advantage through more efficient processing of 10-K filings. Thus,

whether the XBRL mandate has an impact on information asymmetry is an empirical question.

3. Variable Definitions

3.1 Market Variables

Although our primary interest is in the effect of the XBRL mandate on information

asymmetry, we also examine the price impact of trade and trading volume to gain broader

insights into the impact of the mandate on the market. To assess the effect of XBRL adoption on

trading behavior, we focus on abnormal levels of bid-ask spreads, price impact of trade and

trading volume around 10-K filings, where abnormal is defined using the firm as its own control.

Specifically, we follow prior literature (e.g., Asthana et al. 2004) and create abnormal market

measures using the 5-day window around the 10-K filing (trading day -1 to trading day +3) as

our event period, and the 45 trading days prior to the event window (trading day -49 to trading

day -5) as our non-filing control period.

3.1.1 Bid-Ask Spread

Prior research (Cohen, Maier, Schwartz and Whitcomb 1986, Harris 1990, Lee and Ready

1991) indicates that the bid-ask spread captures market makers’ and other liquidity suppliers’

(i.e., public limit order traders) willingness to trade at a low cost. As Leuz and Verrecchia (2000,

p. 99) point out, the bid-ask spread is “commonly thought to capture information asymmetry

explicitly. The reason for this is that the bid-ask spread addresses the adverse selection problem

that arises from transacting in firm shares in the presence of asymmetrically informed investors.

Less information asymmetry implies less adverse selection, which, in turn, implies a smaller bid-

ask spread.” Accordingly, we use a firm’s bid-ask spread as our information asymmetry proxy.

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We measure abnormal spread, ASPREAD, as the event period average daily percent spread

minus the non-filing average daily percent spread, where daily percent spread is the daily

average of each quote’s spread, calculated as the difference between the offer price and bid price,

divided by the midpoint of the offer and bid price, all multiplied by 100.13 We winsorize

ASPREAD (and our other market variables) at 1% and 99% to remove the effect of outliers.14

3.1.2 Price Impact of Trade

Following prior literature (Amihud 2002, Daske, Hail, Leuz and Verdi 2008), we create a

price impact of trade measure to serve as our proxy for liquidity. This measure is inspired by

Kyle’s (1985) lambda, as it attempts to capture the amount of price movement for a given level

of trading volume—i.e., it captures the ability of investors to trade with little price impact. As

indicated in Leuz and Wysocki (2008, p. 6), higher spreads “reduce the liquidity of share

markets, i.e., the ability of investors to quickly buy or sell shares at low cost and with little price

impact” (emphasis added).15 Accordingly, we examine the price impact of trade as our proxy for

liquidity.

We measure abnormal price impact of trade, AIMPACT, following Hasbrouck (2009) and

Goyenko, Holden, and Trzcinka (2009). Specifically, we estimate model (5) in Goyenko et al.

(2009), which regresses the stock return on the signed square-root dollar volume for each five-

minute trading interval during the day, and we use the coefficient on volume as the firm’s price

13 We obtain offer and bid prices from TAQ. As recommended by WRDS documentation, we only use quotes with a positive spread given between 9:30 a.m. and 4:00 p.m. and captured during trading modes. We also remove quotes with spreads greater than 90% of the mid-point price. 14 We rerun our main analyses using raw (unwinsorized) variables and variables truncated at 1% and 99%, and results are qualitatively similar. 15 An alternate liquidity measure is trading volume. However, liquidity relates to investors’ ability to quickly buy or sell shares at low cost and with little price impact (Leuz and Wysocki 2008). Thus, examining volume without consideration of its relation to price cannot tell us whether the market is indeed more liquid. As such, we use the price impact measure following Hasbrouck 2009 and Goyenko, Holden, and Trzcinka 2009, which incorporates both volume and price.

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impact. Using TAQ data, we estimate the price impact for each firm-day. To ensure sufficient

observations for each regression, we require firm-days to have trading activity during at least

two-thirds of the five-minute intervals during the day.16 We then estimate an abnormal measure

around the filing date by subtracting the average daily price impact over the non-filing period

from the average daily price impact over the event period.

3.1.3 Trading Volume

We begin our analyses by examining information asymmetry and market liquidity measures.

However, trading volume can also provide information about the effect of a change in processing

costs on investor behavior. Specifically, academic theory indicates that investors are more likely

to initiate trades as financial filings become easier to process (Grossman and Stiglitz 1980;

Bloomfield 2002). Thus, if XBRL is successful at reducing processing costs, investors may

increase their willingness to trade. However, investor trading may also decrease. In particular,

because XBRL can increase investors’ information sets if they adopt the technology, investors

that are either less effective at incorporating (or simply do not adopt) the technology will face

higher processing costs, as they must compete with others that are leveraging the technology

more effectively. That is, they are at a relative information disadvantage, and therefore must

process more information than they would have otherwise. If the costs related to processing the

additional information exceed the benefits of knowing the information, these investors are less

likely to process and trade on the financial filings. To gain further insights along this dimension,

we examine trading volume around the 10-K filings.

We measure abnormal trading volume, AVOL, following Asthana et al. (2004). In particular,

we use the mean daily trading volume during the event period minus the mean daily trading

16 Note that requiring at least two-thirds of the five-minute intervals during the trading day results in the price impact tests (Table 5) having slightly smaller sample sizes than the spread and volume tests.

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volume during the non-filing period, deflated by the standard deviation of daily trading volume

during the non-filing period.

3.2 Control Variables

To ensure that our results are not driven by other firm-specific characteristics that may affect

abnormal trading behavior, we include several control variables, including the presence of

information intermediaries, market characteristics, information content and timing of the filing,

and firm specific characteristics. We discuss the motivation for the inclusion of these control

variables below and provide detailed variable definitions in Table 1.

To control for the effects of information intermediaries on the firm’s information

environment, we include the log of one plus the number of analysts (Lnanalyst) and the

percentage of shares outstanding held by institutions (Inst_hold). We also include other market

characteristics that are likely to affect trading behavior. Specifically, we control for the prior

quarter turnover (Qt1_turn) in the spread and price impact regressions, prior quarter spread

(Qt1_spread) in the price impact and volume regressions, prior quarter depth (Qt1_depth) in the

spread and volume regressions, and prior quarter stock-return volatility (Qt1_volat) in all three.

In addition to controlling for information intermediaries and other market characteristics, we

also control for the information content and timing of filing. Specifically, based on Bamber and

Cheon (1995) and Bamber, Barron and Stober (1997), we control for the absolute value of the

market reaction to the 10-K filing (Abs_abn_ret).17 Additionally, we control for the information

content of the prior earnings announcement (Abs_esurp), which we calculate as the absolute

value of the forecast error for the most recent consensus forecast prior to the event date. To

control for the potential effects of the timing of the 10-K, we include both the number of days 17 Note that bad news could affect our volume and spread results differently than could good news. As such, in untabulated tests, we control for the sign of the short-window return around the event filing and find qualitatively similar results.

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after the expected filing date (Daysafter_exp) and the number of days after the earnings

announcement (Daysafter_ea).18

Finally, we control for firm specific characteristics that are likely to affect trading activity.

We include Lnmv to control for the market value of the firm (Atiase 1985, Bamber 1987). To

control for prior performance, we include the firm’s return on assets (Roa). We include the ratio

of book value to market value of assets (Btm) to control for a firm’s growth opportunities, and

we include the log of the firm’s stock price (Lnprc) and the log of the number of shareholders

(Lnown) to control for other factors that may lead to differential trading effects.

4. Sample Selection, Research Design and Results

In this section, we discuss our sample selection procedures and examine the effects of

mandatory XBRL adoption on trading behavior. In particular, our primary set of tests

investigates the impact of XBRL on bid-ask spreads, price impact of trade and trading volume.

We then supplement these tests by examining the volume of large and small trades in Section 5.

4.1 Sample Selection

The SEC elected to phase-in the requirement to file interactive data over a three-year period,

where the largest filers are first to adopt the technology. Specifically, all domestic and non-U.S.

large accelerated filers that use U.S. GAAP and have a worldwide public float above $5 billion

were required to file a separate XBRL exhibit containing tagged financial statement items for the

first periodic report containing financial statements for fiscal periods ending on or after June 15,

18 Kross and Schroeder (1984) show that firms delay releasing bad news and the timeliness of that release affects the market reaction (Chambers and Penman 1984), while Asthana et al. (2004) highlight the importance of controlling for the delay after the earnings announcement.

