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Review of Accounting Studies
Erschienen in: Review of Accounting Studies 4/2014

01.12.2014

Initial evidence on the market impact of the XBRL mandate

verfasst von: Elizabeth Blankespoor, Brian P. Miller, Hal D. White

Erschienen in: Review of Accounting Studies | Ausgabe 4/2014

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Abstract

In 2009, the SEC 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, if some larger investors can 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 find evidence of higher abnormal bid-ask spreads for XBRL adopting firms around 10-K filings in the year 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. Additional analyses suggest, however, that these effects may be declining somewhat in more recent years. Collectively, our evidence suggests that a reduction in investors’ data aggregation costs may not have served its intended purpose of leveling the informational playing field, at least during the initial years after mandatory adoption.
Vorheriger Artikel Valuation of tax expense
Nächster Artikel Analyst information production and the timing of annual earnings forecasts

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Fußnoten
1
Although there is a broad spectrum of investor types, we characterize investors as either small (e.g., retail investors or small institutions) or large (e.g., large institutions) for parsimony, where small investors are relatively less sophisticated—i.e., they have relatively fewer resources, abilities, or both—as compared to larger investors. In our empirical analyses, we use trade size as a proxy for investor sophistication following prior research.
 
2
For example, Campbell Pryde, CEO and president of XBRL US, indicates that large institutional investors, such as mutual funds and hedge funds, are using XBRL to fill gaps in their datasets (Merrill Corporation 2012). This is not surprising given the tremendous amount of resources these institutions invested in data collection prior to XBRL adoption. As 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.
 
3
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.
 
4
Consistent with the possibility that the market impact of XBRL is minimal, a Columbia University survey of 26 analysts and investors indicates that only 8 % of the respondents use XBRL-formatted data in their models, analyses, or both. The study also notes that “those using XBRL data are using it for the perceived informational advantage they have by having interactive access to certain types of data that they believe they cannot collect elsewhere with the same effectiveness and efficiency” (Harris and Morsfield 2012, p. 61).
 
5
We create abnormal measures of trading activity using a control period to difference out normal trading activity.
 
6
See Plumlee and Plumlee (2008) for background information on the SEC's efforts to incorporate XBRL into its filing process.
 
7
Although XBRL is designed to facilitate comparability across firms, it must be applied consistently across firms. To promote greater consistency, both the FASB and XBRL US have established best practice suggestions for XBRL tagging. However, to the extent there are inconsistencies across firms, investors may find XBRL less useful. See the appendix for more discussion of XBRL and its intended benefits.
 
8
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.
 
9
The cost of developing or modifying the appropriate software to take advantage of XBRL during our sample period is not trivial because of the shortage of quality XBRL analysis software (SEC 2009, XBRL 2011). Although the availability of viewers may 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.
 
10
Some studies (e.g., Glosten and Harris 1988; Madhavan et al. 1997) attempt to decompose spread into an information asymmetry component and a non-information asymmetry component. Van Ness et al. (2001) provide evidence that spread decomposition methodologies are weak at best, so we focus on overall spread as our primary measure of information asymmetry. However, we also repeat our tests using abnormal measures of the information asymmetry component of spread, as estimated following Akins et al. (2012) and Armstrong et al. (2011). For both estimation methods, we find that the information asymmetry component of spread increases for XBRL firms relative to those for matched non-XBRL firms, with three of the four specifications significantly different from zero.
 
11
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 <90 % of the mid-point price.
 
12
We repeat our main analyses using raw (unwinsorized) variables, as well as eliminating observations with extreme DFBETAs as defined by Belsley et al. (1980) (i.e., those with DFBETA values >2/√n) separately for each regression and winsorizing variables at 1 and 99 %. The results are qualitatively similar.
 
13
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 et al. 2009, which incorporates both volume and price.
 
14
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.
 
15
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.
 
16
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 filed (one day later than the day the html 10-K was filed). Results are identical if this firm’s observations are excluded.
 
