EXTENDED XBRL TAXONOMIES AND FINANCIAL ANALYSTS' INFORMATION

Transcription

1 EXTENDED XBRL TAXONOMIES AND FINANCIAL ANALYSTS' INFORMATION Joseph Johnston Department of Accountancy City University of Hong Kong First Draft: May 5 th 2015 This Draft: April 14 th 2016 Preliminary Draft Please do not quote The author thanks workshop participants at the 2015 NTU-SUFE-CityU Research Summer camp, Airlangga University, Sun Yat-Sen University, Louisiana State University, Nanjing University, the 2015 Pre-ICIS workshop on Accounting Information Systems, and the 2016 AAA AIS mid-year meeting. I am especially grateful for Brian Lam for his excellent research assistance. The work described in this paper was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (CityU ). All errors remain my own.

2 Extended XBRL taxonomies and Financial Analysts' Information ABSTRACT: In this study, I investigate how the use of extended tags in extensible Business Reporting Language (XBRL) filings is associated with the firm s information environment. XBRL allows firms to tag data in their financial reports. Prior research suggests that XBRL in general reduces information processing costs. However, the SEC allows firms to provide customize tags in their XBRL filings which, on the one hand, may allow firms to customize financial statements to provide more firms specific information by highlighting financial statement items that are not included in the standard set of tags. On the other hand, customized tag may inhibit information search, obfuscate financial statement numbers, and ultimately reduce the usefulness of XBRL data. Since the use of extended tags is related to the complexity of the firm, I employ an instrumental variables technique to control for endogeneity. Using financial analysts earnings forecasts error and forecast dispersion as proxies for the firm's information environment, I find extensions are negatively related to analysts forecast error and dispersion. In additional analysis, I find that the association between extension rate and analysts information does not vary with earnings management variables but is stronger for firm with more uncertain information environments and for firms that are more comparable to their industry peers. Furthermore, extension rate and analysts information is insignificant for filings with technical errors. This study is relevant to regulators creating policies about XBRL extensions, both in the US and around the world, in suggesting that on average, extensions are informative to users of the financial statements. 2

3 I. Introduction In this study, I examine the association between a firm s use of extended tags 1 in their extensible Business Reporting Language (XBRL) filings and the firm s information environment, as measured by financial analysts forecast error and forecast dispersion. Recently, the SEC has required firms to submit their filings using XBRL with the goal of decreasing information processing costs and improving investor information by standardizing financial statement items. The literature generally is consistent with the benefit of decreased information processing costs but the goal of standardization is less well researched. Standardization is achieved by applying standard tags to financial statement items that should be interpreted in a similar manner. 2 To allow firms to disclose line items that do not have an official standardized tag, the SEC permits firms to extend the standard XBRL taxonomy. While on the one hand, these extended tags could provide investors with important firm-specific information, there are potential issues in how the firm might use such extended tags. Extended tags may be used to hide earnings management (Lim, Kim, and Kim 2013), used erroneously (Debreceny et al 2005, Debreceny, Farewell, Piechocki, Felden, and Graning 2010, Debreceny, Farewell, Piechocki, Felden, Graning, and d Eri 2011), or may compromise comparability (Dhole, Lobo, Mishra, and Pal 2015). I investigate how the use of extended XBRL tags is associated with financial analysts information. This study is motivated by concerns that company specific extensions to the official SEC taxonomy reduce the comparability of XBRL filings, eliminating one of the key benefits of XBRL. A 2011 survey by the CFA institute found that 88 percent of the respondents preferred to limit or eliminate company-specific tags in favor of having more comparability between 1 An example of the use of XBRL extensions is given in Appendix A. 2 For example, one firm may refer to operating income as operating profits while another may refer to it as income from operations. Using XBRL, both firms could tag this item with the official tag ( OperatingIncomeLoss ), avoiding any confusion of semantics. The use of labels in XBRL could facilitate the different word choices while still preserving the underlying meaning of the words (SEC 2009). 3

4 firms (CFA Institute 2011). The CFA XBRL Guide for Investors also notes that extended tags compromise comparability but recognizes that the flexibility of extended tags allows companies to convey company-specific information in a meaningful way. The SEC, in their rule mandating XBRL, recognizes this issue as well and noted that the standard taxonomy may not include all the items needed by companies within particular industries. The SEC also notes that allowing for firms to create their own tags could improve the interpretation of these line items by investors and data aggregators. In the SEC Staff Observations from Review of Interactive Data Financial Statements, the SEC notes the correct way of using extended items, more recently focusing on use of extended tags when a standard tag or a close equivalent already exists. (SEC 2009, 2010) Understanding the association between extended tags and financial analysts information allows regulators and practitioners to infer whether extended tags convey useful information or make XBRL documents more difficult to use. The results of my study are relevant to regulatory bodies that currently maintain XBRL regulations as well as jurisdictions interested in implementing XBRL. Jurisdictions vary widely on their use of extensions in company filings. The US allows firms to use extended tags when a standard tag for a needed concept does not exist. In contrast, India s Ministry of Corporate Affairs requires companies to choose only the standard tag or, if a standard tag doesn t exist, the next-best-fit element, effectively eliminating the use of extended tags. (Ministry of Corporate Affairs, Government of India 2012). Examining how the use of extended tags affects analysts information allows these regimes to revise their regulations accordingly. Furthermore, regimes that may be considering implementing XBRL can make more informed decisions regarding the use of extended tags. Thus, my study is particularly useful to regulators around the world. The use of extensions is very likely affected by the informational demands of the firms. Firm with more uncertain information ex-ante may have a greater demand for non-standard 4