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2009. We download all 10-K XBRL documents filed with the SEC starting June 15, 2009,

identifying 447 filings with fiscal periods between June 15, 2009 and June 14, 2010.19

We restrict our investigation to annual 10-K filings because Li and Ramesh (2009) document

that there is no significant stock market and volume reaction for quarterly filings, after

controlling for the concurrent release of earnings information. To ensure our 10-K filings have

significant investor interest, we examine the average volume and market reactions on the filing

days. In untabulated analyses, we find significantly positive abnormal volume and absolute

abnormal returns (mean = 2.5%, median = 1.8%) around the 10-K filing for both the XBRL and

non-XBRL firms in our sample, consistent with some set of investors processing the 10-K

filings.

To ensure that we are examining mandatory adoption of the regulation, we remove firms that

either filed XBRL documents in the prior year as part of the voluntary adoption program or had a

public float below the $5 billion cutoff, leaving us with 367 firms. We then obtain financial

information and market information for each firm-filing from Compustat, CRSP, and TAQ.

Requiring all sources of information for the adoption year and the prior fiscal year reduces our

XBRL sample of firms to 333. See Table 2 for details.

We restrict our focus to mandatory adopters for three reasons. First, voluntary XBRL filers

choose to adopt XBRL, which may introduce self-selection biases. Second, the SEC indicated

that the purpose of the voluntary program was to test the related format and technology and

warned investors that during this period “XBRL data should not be relied on for making

19 Note that the SEC allows initial adopters a 30-day grace period in which to file their XBRL documents. However, for our sample of mandatory XBRL adopters, only one firm filed its XBRL documents later than its 10-K filing. For this firm, we set the filing date for our tests equal to the date the XBRL documents were actually filed (one day later than the day the html 10-K was filed). Results are identical if this firm’s observations are excluded.

[17]

decisions about a company’s filing.”20 Third, unlike the firms in our sample where the filing of

the 10-K and XBRL occurred simultaneously, during the voluntary period, firms were more

likely to file their XBRL data much later. Specifically, the mean (median) delay is 69 (31) days,

with delays of up to one year for some firms’ filings, which causes great difficulty in drawing

inferences as the information has become quite stale.

In Table 3 Panel A, we break down the number of mandatory XBRL filers by one-digit SIC

industry codes. Overall, our results show that XBRL filers seem distributed across several broad

industry groups. Of note, the sample has large representation from manufacturing, financial,

transportation, and mining/construction industries. This broad representation should alleviate

concerns that our results are driven by any particular industry.

4.2 Research Design

Our XBRL sample of 666 firm-filings (333 firms with both a pre-XBRL filing and a post-

XBRL filing) is the starting point for our main tests. We use the year before the first phase-in

year of the mandate—i.e., June 15, 2008 through June 14, 2009—as the pre-XBRL period, since

this period represents the closest pre-XBRL time period of the same duration. This pre-period

enables us to determine whether there was a shift in trading behavior after the adoption of

XBRL. It is worth noting that there is an overlap between our pre-XBRL period and the 2007-

2008 financial crisis. As shown in Figure 1, the last six months of 2008 have relatively negative

returns and increased volume compared to the twelve-month period after XBRL adoption (i.e.,

post period). However, only 4% of the filings of the firms that adopted XBRL (8.5% (6.8%) of

the filings of the industry-size (industry-analyst) control firms that did not adopt XBRL) were

filed during the latter half of 2008, so the vast majority of the filings in our sample came after the

20 See http:// www.sec.gov/Archives/edgar/xbrl.html

[18]

2007-2008 financial crisis and the related erratic market behavior.21

To facilitate identification and further mitigate any potential impact of the financial crisis,

we take advantage of the XBRL mandate’s phase-in period. This phase-in provides a set of

XBRL adopting firms and a set of non-adopting firms, allowing us to implement a difference-in-

difference research design, where we compare changes in trading behavior across pre- and post-

adoption regimes for the mandatory XBRL adopters with those of our control groups. This

design choice mitigates the impact of market-wide non-XBRL related factors that may have

occurred during the transition period.

Using the staggered implementation of the mandate, we create two control samples of firms

with comparable information environments to those of XBRL firms, but that were not required to

file interactive reports. First, we choose control firms based on industry and size. For each

industry (defined using three-digit SIC classifications), we count the number of firms from the

XBRL sample in that industry and select an equivalent number of control firms, using the largest

market value as the selection criteria. Given that the requirement to file XBRL financial

statements is determined by a minimum market float, it is not surprising that the average size for

each industry comparison group in Panel B of Table 3 is smaller than the XBRL group.

However, it is important to point out that the average matched control firm is still a large

company with an average market float exceeding $3 billion, which is much larger than the

market float of all non-XBRL filers ($0.8 billion).

Second, we choose control firms based on industry and high analyst coverage in order to

obtain control firms with richer information environments. Panel B of Table 3 shows that the

average number of analysts for each of the two industry control groups (10 and 12 for the

21 Although we do not have clear predictions as to any potential bias induced by the financial crisis, as a robustness test, we remove firms with 10-K filings in the latter half of 2008 and rerun our analyses. We find qualitatively similar results, which are discussed in Section 6.

[19]

industry-size and industry-analyst groups, respectively) is close to the average for XBRL firms

(15.6), suggesting that the control groups have information environments that are reasonably

similar to the XBRL firms.

In addition to calculating abnormal trading behavior measures and using a difference-in-

difference design, where we match firms on size, industry and analyst following, we include both

market capitalization and analyst following in our regressions and a variety of other control

variables known to be correlated with market formation as discussed earlier in an attempt to rule

out alternative explanations and spurious correlations.

To examine the specific effects of XBRL on trading behavior, we estimate the following

OLS regression model:

MKT_VAR = α0 + α1POST + α2XBRL + α3POST*XBRL + αi∑CONTROLS + ε (1),

where MKT_VAR is defined as either abnormal bid-ask spread (ASPREAD), abnormal price

impact of trade (AIMPACT), or abnormal trading volume (AVOL). XBRL is an indicator variable

that equals one if the firm was required to adopt XBRL during the initial phase-in period, and

zero for control firms. POST is equal to one if the fiscal year ends on or after June 15, 2009,

while filings that occur in the year prior are coded as zero. The interaction term, POST*XBRL,

therefore captures the impact of XBRL on trading behavior (i.e., spread, price impact of trade

and/or trading volume). In addition, we cluster standard errors by firm.22 CONTROLS represents

the control variables defined in Section 3.

22 We cluster standard errors by firm to control for time-series correlation across a given firm’s two observations. We do not use firm fixed effects because including firm fixed effects forces us to exclude the XBRL indicator variable, as the two variables are linear combinations of each other. However, we rerun our main analyses using firm fixed effects, and the results are qualitatively similar. For cross-sectional correlation, the POST variable is equivalent to including time fixed effects using a fiscal year indicator variable, since there are only two years of observations. We do not cluster standard errors by time because there are only two years of observations and thus too few clusters (Petersen 2009, Gow, Ormazabal, and Taylor 2010). However, we cluster by the filing date as a robustness test, and we find qualitatively similar results. See Section 6 for details.

[20]

4.3 Results

Table 4 reports the multivariate regression results for the effects of XBRL on abnormal bid-

ask spreads (ASPREAD). Columns 1 and 2 (3 and 4) report the coefficient estimates and p-values

from estimating equation 1, where the matched sample is the industry-size (industry-analyst)

control group. The coefficient estimates on POST*XBRL are significantly positive across both

tests (p-values .012 and .008, respectively). Hence, we provide evidence that XBRL adopters

experience higher abnormal spreads relative to a matched sample of non-XBRL adopters. We

next examine the effect of XBRL on the abnormal price impact of trade (AIMPACT) in Table 5

and abnormal trading volume (AVOL) in Table 6. In Table 5, we find that the coefficient

estimates on POST*XBRL across both tests are significantly positive (both p-values <.01). This

evidence shows an increase in our price impact measure relative to both industry control groups,

which suggests that as liquidity dries up, investors’ unwillingness to trade results in greater price

pressure—i.e., investors’ ability to trade at a low cost declines. In Table 6, we further enhance

our understanding of shifts in trading behavior by examining trading volume. The results indicate

that abnormal trading volume decreases for XBRL firms relative to both industry control groups,

since coefficient estimates on POST*XBRL are significantly negative (both p-values <.01).