17
Given these potential concerns, we do not formally incorporate voluntary filers into our primary analyses. However, in untabulated tests, we repeat our main analyses after including a voluntary filer indicator, XBRL_Voluntary, and interact it with Post to determine whether the effects of XBRL adoption differ across voluntary and mandatory adopters. We find no significant difference between voluntary and mandatory adopters (i.e., Post*XBRL_Voluntary is not statistically significant across all four of our main tests—spread, price impact, volume, and large/small volume). However, since there are only 69 voluntary adopters, it is difficult to conclude whether there really is no difference or whether there is not sufficient power to determine the difference.
 
18
We do not have clear predictions as to any potential bias induced by the financial crisis, especially given our use of abnormal market measures and a difference-in-difference design. Nevertheless, as a robustness test, we remove firms with 10-K filings in the latter half of 2008 and repeat our analyses. We find qualitatively similar results: an increase in abnormal spread, increase in abnormal price impact, and decrease in abnormal volume for XBRL firms relative to matched sets of non-XBRL firms. In addition, we remove financial firms (i.e., firms in SIC 6000 thru 6999 industries) from our analyses and find qualitatively similar results.
 
19
Although we use a difference-in-difference design and abnormal measures as well as include a variety of control variables, an unobservable effect within industries (our matching group) may remain. Therefore, in untabulated analyses, we repeat our analyses including indicator variables for each three-digit SIC (i.e., industry fixed effects) following Cram et al. (2009) and find that our results still hold at statistically significant levels.
 
20
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 repeat 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 et al. 2010). However, we cluster by the filing date as a robustness test, and we find qualitatively similar results.
 
21
In addition to increasing bid-ask spread, liquidity suppliers can address adverse selection concerns by adjusting the number of shares they are willing to trade (Leuz and Wysocki 2008; Lee et al. 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 nonfiling 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 result provides further evidence that adverse selection concerns have increased for XBRL-adopting firms around 10-K filings.
 
22
We use a SUR approach following prior research by Bhattacharya (2001) to compare estimates across equations.
 
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 research, 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. Finally, since trade size for institutional investors has been decreasing over time (Campbell et al. 2009), we repeat our tests using alternate small investor trade size cutoffs of $5,000 and $7,000 and find qualitatively similar results.
 
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 or willingness to trade as actively around 10-K filings of XBRL firms.
 
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.
 
28
We also repeat the tests including all three disclosure variables simultaneously and find very similar results.
 
29
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. 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 1 % 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.
 
