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.
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Notes
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.
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.
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.
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).
We create abnormal measures of trading activity using a control period to difference out normal trading activity.
See Plumlee and Plumlee (2008) for background information on the SEC's efforts to incorporate XBRL into its filing process.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
We use a SUR approach following prior research by Bhattacharya (2001) to compare estimates across equations.
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.
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.
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.
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.
We also repeat the tests including all three disclosure variables simultaneously and find very similar results.
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.
References
Akins, B., Ng, J., & Verdi, R. S. (2012). Investor competition over information and the pricing of information asymmetry. The Accounting Review, 81, 35–58.
Amihud, Y. (2002). Illiquidity and stock returns: Cross-section and time-series effects. Journal of Financial Markets, 5, 31–56.
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.
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.
Atiase, R. K. (1985). Predisclosure information, firm capitalization and security price behavior around earnings announcements. Journal of Accounting Research, 23, 21–36.
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.
Barclay, M., & Warner, J. (1993). Stealth trading and volatility: which trades move prices? Journal of Financial Economics, 34, 281–305.
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.
Bhattacharya, N. (2001). Investors’ trade size and trading responses around earnings announcements: an empirical investigation. The Accounting Review, 76, 221–244.
Bhattacharya, N., Black, E., Christensen, T., & Mergenthaler, R. (2007). Who trades on pro forma earnings information? The Accounting Review, 82, 581–619.
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.
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.
Campbell, J., Ramadorai, T., & Schwartz, A. (2009). Caught on tape: Institutional trading, stock returns, and earnings announcements. Journal of Financial Economics, 92, 66–91.
Chambers, A., & Penman, S. (1984). Timeliness of reporting and the stock price reaction to earnings announcements. Journal of Accounting Research, 22, 21–47.
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.
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.
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.
Financial Accounting Standards Board. (2011). FASB US GAAP financial reporting taxonomy technical guide: Version 2011. http://www.fasb.org/cs/ContentServer?site=FASB&c=Document_C&pagename=FASB/Document_C/DocumentPage&cid=1176157270813. Accessed July 5, 2012 .
Financial Executives International. (2011). FEI—CCR XBRL rulemaking request. Retrieved July 5, 2012 from the FEI’s website: http://www.financialexecutives.org/KenticoCMS/Knowledge-Center/Comment-Letters/FEI-CCR-XBRL-Rulemaking-Request.aspx.
Glosten, L. R., & Harris, L. E. (1988). Estimating the components of the Bid/Ask spread. Journal of Financial Economics, 21, 123–142.
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.
Goyenko, R. Y., Holden, C. W., & Trzcinka, C. A. (2009). Do liquidity measures measure liquidity? Journal of Financial Economics, 92, 153–181.
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.
Hirshleifer, D., & Teoh, S. H. (2003). Limited attention, information disclosure, and financial reporting. Journal of Accounting and Economics, 36, 337–386.
Hodge, F., Kennedy, J., & Maines, L. (2004). Does search-facilitating technology improve the transparency of financial reporting? The Accounting Review, 79, 687–703.
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.
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.
Kyle, A. (1985). Continuous auctions and insider trading. Econometrica, 53, 1315–1335.
Lee, C. (1992). Earnings news and small traders: An intra-day analysis. Journal of Accounting and Economics, 15, 265–302.
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.
Lee, C., & Radhakrishna, B. (2000). Inferring investor behavior: Evidence from TORQ data. Journal of Financial Markets, 3, 83–111.
Lee, C., & Ready, M. J. (1991). Inferring trade direction from intraday data. The Journal of Finance, 46, 733–746.
Leuz, C., & Verrecchia, R. E. (2000). The economic consequences of increased disclosure. Journal of Accounting Research, 38, 91–124.
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.
Li, E. X., & Ramesh, K. (2009). Market reaction surrounding the filing of periodic SEC reports. The Accounting Review, 84, 1171–1208.
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.