As previously discussed, our difference-in-difference design implemented in Tables 4, 5, and

6 enables us to account for any market-wide changes across the periods examined. As such, we

are primarily interested in the impact of the regulation on XBRL firms relative to our matched

groups (i.e., Post*XBRL). In order to observe the impact of the regulation on XBRL firms

irrespective of the control firms, we sum the coefficients on Post and Post*XBRL in each of the

previously discussed tables. The results in Table 4 show that for both samples there is a

significant overall increase in abnormal spreads after firms adopt XBRL (both p-values <.01).

The sum of the two coefficients in Table 5 are positive, but weaker, for both groups (p-values

[21]

.243 and .056, respectively) providing some weak evidence of reduced liquidity for XBRL firms.

Finally, the sum of Post and Post*XBRL in Table 6 is significantly negative across both tests (p-

values <.01), which indicates that abnormal volume decreased in XBRL firms after adoption.

Combined, the evidence in Tables 4, 5, and 6 is consistent with an increased concern of adverse

selection, where traders attempt to protect themselves from a potential information disadvantage

by requiring additional compensation for trading (higher bid-ask spread) and reducing their

trading (lower trading volume), which results in less liquidity.

5. Additional Analyses

5.1 Investor Type

We next attempt to understand whether the reduction in trading volume documented in

Section 4 stems from reductions in small and/or large investors. Although we cannot directly

identify these investors’ trades, prior literature (Lee 1992, Lee and Radhakrishna 2000, Barber,

Odean, and Zhu 2009) indicates that smaller trades can serve as a proxy for retail and smaller

institutional trading. However, recent research (Campbell, Ramadorai, and Schwartz 2009)

indicates that trade size may be a poor proxy for inferring trader type, particularly in more recent

years, as larger investors can break up their trades into many smaller trades. As such, we

recognize that these proxies are noisy and note that the results should be interpreted with caution.

To examine whether the mandate had a differential impact on traders, we use a seemingly

unrelated regressions (SUR) approach to estimate the following equations:

SML_AVOL = α0 + α1POST + α2XBRL + α3POST*XBRL + α∑CONTROLS + µ LRG_AVOL = β0 + β1POST + β2XBRL + β3POST*XBRL + β∑CONTROLS + ε (2),

[22]

where SML_AVOL (LRG_AVOL) represents abnormal trading for smaller (larger) investors and

standard errors are clustered by firm.23 We base our trade classifications on Bhattacharya (2001),

where trades are assumed to be made by small (large) investors if the dollar amount of the trade

is less than or equal to $10,000 (greater than or equal to $50,000).24 However, in order to keep a

sufficient buffer between small and large trades, we exclude firms with a share price greater than

$100 (i.e., the maximum small trade in our sample is $10,000).25 All control variables have been

previously defined.

Table 7 provides the SUR results for small and large investors’ trades. We find small

investors are less likely to trade shares of mandatory XBRL adopting firms around 10-K filings

(both p-values <.01). Interestingly, we find some evidence that large traders are also less likely

to trade shares of XBRL adopters relative to the control sample (p-values =.06 and <.01,

respectively).26 However, the reduction in trade is roughly two to four times greater for small

trades (all p-values <.01). This reduction in small trades is consistent with smaller investors

having increased concerns of adverse selection as a result of the new technology.

5.2 Falsification Test – Earnings Announcement Test

To rule out alternate explanations related to differences across XBRL and non-XBRL firms

during our sample period that are unrelated to XBRL adoption, we conduct our main analyses on

23 SML_AVOL (LRG_AVOL) is calculated in the same way as AVOL, using only small (large) trade volume characteristics. However, some of the firms do not have large trade volume during the pre-filing window or the event window, making the abnormal ratio undefined. In cases where the large trade volume is zero in both the event and pre- periods, we set the ratio equal to zero (since there was effectively no abnormal trading volume). In cases where the large trade volume is zero in the pre-period but not the event period, we drop the observation from the SUR regression, leaving Table 7 tests with slightly smaller sample sizes than in Table 3. 24 Consistent with prior literature, we also exclude the opening trade because it is often the sum of multiple orders and including it could add noise to the measures (Lee and Ready 1991; Lee 1992; Bhattacharya et al. 2007). Further, we follow Bhattacharya et al. (2007) and only include trades with a ‘regular sales’ condition code. 25 Eliminating medium-sized trades increases the power of the test, since large investors may try to break up their trades to disguise their identity (Kyle 1985; Meulbroek 1992; Barclay and Warner 1993) but for a variety of reasons are unlikely to make very small trades (Bhattacharya et al. 2007). 26 Perhaps the increased market frictions (higher spreads) and greater price impact of trading reduce larger investors’ ability and/or willingness to trade as actively around 10-K filings of XBRL firms.

[23]

a different, but related information event that does not incorporate XBRL: earnings

announcements. If there is something systematic to either the XBRL firms or the non-XBRL

control firms that drives the difference other than XBRL, then we should observe a similar

pattern around this alternate information event during the same time period.

For the firm-year observations in our sample, we identify the related earnings announcement

date and re-estimate the market variables using the (-1, +3) window around this date as the event

period.27 Panels A, B, C and D of Table 8 provide comparisons between our main results (using

10-K filings) and the falsification results (using earnings announcements) for abnormal spread,

abnormal price impact, abnormal volume, and abnormal small versus large trades, respectively,

around the events on XBRL adoption status. Table 8 shows that, in contrast to our main results,

none of the Post*XBRL coefficients are significantly different from zero in any of the

falsification tests. This lack of differences in abnormal spreads, abnormal price impact, and

abnormal volume (including large and small trades) between the XBRL and non-XBRL firms

around earnings announcements is consistent with XBRL adoption driving our main results.

5.3 Changes in Disclosure

We next consider the potential for changes in the quantity and/or complexity of information

in the financial statements as firms adopted XBRL. For example, Miller (2010) shows that 10-Ks

have become longer and more complex over recent years and that longer and more complex 10-

Ks result in lower trading volume as investor processing costs increase. Accordingly, we run our

main tests again and include controls for the quantity and/or complexity of the information in the

10-K. Specifically, we control for the log of the total number of words in the report (after

implementing cleaning techniques similar to those described by Miller (2010)) to capture the 27 To ensure that the earnings announcement event period was not affected by the XBRL filing, we drop observations where the filing date fell during the earnings announcement event window. After this change, the earnings announcement sample is approximately 1,000 observations, or 80% of the original filing sample.

[24]

amount of data in text format, the log of the sum of the number of words and the number of table

cells as a broader measure of the amount of information provided in the report (Miller 2010), and

the FOG score (as described in Li 2008) to capture the complexity or readability.

Table 9 provides the results of regressing abnormal spread, abnormal price impact, and

abnormal volume (including large and small trades) around 10-K filing dates on XBRL adoption

status, controlling for different measures of disclosure quantity or complexity. As shown, the

coefficient on Post*XBRL is consistent with our main results and remains significant in all test

specifications. We also note that the coefficient magnitude is very similar to our main results

across all specifications, providing support that a change in disclosure is not driving our

findings.28

6. Falsification and Robustness Tests

In this section, we discuss an additional falsification test and several robustness tests we

conduct related to our primary analyses.

6.1 Falsification Test – Alternative XBRL Cutoff Test

To help mitigate any additional concerns that firm size may be driving our results, we

provide a falsification test where we restrict our sample to XBRL adopters only and examine

changes in investor trading activity for the largest XBRL firms relative to the smallest XBRL

firms. If our results are driven by size rather than XBRL, we would expect to see a differential

impact on market frictions between large and small XBRL firms, similar to that which we

observed between XBRL and non-XBRL firms in our main analyses—i.e., higher abnormal

spreads, higher abnormal price impact and lower abnormal trading volume. To test this, we rerun

28 We also run the tests including all three disclosure variables simultaneously, and we again find (untabulated) that the Post*XBRL coefficient is consistent with our main findings, statistically significant, and similar in magnitude.