Literatur
Akins, B., Ng, J., & Verdi, R. S. (2012). Investor competition over information and the pricing of information asymmetry. The Accounting Review, 81, 35–58.CrossRef
Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5, 31–56.CrossRef
Armstrong, C. S., Core, J. E., Taylor, D. J., & Verrecchia, R. E. (2011). When does information asymmetry affect the cost of capital? Journal of Accounting Research, 49, 1–40.CrossRef
Asthana, S., Balsam, S., & Sankaraguruswamy, S. (2004). Differential response of small versus large investors to 10-K filings on EDGAR. The Accounting Review, 79, 571–589.CrossRef
Atiase, R. K. (1985). Predisclosure information, firm capitalization and security price behavior around earnings announcements. Journal of Accounting Research, 23, 21–36.CrossRef
Bamber, L. S. (1987). Unexpected earnings, firm size, and trading volume around quarterly earnings announcements. The Accounting Review, 62, 510–532.
Bamber, L., Barron, O., & Stober, T. (1997). Trading volume and different aspects of disagreement coincident with earnings announcements. The Accounting Review, 72, 575–597.
Bamber, L., & Cheon, Y. (1995). Differential price and volume reactions to accounting earnings announcements. The Accounting Review, 70, 417–441.
Barber, B., Odean, T., & Zhu, N. (2009). Do retail trades move markets? The Review of Financial Studies, 22, 151–186.CrossRef
Barclay, M., & Warner, J. (1993). Stealth trading and volatility: which trades move prices? Journal of Financial Economics, 34, 281–305.CrossRef
Bartley, J., Chen, Y., & Taylor, E. (2010). Avoiding common errors of XBRL implementation. Journal of Accountancy, 209, 46–51.
Belsley, D. A., Kuh, E., & Welsch, R. E. (1980). Regression diagnostics: Identifying influential data and sources of collinearity. New York: John Wiley and Sons.CrossRef
Bhattacharya, N. (2001). Investors’ trade size and trading responses around earnings announcements: an empirical investigation. The Accounting Review, 76, 221–244.CrossRef
Bhattacharya, N., Black, E., Christensen, T., & Mergenthaler, R. (2007). Who trades on pro forma earnings information? The Accounting Review, 82, 581–619.CrossRef
Blankespoor, E., Miller, G. S. and White, H. (2014). The role of dissemination in market liquidity: Evidence from firms’ use of twitter. The Accounting Review, 89(1).
Bloomfield, R. (2002). The ‘incomplete revelation hypothesis’ and financial reporting. Accounting Horizons, 16, 233–243.CrossRef
Bushee, B., Matsumoto, D., & Miller, G. (2003). Open versus closed conference calls: The determinants and effects of broadening access to disclosure. Journal of Accounting and Economics, 34, 149–180.CrossRef
Campbell, J., Ramadorai, T., & Schwartz, A. (2009). Caught on tape: Institutional trading, stock returns, and earnings announcements. Journal of Financial Economics, 92, 66–91.CrossRef
Chambers, A., & Penman, S. (1984). Timeliness of reporting and the stock price reaction to earnings announcements. Journal of Accounting Research, 22, 21–47.CrossRef
Cohen, K. J., Maier, S. F., Schwartz, R. A., & Whitcomb, D. K. (1986). The microstructure of securities markets. Englewood Cliffs, NJ: Prentice-Hall.
Cram, D. P., Karan, V., & Stuart, I. (2009). Three threats to validity of choice-based and matched-sample studies in accounting research. Contemporary Accounting Research, 26, 477–516.CrossRef
Daske, H., Hail, L., Leuz, C., & Verdi, R. (2008). Mandatory IFRS reporting around the world: Early evidence on the economic consequences. Journal of Accounting Research, 46, 1085–1142.
Diamond, D., & Verrecchia, R. (1991). Disclosure, liquidity, and the cost of capital. The Journal of Finance, 46, 1325–1359.CrossRef
Efendi, J., Park, J.D. and Subramaniam, C. (2010). Do XBRL Reports have incremental information content? An empirical analysis. Working paper, The University of Texas at Arlington and Towson University.
Glosten, L. R., & Harris, L. E. (1988). Estimating the components of the Bid/Ask spread. Journal of Financial Economics, 21, 123–142.CrossRef
Gow, I. D., Ormazabal, G., & Taylor, D. J. (2010). Correcting for cross-sectional and time-series dependence in accounting research. The Accounting Review, 85, 483–512.CrossRef
Goyenko, R. Y., Holden, C. W., & Trzcinka, C. A. (2009). Do liquidity measures measure liquidity? Journal of Financial Economics, 92, 153–181.CrossRef
Grossman, S., & Stiglitz, J. (1980). On the impossibility of informationally efficient markets. American Economic Review, 70, 393–407.