McCann, D. (2010). 18,000 tagging errors in XBRL filings so far: But that’s better than expected, say the SEC and XBRL US. Retrieved July 5, 2012 from the CFO.com website: http://www.cfo.com/article.cfm/14529555.
Merrill Corporation. (2012). Where does the market obtain XBRL data? Retrieved July 26, 2012 from Merrill disclosure solutions’ website: http://merrillcompliancesolutions.wordpress.com/2012/07/26/where-does-the-market-obtain-xbrl-data/.
Merton, R. C. (1987). A simple model of capital market equilibrium with incomplete information. The Journal of Finance, 42(3), 483–510.
Meulbroek, L. (1992). An empirical analysis of illegal insider trading. The Journal of Finance, 47(5), 1661–1699.
Miller, B. (2010). The effects of reporting complexity on small and large investor trading. The Accounting Review, 85, 1487–1519.
Petersen, M. A. (2009). Estimating standard errors in finance panel data sets: Comparing approaches. Review of Financial Studies, 22, 435–480.
Plumlee, D. R., & Plumlee, M. A. (2008). Assurance on XBRL for financial reporting. Accounting Horizons, 22, 353–368.
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.
Puckett, A., & Yan, X. (2011). The interim trading skills of institutional investors. The Journal of Finance, 66(2), 601–633.
Securities and Exchange Commission. (2005). XBRL voluntary financial reporting program on the EDGAR system. Release No. 33–8529. Retrieved August 13, 2013 from the SEC’s website: http://www.sec.gov/rules/final/33-8529.pdf.
Securities and Exchange Commission. (2008). Commission guidance on the use of company websites. Release No. 34-58288. Retrieved July 5, 2012 from the SEC’s website: http://www.sec.gov/rules/interp/2008/34-58288.pdf.
Securities and Exchange Commission. (2009). Interactive data to improve financial reporting. Retrieved July 5, 2012 from the SEC’s website: http://www.sec.gov/rules/final/2009/33-9002.pdf.
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.
Whalen, C. (2004). Opening the books: A new computer format for financial reporting could improve investment analysis. Barron’sOnline. (January 19, 2004). http://online.wsj.com/news/articles/SB107429714251690100.
XBRL U.S. GAAP Financial Taxonomy. (2011) Retrieved July 5, 2012 from the XBRL.US website: http://xbrl.us/taxonomies/Pages/US-GAAP2011.aspx.
Yohn, T. (1998). Information asymmetry around earnings announcements. Review of Quantitative Finance and Accounting, 11, 165–182.
Acknowledgments
We are grateful to two anonymous referees, Salman Arif, Daniel Beneish, John Core, Anna Costello, Patricia Dechow (Editor), 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, Ohio State University, Santa Clara University, University of California—Berkeley, University of Chicago, University of Florida, University of Miami, University of Michigan, University of North Carolina, University of Pennsylvania (Wharton), University of Southern California, University of Utah, Washington University 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, Brian Miller gratefully acknowledges financial support from the Arthur Weimer Faculty Fellowship, and Hal White gratefully acknowledges financial support from Ernst and Young.
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Appendices
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 (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, 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 its 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.Footnote 29
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 analysis. Christopher Whalen (2004), co-founder of 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 and 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 Hewlett-Packard (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, he or she would be limited to total revenue in 2009 of $114,552 million and $95,758 million for HP and IBM, respectively (see Fig. 4). However, using 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).
HP and IBM 2009 10-K portions
In addition to quicker processing and a more comprehensive set of data items, the standardized tagging structure of XBRL 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 Fig. 4, 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 about what is included and allows for easy comparison. As another example, firms may group line items together differently (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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Blankespoor, E., Miller, B.P. & White, H.D. Initial evidence on the market impact of the XBRL mandate. Rev Account Stud 19, 1468–1503 (2014). https://doi.org/10.1007/s11142-013-9273-4
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DOI: https://doi.org/10.1007/s11142-013-9273-4