[25]

our main analyses using large XBRL firms (those with market floats above the median XBRL

market float) as the treatment sample and small XBRL firms as the control sample. Although

there is a significant difference in size across the two groups (mean market float of $24.1 billion

[$6.7 billion] for the large [small] XBRL firms), we find no difference in abnormal volume and

price impact, and a significant decrease in abnormal spread for the large XBRL firms compared

to the small XBRL firms. These findings provide additional support that the increase in market

frictions is related to XBRL adoption and not market float.

6.2 Robustness Tests

6.2.1 Alternative Control Groups

In our main analysis, we attempt to control for the difference in information environment

between XBRL and non-XBRL firms using industry-size and industry-analyst matches as well as

including size and information intermediary control variables in our tests. However, there may

be other factors more directly associated with float that impact a firm’s information environment.

For example, firms with more than $700 million float are considered ‘large accelerated filers’ by

the SEC and are subject to a 60-day (rather than 75-day or 90-day) 10-K filing deadline.

Accordingly, we construct our non-XBRL sample using firms that have a public market float of

at least $700 million. For symmetry, we also remove XBRL firms with a market float above $9.3

billion to bound the range to the $5 billion XBRL cutoff plus or minus $4.3 billion. Our results

continue to hold at statistically significant levels for this subsample of firms: abnormal spread

increases, abnormal price impact increases, and abnormal volume decreases.

We also perform two similar tests that use a subsample of XBRL and non-XBRL firms. For

the first test, we examine the smallest XBRL firms and the largest non-XBRL firms based on

market float. Specifically, we remove XBRL (non-XBRL) firms with floats above (below) the

[26]

median XBRL (non-XBRL) firm float, resulting in a mean market float of $6.6 billion ($2.0

billion) for the XBRL (non-XBRL) firms. For the second test, we examine the XBRL firms with

the smallest analyst coverage and the non-XBRL firms with the largest analyst coverage by

removing XBRL (non-XBRL) firms with the number of analysts above (below) the median

XBRL (non-XBRL) number of analysts. After these cuts, the average analyst following is 9.9

(9.0) for the XBRL (non-XBRL) firms. Although both of these tests reduce our sample by 50%,

our results continue to hold for both alternate sample cuts at statistically significant levels.

6.2.2 Alternative Measures of Information Asymmetry

Although we estimate information asymmetry using firms’ bid-ask spread, we acknowledge

that there may be components of spread that are unrelated to information asymmetry. Some

studies (e.g., Glosten and Harris 1988; Madhavan, Richardson, and Roomans 1997) attempt to

decompose spread into an information asymmetry component and a non-information asymmetry

component (which captures inventory holding and order processing costs). Unfortunately, Van

Ness, Van Ness, and Warr (2001) provide evidence that the spread decomposition methodologies

are weak at best. Given these concerns, we focus on overall spread as our primary measure of

asymmetry, but also rerun our tests using the information asymmetry component of spread, as

estimated following Akins, Ng, and Verdi (2012) and Armstrong, Core, Taylor, and Verrecchia

(2011).29 For both the Akins et al. (2012) and Armstrong et al. (2011) estimations, we find that

the information asymmetry component of spread increases for firms upon implementation of

XBRL, as compared to the set of matched firms. Specifically, the coefficients for both measures

are positive and significantly different from zero for the industry-size control group, while the 29 Akins et al. (2012) build on the Glosten and Harris (1988) model, and Armstrong et al. (2011) build on the Madhavan et al. (1997) model. For both measures of the information asymmetry component, we follow the empirical implementation using TAQ data as described in the appendices of Akins et al. (2012) and Armstrong et al. (2011), respectively, with the difference that we estimate the spread components for each firm-day. We then estimate abnormal measures around the filing dates using the same formula as our ASPREAD measure.

[27]

coefficients are positive for both measures for the industry-analyst control group, but statistically

insignificant at conventional levels.

6.2.3 Market Depth

In our main analyses, we use bid-ask spread as our proxy for information asymmetry.

However, liquidity suppliers can also address adverse selection concerns by adjusting the

number of shares they are willing to trade (Leuz and Wysocki (2008); Lee, Mucklow and Ready

1993). While both spread and depth can be used to address information asymmetries, it is not

necessary that both be used simultaneously. In other words, either reductions in spread or greater

depth alone can indicate reductions in information asymmetry, provided the alternate measure

does not counteract the effect (e.g., lower spread, but reduced depth as well), which would

suggest a substitution effect by liquidity suppliers (Lee, Mucklow and Ready 1993).

Accordingly, we examine firms’ market depths around the 10-K filings.

We measure abnormal depth as the log of the average daily depth during the event period

minus the log of the average daily depth during the non-filing period, where the daily depth is the

daily average of each quote’s depth, calculated as the sum of the dollar offer size and the dollar

bid size. We find that abnormal depths decline for our XBRL firms, as compared to both of our

matched sets of non-XBRL adopters. This provides further support that adverse selection

concerns have increased for XBRL-adopting firms.

6.2.4 Matched Group Indicator Variables

One of the strengths of our research design is the use of non-adopting firms as a control

group from which to compare XBRL firms. Specifically, we limit the control groups to non-

XBRL adopting firms in each 3-digit SIC industry of the XBRL firms. Cram, Karan, and Stuart

(2009) recommend including indicator variables for each pair or group of matched firms when

[28]

conducting a matched analysis. Although we use abnormal measures and include a variety of

control variables, it is possible that an unobservable effect within industries (our matching group)

remains. Therefore, we rerun our analyses including indicator variables for each 3-digit SIC (i.e.

industry fixed effects). We find that our results continue to hold at statistically significant levels.

6.2.5 Financial Crisis

We then examine whether our results are impacted by the 2007-2008 financial crisis. In our

main analyses, we use both abnormal market measures and a difference-in-difference design to

mitigate confounding effects; however, our pre-XBRL period overlaps with the very end of the

crisis. In particular, we have 26 XBRL firms and 55(44) industry-size (industry-analyst) matched

non-XBRL firms that file 10-Ks in the last half of 2008, which accounts for roughly 4% and 8%

of the XBRL firms’ and non-XBRL control firms’ filings, respectively. Given the extreme

market decline and tremendous uncertainty in 2007 and 2008, it is not clear what impact this

period may have on our market metrics. Accordingly, we rerun our analyses excluding firms that

filed in the latter half of 2008 to address any potential bias. We continue to find an increase in

abnormal spread, an increase in abnormal price impact, and a decrease in abnormal volume for

XBRL firms, as compared to both of our matched sets of non-XBRL firms.30

6.2.6 Two-Way Clustering by Firm and Date

In our main analyses, we cluster standard errors by firm to account for correlation in the

residuals across firm observations. We do not cluster by year because there are only two years in

our sample, and to create consistent estimates of standard errors, at least 10 to 50 clusters (i.e.

years, in this case) are recommended (Petersen (2009); Gow, Ormazabal, and Taylor 2010).

However, because our tests focus on market activity on filing dates and filing dates tend to

30 We also remove financial firms (i.e., firms in SIC 6000 thru 6999 industries) from our analyses and find qualitatively similar results.

[29]

cluster together over the year, there could be correlation in the residuals of firms filing on the

same dates. Therefore, we rerun our abnormal spread, price impact, and volume analyses using

standard errors clustered by firm and by filing date (two-way clustering), and we find that our

results continue to hold at statistically significant levels.31

7. Conclusion

In April 2009, the SEC mandated that companies report their financial statement data using

XBRL. The SEC asserts that this new search-facilitating technology will improve the usability of

financial disclosures, thereby reducing information asymmetry. Given investors’ differential

abilities to take advantage of the new technology, the impact of the regulation is unclear.

Although the SEC argues that XBRL provides more benefits to small investors than it does to

large investors, the potential for large traders to use their superior processing capabilities to

leverage the new technology for informational gains may actually disadvantage small investors.