Harris, L. (1990). Liquidity, trading rules and electronic trading systems. New York University Monograph Series in Finance and Economics, Monograph No. 1990-4.
Harris, T., and S. Morsfield (2012). An evaluation of the current state and future of XBRL and interactive data for investors and analysts. White Paper, Columbia Business School.
Hasbrouck, J. (2009). Trading costs and returns for U.S. equities: Estimating effective costs from daily data. The Journal of Finance, 64, 1445–1477.CrossRef
Hirshleifer, D., & Teoh, S. H. (2003). Limited attention, information disclosure, and financial reporting. Journal of Accounting and Economics, 36, 337–386.CrossRef
Hodge, F., Kennedy, J., & Maines, L. (2004). Does search-facilitating technology improve the transparency of financial reporting? The Accounting Review, 79, 687–703.CrossRef
Kalay, A. (2011). Investor sophistication, disclosure and the information environment of the firm. Dissertation, The University of Chicago.
Kim, O., & Verrecchia, R. E. (1994). Market liquidity and volume around earnings announcements. Journal of Accounting and Economics, 17, 41–67.CrossRef
Kross, W., & Schroeder, D. (1984). An empirical investigation of the effect of quarterly earnings announcement timing on stock returns. Journal of Accounting Research, 22, 153–176.CrossRef
Kyle, A. (1985). Continuous auctions and insider trading. Econometrica, 53, 1315–1335.CrossRef
Lee, C. (1992). Earnings news and small traders: An intra-day analysis. Journal of Accounting and Economics, 15, 265–302.CrossRef
Lee, C., Mucklow, B., & Ready, M. J. (1993). Spreads, depths, and the impact of earnings information: An intraday analysis. The Review of Financial Studies, 6, 345–374.CrossRef
Lee, C., & Radhakrishna, B. (2000). Inferring investor behavior: Evidence from TORQ data. Journal of Financial Markets, 3, 83–111.CrossRef
Lee, C., & Ready, M. J. (1991). Inferring trade direction from intraday data. The Journal of Finance, 46, 733–746.CrossRef
Leuz, C., & Verrecchia, R. E. (2000). The economic consequences of increased disclosure. Journal of Accounting Research, 38, 91–124.CrossRef
Leuz, C. and Wysocki, P. (2008). Economic consequences of financial reporting and disclosure regulation: A review and suggestions for future research. Working paper, University of Chicago and University of Miami.
Li, F. (2008). Annual report readability, current earnings, and earnings persistence. Journal of Accounting and Economics, 45, 221–247.CrossRef
Li, E. X., & Ramesh, K. (2009). Market reaction surrounding the filing of periodic SEC reports. The Accounting Review, 84, 1171–1208.CrossRef
Madhavan, A., Richardson, M., & Roomans, M. (1997). Why do security prices change? A Transaction-level analysis of NYSE stocks. The Review of Financial Studies, 10, 1035–1064.CrossRef
Merton, R. C. (1987). A simple model of capital market equilibrium with incomplete information. The Journal of Finance, 42(3), 483–510.CrossRef
Meulbroek, L. (1992). An empirical analysis of illegal insider trading. The Journal of Finance, 47(5), 1661–1699.CrossRef
Miller, B. (2010). The effects of reporting complexity on small and large investor trading. The Accounting Review, 85, 1487–1519.CrossRef
Petersen, M. A. (2009). Estimating standard errors in finance panel data sets: Comparing approaches. Review of Financial Studies, 22, 435–480.CrossRef
Plumlee, D. R., & Plumlee, M. A. (2008). Assurance on XBRL for financial reporting. Accounting Horizons, 22, 353–368.CrossRef
Premuroso, R. F., & Bhattacharya, S. (2008). Do early and voluntary filers of financial information in XBRL format signal superior corporate governance and operating performance? International Journal of Accounting Information Systems, 9, 1–20.CrossRef
Puckett, A., & Yan, X. (2011). The interim trading skills of institutional investors. The Journal of Finance, 66(2), 601–633.CrossRef
Van Ness, B. F., Van Ness, R. A., & Warr, R. S. (2001). How well do adverse selection components measure adverse selection? Financial Management, 30, 77–98.CrossRef
Yohn, T. (1998). Information asymmetry around earnings announcements. Review of Quantitative Finance and Accounting, 11, 165–182.CrossRef
Metadaten
Titel
Initial evidence on the market impact of the XBRL mandate
verfasst von
Elizabeth Blankespoor
Brian P. Miller
Hal D. White
Publikationsdatum
01.12.2014
Verlag
Springer US
Erschienen in
Review of Accounting Studies / Ausgabe 4/2014
Print ISSN: 1380-6653
Elektronische ISSN: 1573-7136
DOI
https://doi.org/10.1007/s11142-013-9273-4

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