In this study, we investigate the effects of the initial phase-in of the mandatory XBRL filing

requirement on market behavior—bid-ask spread, price impact of trade, and trading volume—

around 10-K filings.

Using a difference-in-difference design to examine market effects resulting from mandatory

XBRL adoption, we find greater market frictions for mandatory XBRL adopters as compared to

two matched sets of firms. In particular, we find evidence of greater abnormal bid-ask spreads,

increased abnormal price impact of trade, and lower abnormal trading volume for adopting firms

31 We also examine the timing of filings during the calendar year to ensure that the XBRL firms and the non-XBRL control firms are not clustered in different periods. For the XBRL firms, 85% of firms file in February and March. We find similar concentrations for our non-XBRL control firms: 77% of industry-size matched firms and 78% of industry-analyst matched firms file in February and March. Thus, there does not appear to be evidence of significant differential clustering between the groups. In addition, in a robustness test described in 6.2.1, we examine a bandwidth of firms that are large accelerated filers, and thus have the same filing requirements, and our results remain significant.

[30]

than for non-adopting firms after XBRL implementation. Further, we find that the reduction in

abnormal trading volume is much larger for small traders.

Our paper contributes along several dimensions. First, we provide initial evidence on the

impact of the XBRL mandate as the SEC shifts from the current EDGAR filing system to a fully

interactive data system. Second, our findings provide insight into the impact of a recent

innovation in information technology on the relation between disclosure and its effect on the

heterogeneity in investors’ information sets. In particular, although XBRL does not change the

disclosure itself in any way, the search-facilitating technology may exacerbate processing

differences across investors, as large investors are able to use their superior ability and/or

resources to leverage the technology when processing disclosures. Finally, we contribute to the

emerging literature examining the role of technology in firms’ communications with investors.

[31]

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APPENDIX – XBRL Overview

Financial statements are comprised of numbers that can provide information about a firm’s performance and value. To understand the basic meaning of a number, though, users of financial statements (including data aggregators) must read the contextual information surrounding the number. For example, a textual analysis language such as Perl, could find the number “167” in a set of financial statements, but the program would not know whether that number represents the firm’s sales, assets, number of employees, mailing address, or even the page number of the document. For a computer to understand the meaning of “167,” the user would have to program it to look before, after, above, and/or below for the description (e.g. “Product Sales”), the year (e.g. 2009), the currency (e.g. USD), the denomination (e.g. millions), etc. Because firms use a variety of formats, it is often difficult to automatically identify the relevant descriptions, or “understand” the number. In addition, comparing data items across firms can be difficult if firms use slightly different descriptions, such as “Net Sales – Product” instead of “Revenue from Products,” “Sales” or “Inventory Sales.” Essentially, computer software is dependent on users providing a set of rules that approximate the process of manually reading and assigning meaning to data items, and these rules are costly to create and not precise enough for all settings. Because of the difficulty of automating the “understanding” of data items, data aggregators only collect a subset of available information. However, the recent XBRL mandate provides an opportunity to improve the information collection process.

XBRL is an electronic language designed for business reporting that aids in computer-automated acquisition, classification, comparison, and representation of key information within reports. Essentially, companies can use XBRL to identify a specific data item within a report and obtain computer-readable information about the item (such as its name, relevant time period, and currency—e.g. Total Assets, 12/31/2009, USD) and about the item’s relationship to other items (e.g. Assets = Liabilities + Equity). By “tagging” each data item with this additional information, XBRL allows computer programs to “understand” the meaning of data items, thus enabling automated use and display of the information in a variety of formats, depending on the user’s preferences: comparing data across periods, comparing data across firms, highlighting certain types of accounts, etc. ADVANTAGES OF XBRL

The SEC discusses several advantages of XBRL in their 2009 Final Rule including: (1) more efficient processing, (2) a more comprehensive set of data than provided by aggregators, and (3) improved comparability across filings.32

First, the data is available in a less costly and timelier fashion. Once the setup costs have been incurred, the costs of processing the data in XBRL filings should be greatly reduced. Specifically, instead of hand-coding the information from html filings, investors can rely on computer-automated tools to access and compare data across many filings in a fraction of the time, leaving more time for ‘actual analysis.’ Christopher Whalen (2004), co-founder of

32 An additional benefit of XBRL lauded by the SEC is that the data should be more accurate than that provided by data aggregators or manually input by individuals. This benefit has been challenged by some noting that there are errors in some of the XBRL tags. According to McCann (2010), these errors affect about one percent of the tags, but the vast majority of the errors are easily fixable with a proper algorithm (e.g., incorrect sign). Although the cost is likely minor, the existence of these tagging errors is an additional cost for investors implementing the new technology.

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Institutional Risk Analytics, claims that "Putting financial data in ready-to-crunch condition might seem a trivial detail until you consider that Wall Street currently spends 80% of its time and money in data mining and 20% on actual analysis – what data pros call the ‘80-20 rule.’” Whalen adds that standardized data format could flip the 80-20 rule, allowing analysts to spend most of their time assessing rather than gathering financial data.

Second, the level of detail available to investors is more flexible and much richer with XBRL than with data aggregators. In fact, even during the first year of adoption when detailed tagging of footnotes was not fully implemented, many useful items tagged in the financial statements were not available through data services (e.g., Factset, Bloomberg, etc.). For example, revenue breakdown (e.g. products, services, financing), cost of revenue breakdown (e.g. cost of products, cost of services, financing interest), receivables breakdown (e.g. trade, financing, short & long term), and breakdowns of provision for bad debt and provision for inventory are available with XBRL, but not available through traditional data services. Since data aggregators do not capture all information in the filings, investors using XBRL filings are at a relative advantage. Below we illustrate the benefits of this additional detailed data by comparing HP and International Business Machines (IBM) financial disclosures.

For an investor interested in comparing HP and IBM for forecasting and valuation purposes, the first item of interest is typically revenue. If the investor uses information from a common data aggregator, they would be limited to total revenue in 2009 of $114,552 million and $95,758 million for HP and IBM, respectively (See Figure A1). However, utilizing XBRL tagging, investors can identify the revenue on the Consolidated Statements of Operations in the 10-K filing broken down by source (Goods, Service, and Financing). This information is useful to investors as they can now observe that 65% of HP’s revenues are from selling goods, while 40% of IBM’s revenues are from selling goods. This is but one detail provided by XBRL that is unavailable from data aggregators. In fact, the most recent U.S. GAAP taxonomy (2011) includes over 15,000 tags (and growing), compared to the several hundred that are typically captured by third-party aggregators (FASB, 2011).

Sales per Data Aggregator

Figure A1 – HP and IBM 2009 10-K Portions

Name: SalesRevenueGoodsNet Label: Products

Name: SalesRevenueGoodsNet Label: Sales

Sales per Data Aggregator

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In addition to quicker processing and a more comprehensive set of data items, the standardized tagging structure of XBRL also provides a way to compare information across firms. Given the precision and standardization of the tagging structure, investors no longer have to guess how to map items from one firm’s financial statements into others’ financial statements. For example, firms may sometimes differ in the naming convention of certain line items. As shown in Figure A1, HP identifies income earned from the sale of goods as “Products” revenue, while IBM labels it “Sales” revenue. The XBRL tag “SalesRevenueGoodsNet” reduces the uncertainty of what is included and allows for easy comparison. As another example, firms may group line items together differently than another firm (e.g., depreciation may be included in operating activities for some firms but not in others). The XBRL tagging structure allows investors to quickly access comparable items without having to manually adjust data groupings across firms for better comparisons—it has been done for them already. Allowing investors to acquire and process consistent financial information for all firms in an industry enables them to reliably compare across the firms as well as across time.

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FIGURE 1

S&P Pre and Post Period Returns and Volume

Pre-XBRL Period Post-XBRL Period

This figure shows volume and returns of the S&P 500 index from June 15, 2008 to June 15, 2010 (Source: ^GSPC Yahoo Finance).

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TABLE 1 Variable Definitions

Abs_abn_ret Market Return Absolute value of (FirmCumRetEvent - MktCumRetEvent), using the value-weighted average return for all CRSP firms as the market return, using data from CRSP

Abs_esurp Forecast Error Earnings surprise scaled by price as of the end of the same fiscal year. The forecast error is normally based on the most recent IBES consensus forecast prior to the event date. If IBES data is unavailable, we use the forecast error for the most recent First Call consensus forecast. If neither is available, we use the seasonal random walk earnings surprise using Compustat data.

AIMPACT Abnormal Price Impact

of Trade

Estimated from model 5 in (Goyenko et al. 2009, p.156) as the slope coefficient λ(TAQ) from the regression rn = λ(TAQ) · Sn + un, where for the nth five-minute period, rn is the stock return, Sn is the signed square-root dollar volume, and un is the error term. (Goyenko et al. 2009, p.156). Following Hasbrouck (2009), we multiply this coefficient by 106.

ASPREAD Abnormal Spread

(AvgDailySpreadEvent–AvgDailySpreadNon-Filing), where AvgDailySpread = Avg((OfferPrice-BidPrice)/((OfferPrice+BidPrice)/2))*100 for all valid quotes for the firm during the day, using data from TAQ

AVOL Abnormal Volume

((AvgDailyVolumeEvent– AvgDailyVolumeNon-Filing)/ StdDevDailyVolumeNon-Filing) based on TAQ data.

Btm Book to market measured as of the fiscal year end, using Compustat at/(at – ceq + (CRSP MVE))

Daysafter_ea Timing of Earnings

the number of calendar days the 10-K is filed after the preliminary earnings announcement date, based on Compustat earnings announcement dates

Daysafter_exp Timing of Filing

the number of calendar days the 10-K is filed after the expected filing date (last year’s 10-K filing date)

Event Event Period measured 1 day prior to the 10-K filing through 3 days after the filing

Lnanalyst Analyst Following

Log(1+Number of analysts), where the number of analysts is taken from the most recent consensus analyst forecast measurement date prior to the earnings announcement date (but not more than 90 days prior) using I/B/E/S (or FirstCall if data unavailable in I/B/E/S)

Inst_hold Institutional Holdings

Percent of shares outstanding held by institutions, as measured by Thomson Reuter’s Institutional Holdings database (Spectrum)

Lnmv Market Value Log(Market Value of Equity) as of the fiscal year end, using data from CRSP

Lnown Institutional Ownership

Log(1+Number of shareholders (Compustat cshr)) as of the current fiscal year-end

Lnprc Price Log(Price) as of the fiscal year end, using data from CRSP

LRG_AVOL Large Investors AVOL measured for large trades – includes trades greater than or equal to $50,000, using data from TAQ

Non-Filing Non-Filing Period

Measured 49 days prior to the 10-K filing through 5 days before the filing

POST Post Period Indicator variable equal to one if the 10-K filing occurs between June 15, 2009 and June 14, 2010, while filings that occur in the year prior are coded as zero

Qt1_depth Prior Quarter Depth

Log of quarter t-1 average of daily depths, where depth is the daily average of each quote’s depth, calculated as the sum of the dollar offer size and the dollar bid size

40

multiplied by 100 for all valid quotes for the firm during the day, using data from TAQ

Qt1_spread Prior Quarter Spread

Log of quarter t-1 average of daily spreads, where spread = Avg((OfferPrice-BidPrice)/((OfferPrice+BidPrice)/2))*100 for all valid quotes for the firm during the day, using data from TAQ

Qt1_turn Prior Quarter Turnover

Firm Turnover in quarter t-1 minus market turnover in quarter t-1, where turnover is the average daily share turnover, defined as the average daily dollar volume divided by the average market value of shares outstanding for the firm, using data from CRSP.

Qt1_volat Prior Quarter Stock-Return

Volatility

Standard deviation of Log(1+Daily Return), annualized by multiplying by √252, measured over quarter t-1, using data from CRSP

ROA Performance Operating earnings scaled by assets, defined as Compustat items (ib / at)

SML_AVOL Small Investors AVOL measured for small trades - includes trades less than or equal to $10,000, using data from TAQ

XBRL XBRL Adopters Indicator variable equal to one if the firm was mandated to adopt XBRL in the first year of adoption and zero otherwise

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TABLE 2 Sample Selection

This table provides details about the sample selection of mandatory XBRL firms.There are several items to note about sample selection. First, firms were technically required to file XBRL documents for fiscal periods ending on or after June 15, 2009, starting with 10-Q filings. Therefore, if a firm's first financial statement filing after June 15, 2009, was a 10-K filing, they were able to delay the XBRL filing until the first 10-Q filing. Accordingly, these firms are included in the non-XBRL sample since they were not required to and did not file XBRL documents. Second, the 36 filings dropped for being voluntary adopters in the mandatory period had market float of less than $5 billion for both fiscal year 2009 and 2008. 79 XBRL-adopting firms in our sample had public float above the $5 billion cutoff for fiscal year 2008, but not for fiscal year 2009. These firms were required to adopt XBRL for their 10-Q filings for fiscal quarters ending after June 15, 2009, since fiscal year 2008 market float determined their filing status. When they continued to file XBRL statements for fiscal year 2009 (even though their market float dropped below $5 billion), we count them as mandatory filers because they were required to file several XBRL 10-Q's prior to the 10-K and were required to file XBRL 10-Q's and 10-K's after June 15, 2010 as part of the second phase of implementers.

447

(44)

(36)

(34)

Total Mandatory XBRL Filings/Firms in Year of Adoption 333

XBRL 10-K's Filed for Fiscal Periods between 6/15/2009 and 6/15/2010

Less filings that have market float of less than $5 billion (i.e. voluntary adoption in mandatory period)

Less filings of firms that voluntarily filed XBRL in prior period

Less filings that don't have necessary Compustat, CRSP, and TAQ data for both pre and post years

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TABLE 3 Industry Breakdown and Descriptive Statistics

This table provides details about the industry breakdown of mandatory XBRL firms by one-digit SIC (Panel A) and descriptive statistics of XBRL and Non-XBRL control samples (Panel B). XBRL is the sample of XBRL adopters in our sample. All Non-XBRL represents all firms with available data that were not required to adopt XBRL during the first phase-in period and is provided as a basis for comparison. Industry & Size is the control sample based on the largest market value within an industry. Industry & Analyst is the control sample based on the highest analyst following within an industry. Market Float is the aggregate market value of equity held by non-affiliates as of the end of the second quarter of the fiscal year being reported, as disclosed in the 10-K filing. Market Value is the market value of all outstanding shares as of the end of the fiscal year. Assets is the total assets as of the end of the fiscal year (Compustat atq). Number of Analysts is the number of analysts covering the firm, taken from the most recent consensus analyst forecast measurement date in I/B/E/S (or FirstCall if data is not available in I/B/E/S) prior to the earnings announcement date.

Panel A - Industry Breakdown of XBRL Sample

One-Digit SIC Industry Name1 Mining and Construction 37 11.1%2 Light Manufacturing and Chemicals 51 15.3%3 Heavy Manufacturing 83 24.9%4 Transportation and Public Utilities 46 13.8%5 Wholesale and Retail Trade 26 7.8%6 Finance, Insurance, and Real Estate 62 18.6%7 Services 20 6.0%8 Health Services 5 1.5%9 Unclassified 3 0.9%

Total 333 100.0%

Panel B - Descriptives Across Samples

XBRLAll

Non-XBRLIndustry &

SizeIndustry &

Analyst Number of Firms 333 2,918 324 324Number of Observations 666 5,836 648 648

Market Float (in millions) (Mean) 15,959 843 3,030 2,641(median) (10,397) (261) (1,973) (1,452)

Market Value (in millions) (Mean) 15,170 929 3,403 2,798(median) (10,025) (309) (2,261) (1,523)

Assets (in millions) (Mean) 28,604 1,927 7,326 6,337(median) (14,076) (457) (2,987) (2,063)

Number of Analysts (Mean) 15.6 5.2 10.0 12.0(median) (15) (4) (10) (11)

Percent of Total

Number of XBRL Firms

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TABLE 4 Results of Regressing Abnormal Bid-Ask Spread around 10-K Filing Dates on XBRL Adoption

This table provides the results of regressing abnormal bid ask spread (ASPREAD) on an XBRL-adopter indicator, a post-/pre- period indicator, their interaction, and control variables, using 333 mandatory XBRL-adopting firms and the two Non-XBRL control groups (324 industry-size and industry-analyst matched firms), for 2008 and 2009. Two-sided p-values are provided in parentheses to the right of the coefficients. All variables are defined in Table 1 and are winsorized at the 1% and 99% level. Standard errors are robust and clustered by firm. “*”, “**”, and “***” indicate significance at the 10%, 5%, and 1% or lower levels, respectively.

Dependent Variable:Matched Sample:

ρ-Value ρ-ValueMain Variables

Post 0.0419 (0.296) 0.0607 (0.103)

XBRL – 0.0687 * (0.085) – 0.0718 ** (0.039)

Post * XBRL 0.1182 ** (0.012) 0.1049 *** (0.008)Control Variables

Lnmv – 0.0060 (0.886) 0.0212 (0.576)

Btm 0.0492 (0.465) 0.0546 (0.284)

Inst_Hold 0.0628 (0.292) 0.0772 (0.134)

Daysafter_exp 0.0061 * (0.050) 0.0041 (0.130)

Daysafter_ea – 0.0004 (0.633) 0.0010 (0.222)

Abs_abn_ret 0.6625 *** (0.003) 0.6839 ** (0.044)

Lnanalyst – 0.0169 (0.609) – 0.0027 (0.916)

Lnown – 0.1910 (0.137) 0.1285 (0.601)

Lnprc 0.0128 (0.714) – 0.0315 (0.217)

Qt1_turn – 2.3189 (0.192) – 1.0633 (0.499)

Qt1_volat 0.0536 (0.337) 0.0500 (0.373)

Qt1_depth 0.0412 (0.539) – 0.0248 (0.514)

Abs_esurp 0.0416 (0.719) 0.2018 (0.333)

Roa 0.1622 (0.490) 0.3892 *** (0.003)

Intercept – 0.5185 (0.348) – 0.0177 (0.944)

F-Test:

Post + Post*XBRL 0.1601 *** (0.000) 0.1656 *** (0.000)

NAdj. R-sq

Coefficient Coefficient

Abnormal Bid-Ask Spread (ASPREAD )Industry-Size Industry-Analyst

0.024 0.0451,314 1,314

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TABLE 5 Results of Regressing Abnormal Price Impact of Trade around 10-K Filing Dates on XBRL Adoption

This table provides the results of regressing abnormal price impact (AIMPACT) on an XBRL-adopter indicator, a post-/pre- period indicator, the interaction of the two indicators, and control variables, using a sample of 317 mandatory XBRL-adopting firms and each of the two Non-XBRL control groups (287 (294) industry-size (industry-analyst) matched firms), each with two years represented: 2008 and 2009. These samples are slightly smaller because we require observations to have trading during at least 2/3 of the 5-minute intervals during the trading day. Two-sided p-values are provided in parentheses to the right of the coefficients. All variables are defined in Table 1 and are winsorized at the 1% and 99% level. Standard errors are robust and clustered by firm. “*” indicates significance at the 10% level, “**” at the 5% level, and “***” at the 1% or lower level.

Dependent Variable:Matched Sample:

ρ-Value ρ-ValueMain Variables

Post – 0.2849 *** (0.001) – 0.2800 *** (0.003)

XBRL – 0.2232 *** (0.008) – 0.2862 *** (0.001)

Post * XBRL 0.3985 *** (0.001) 0.4541 *** (0.001)Control Variables

Lnmv – 0.0451 (0.405) – 0.0646 (0.194)

Btm 0.3630 * (0.083) 0.3951 ** (0.014)

Inst_Hold 0.1979 (0.209) 0.1630 (0.335)

Daysafter_exp 0.0034 (0.663) 0.0113 (0.143)

Daysafter_ea 0.0039 * (0.062) 0.0028 (0.244)

Abs_abn_ret 2.9982 ** (0.016) 4.5970 *** (0.002)

Lnanalyst – 0.0094 (0.889) 0.0695 (0.416)

Lnown – 0.0641 (0.823) 1.0682 (0.135)

Lnprc 0.0094 (0.856) 0.0318 (0.577)

Qt1_turn – 6.9130 (0.119) – 6.0643 (0.159)

Qt1_volat 0.1095 (0.681) 0.0638 (0.784)

Abs_esurp 0.4537 (0.330) 0.2863 (0.559)

Roa 0.6515 (0.455) 1.1631 ** (0.038)

Intercept – 0.1298 (0.845) – 0.3032 (0.605)

F-Test:

Post + Post*XBRL 0.1136 (0.243) 0.1741 * (0.056)

NAdj. R-sq

Abnormal Price Impact (AIMPACT )Industry-Size Industry-Analyst

0.087 0.121

Coefficient Coefficient

1,208 1,222

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TABLE 6 Results of Regressing Abnormal Volume around 10-K Filing Dates on XBRL Adoption Status

This table provides the results of regressing abnormal volume (AVOL) on an XBRL-adopter indicator, a post-/pre- period indicator, the interaction of the two indicators, and control variables, using 333 mandatory XBRL-adopting firms and the two Non-XBRL control groups (324 industry-size and industry-analyst matched firms), for 2008 and 2009. Two-sided p-values are provided in parentheses to the right of the coefficients. All variables are defined in Table 1 and are winsorized at the 1% and 99% level. Standard errors are robust and clustered by firm. “*”, “**”, and “***” indicate significance at the 10%, 5%, and 1% or lower levels, respectively.

Dependent Variable:Matched Sample:

ρ-Value ρ-ValueMain Variables

Post 0.2017 * (0.055) 0.3335 *** (0.001)

XBRL 0.3389 *** (0.001) 0.4438 *** (0.000)

Post * XBRL – 0.4204 *** (0.000) – 0.5724 *** (0.000)Control Variables

Lnmv – 0.0636 (0.319) – 0.0519 (0.383)

Btm – 0.3355 (0.143) – 0.2571 (0.192)

Inst_Hold – 0.0058 (0.969) 0.1273 (0.427)

Daysafter_exp 0.0027 (0.709) 0.0002 (0.976)

Daysafter_ea – 0.0334 *** (0.000) – 0.0331 *** (0.000)

Abs_abn_ret 4.9947 *** (0.000) 4.5302 *** (0.000)

Lnanalyst 0.1100 * (0.095) 0.0456 (0.617)

Lnown – 0.3947 (0.511) – 0.4560 (0.435)

Lnprc 0.1224 ** (0.037) 0.1433 ** (0.027)

Qt1_volat – 0.4133 *** (0.006) – 0.4084 *** (0.008)

Qt1_depth – 0.0733 (0.251) – 0.0346 (0.628)

Qt1_spread – 0.0345 (0.392) – 0.0153 (0.702)

Abs_esurp – 0.4321 (0.592) – 0.3731 (0.525)

Roa – 0.4827 (0.610) – 0.3269 (0.617)

Intercept 1.6703 ** (0.023) 0.9991 (0.147)

F-Test:

Post + Post*XBRL – 0.2187 *** (0.005) – 0.2389 *** (0.002)

NAdj. R-sq

Abnormal Volume (AVOL )Industry-Size Industry-Analyst

0.204 0.209

Coefficient Coefficient

1,314 1,314

46

TABLE 7 Results of Regressing Abnormal Large and Small Trading Volume around 10-K Filing Dates on XBRL

Adoption Status

This table provides the results of using seemingly unrelated regression to simultaneously regress abnormal large trading volume (LRG_AVOL) and abnormal small trading volume (SML_AVOL) on an XBRL-adopter indicator, a post-/pre- period indicator, their interaction, and control variables, using a sample of 324 mandatory XBRL-adopting firms and each of the two Non-XBRL control groups (317 (323) industry-size (industry-analyst) matched firms), for 2008 and 2009. These samples are slightly smaller because firms with zero large trades in the pre period have an undefined abnormal large trading volume ratio. If there are zero large trades in the event and pre periods, we set the abnormal ratio equal to zero. However, if there are large trades in the event window but zero in the pre period, we drop that firm's observations from the regressions because the ratio is undefined and there is not a reasonable proxy for its value. Two-sided p-values are provided in parentheses beneath the coefficients for variables of interest, and for parsimony, stars are provided to indicate coefficient significance for the remaining control variables. Chi-squared statistics and related p-values are provided for tests of the difference between the Post*XBRL coefficients for large versus small abnormal trading. All variables are defined in Table 1 and are winsorized at the 1% and 99% level. Standard errors are robust and clustered by firm. “*” indicates significance at the 10% level, “**” at the 5% level, and “***” at the 1% or lower level.

Main VariablesPost 0.0703 – 0.2812 *** 0.1902 ** – 0.1512

(0.448) (0.006) (0.042) (0.132)

XBRL 0.2607 *** 0.6404 *** 0.3842 *** 0.6688 ***(0.008) (0.000) (0.000) (0.000)

Post * XBRL – 0.2166 * – 0.6883 *** – 0.3338 *** – 0.7733 ***(0.060) (0.000) (0.003) (0.000)

Diff: χ2 statistic (p-value)

Control VariablesLnmv – 0.0275 0.0339 – 0.0567 – 0.0194Btm – 0.1877 0.2294 – 0.1529 0.2359Inst_hold – 0.1464 – 0.0769 – 0.0498 0.0579Daysafter_exp – 0.0078 0.0040 – 0.0071 0.0020Daysafter_ea – 0.0191 *** – 0.0391 *** – 0.0174 *** – 0.0381 ***Abs_abn_ret 4.9463 *** 7.8667 *** 5.1696 *** 8.7453 ***Lnanalyst 0.0066 – 0.0149 – 0.0325 – 0.0848Lnown – 0.0981 – 0.6381 – 0.0526 – 0.7831Lnprc – 0.0029 0.1842 *** 0.0533 0.2726 ***Qt1_volat – 0.3172 ** – 0.5235 *** – 0.3690 ** – 0.4672 **Qt1_depth 0.0221 – 0.2088 *** 0.0777 – 0.0609Qt1_spread – 0.0676 ** – 0.1245 *** – 0.0220 – 0.0981 **Abs_esurp – 0.3815 – 0.6775 – 0.3772 – 0.5176Roa 0.0602 – 0.0463 – 0.2087 – 0.1173Intercept 0.6587 2.6022 *** 0.0027 1.1534

χ2 test: Post + Post*XBRL – 0.1463 * – 0.4071 *** – 0.1436 * – 0.6221 ***(0.0839) (0.0000) (0.0945) (0.0000)

N

Industry-Size Industry-Analyst

1,282

Large Small

17.2 (.00) 15.1 (.00)

Large Small

1,294

47

TABLE 8 Falsification Test: Results of Regressing Abnormal Spread, Price Impact, and Volume around Earnings

Announcements on XBRL Adoption Status

This table provides a comparison of our main results in Tables 4 through 7, with the results from a falsification test, i.e. regressing abnormal spread (ASPREAD), abnormal price impact (AIMPACT), and abnormal volume (AVOL) around earnings announcements on an XBRL-adopter indicator, a post-/pre- period indicator, the interaction of the two indicators, and control variables, using 273 mandatory XBRL-adopting firms and the two Non-XBRL control groups (245 industry-size and industry-analyst matched firms) for 2008 and 2009. Two-sided p-values are provided in parentheses beneath the coefficients. All variables are defined in Table 1 and are winsorized at the 1% and 99% level. Standard errors are robust and clustered by firm. “*”, “**”, and “***” indicate significance at the 10%, 5%, and 1% or lower levels, respectively.

Panel A - Abnormal Bid-Ask Spread (ASPREAD )

Post * XBRL 0.1182 ** 0.1049 *** 0.0357 0.0278(0.012) (0.008) (0.515) (0.582)

Panel B - Abnormal Price Impact (AIMPACT)

Post * XBRL 0.3985 *** 0.4541 *** 0.1017 – 0.0236(0.001) (0.001) (0.337) (0.849)

Panel C - Abnormal Volume (AVOL )

Post * XBRL – 0.4204 *** – 0.5724 *** 0.1296 0.1178(0.000) (0.000) (0.410) (0.469)

Panel D - Abnormal Volume - Large and Small

Post * XBRL – 0.2166 * – 0.6883 *** 0.0199 0.1943(0.060) (0.000) (0.898) (0.200)

Diff: χ2 statistic (p-value)

Post * XBRL – 0.3338 *** – 0.7733 *** – 0.1019 0.1311(0.003) (0.000) (0.525) (0.408)

Diff: χ2 statistic (p-value) 2.38 (.12)

1.37 (.24)

Main Test Results Event: 10-K Filing

Falsification Test ResultsEvent: Earnings Announcements

Large SmallIndustry-SizeIndustry-Size

Large Small

Industry-AnalystLarge Small

17.2 (.00)

15.1 (.00)

Industry-Size Industry-Analyst

Industry-AnalystLarge Small

Industry-Size Industry-Analyst

48

TABLE 9 Results of Regressing Abnormal Spread, Price Impact, and Volume around 10-K Filing Dates on XBRL Adoption, Controlling for Disclosure

Panel A - Abnormal Bid-Ask Spread (ASPREAD )

Post * XBRL 0.1182 ** 0.1049 *** 0.1216 *** 0.1125 *** 0.1235 *** 0.1132 *** 0.1202 ** 0.1102 ***(0.012) (0.008) (0.008) (0.005) (0.008) (0.005) (0.011) (0.006)

Panel B - Abnormal Price Impact (AIMPACT)

Post * XBRL 0.3985 *** 0.4541 *** 0.4185 *** 0.4694 *** 0.4204 *** 0.4691 *** 0.4026 *** 0.4613 ***(0.001) (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) (0.001)

Panel C - Abnormal Volume (AVOL )

Post * XBRL – 0.4204 *** – 0.5724 *** – 0.4230 *** – 0.5709 *** – 0.4253 *** – 0.5720 *** – 0.4198 *** – 0.5711 ***(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Panel D - Abnormal Volume - Large and Small

Post * XBRL – 0.2166 * – 0.6883 *** – 0.2118 * – 0.6777 *** – 0.2130 * – 0.6797 *** – 0.2070 * – 0.6928 ***(0.060) (0.000) (0.070) (0.000) (0.068) (0.000) (0.080) (0.000)

Diff: χ2 (p-value)

Post * XBRL – 0.3338 *** – 0.7733 *** – 0.3259 *** – 0.7630 *** – 0.3280 *** – 0.7657 *** – 0.3322 *** – 0.7734 ***(0.003) (0.000) (0.005) (0.000) (0.005) (0.000) (0.004) (0.000)

Diff: χ2 (p-value)

Disclosure ComplexityDisclosure QuantityResults Controlling for Disclosure

Main Test Results

Small

17.5 (.00)

Industry-AnalystLarge Small

14.7 (.00)

16.2 (.00)

Industry-AnalystLarge Small

14.2 (.00)

Fog ScoreInd-Size Ind-Analyst

Industry-SizeLarge

Ln(Number ofWords and Table Cells)Ind-Size Ind-Analyst

Industry-SizeLarge Small

17.2 (.00)

15.1 (.00) 14.2 (.00)

16.1 (.00)

Industry-Analyst Industry-AnalystLarge Small Large Small

Industry-Size Industry-SizeLarge Small Large Small

Ln(Number of Words)Ind-Size Ind-Analyst Ind-Size Ind-Analyst

49

This table provides a comparison of our main results in Tables 4 through 7, with the results from running the same regressions and including variables controlling for disclosure. Ln(Number of Words) is the log of the number of words in the firm’s 10-K filing, Ln(Number of Words and Table Cells) is the log of the sum of the number of words and table cells in the firm’s 10-K filing. Fog score is the estimate of the readability of the firm’s 10-K filing, following Li (2008). For all three measures, we implemented cleaning techniques as described in Miller (2010). Two-sided p-values are provided in parentheses beneath the coefficients. All variables are defined in Table 1 and are winsorized at the 1% and 99% level. Standard errors are robust and clustered by firm. “*”, “**”, and “***” indicate significance at the 10%, 5%, and 1% or lower levels, respectively.