5 items. This uncertainty may also be reflected in analysts forecast error and forecast dispersion. Therefore the use of extension is not exogenous to firm s information environment. I address this endogeneity using instrumental variables (IV) regressions where the main instrumental variable is the number of standard items defined in the US GAAP used by the firm. This allows me to analyze how the firm s use of extensions affect analysts forecasts error and dispersion controlling for endogeneity. I examine the extension rates at several different levels. First, I examine the extension rate for the complete filing, which is similar to the rate used by other studies. I then make use of a feature of the XBRL filings that allows me to identify and separate out the different sections of the quarterly filling. I measure the extension rates for sections identified as financial statements and as footnote disclosures separately. Finally, I measure extension rates within the balance sheet, the cash flow statement, and the comprehensive income statement. Using a sample of 21,800 quarterly filings over the period 2010 to 2014, I find that XBRL extension rate is positively associated with analysts forecast error and analysts forecast dispersion using OLS but negatively associated with analysts forecast error and analysts forecast dispersion when using IV. I find similar results when I examine the extension rate at the statement level and at the footnote disclosure level and, by comparing the standardized coefficients, I find extensions in the financial statements generally have a stronger relation with analysts forecast error and dispersion. When I examine the individual financial statements, I find a consistently negative relation between analysts forecast error and dispersion and extension rates within the balance sheet, cash flow statement, and income statement: I also find that the cash flow statement generally has the largest standardized coefficient. Overall, the evidence suggests that more extensions are associated with a better information environment and are consistent with the use of extensions being, on average, informative rather than an obstacle to information collection. 5

6 In additional analysis, I examine the cross-sectional variability of the association between analysts forecast error and dispersion. Several different firm level factors may influence the association between extension rate and analysts information. First, if extensions capture the disclosure of firm-specific information, then we would expect firms that operate in more uncertain environments to disclose more firm-specific information through extensions. Second, if extensions are used erroneously, we would expect a stronger relationship between extensions and analysts forecasts when there are errors in XBRL filing. Third, if XBRL extensions compromise comparability, then we would expect a weaker association between extensions and analysts information when firms are more comparable. Finally, if firms are using extensions to hide earnings management, then we would expect a weaker relationship between extension rates and analysts information when firms have large abnormal accruals. I find a stronger negative association between XBRL extension rates and analysts forecast error and dispersion when firms operate in more uncertain environments and when firms are less comparable to their industry peers. I fail to find a significant difference in the association between extension rates and analysts forecast error and dispersion for firms with errors in their XBRL filing and firms with higher levels of earnings management. This is consistent with the use of XBRL extension being largely informative in nature about idiosyncratic information. I contribute to the growing literature on the impacts of XBRL. A number of papers use XBRL implementation as a proxy for reduced information processing costs. These studies show that XBRL is related to a better information environment. With particular relevance to my study, Li, Lin and Ni (2014) and Liu, Wang, and Yao. (2014a) find that firms using XBRL filings generally have higher analysts following and lower analysts forecast error. Li et al (2014) also show that XBRL filings are associated with lower analysts forecast dispersion. However, these studies generally only look at whether the firm files its financial reports in XBRL and do not consider the use of extended tags within the filings. I find evidence that the 6

7 use of extended tags does reduce analysts forecast error and analysts forecast dispersion, suggesting that extended items do not erode the usefulness of XBRL filing. Second, I reconcile the mixed evidence in the current literature on the informativeness of extensions. Hoitash and Hoitash (2014) find that the number of extensions used is associated with a poorer information environment and suggest that extensions are related to reporting complexity. On the other hand, Li and Nwaeze (2015) find that the extension rate is negatively related to the firm s information environment in the early stages of XBRL implementations but positively related to the firm s information environment in later years. My results suggest that, after correcting for endogeneity, XBRL extensions are robustly positively related to the firm s information environment. The paper is organized as follows. In the next section, I provide a background on XBRL extensions and a literature review regarding XBRL and the firm s information environment. Section 3 describes my methodology and Section 4 provides results. I provide conclusions in Section 5. II. Literature Review and Hypothesis Development Literature Review Since the SEC started allowing firms to file XBRL documents in its voluntary filing program, there have been substantial studies investigating the informativeness of XBRL filings, arguing that XBRL reduces the information processing costs which directly or indirectly improve the firm s information environment. Specifically, prior literature finds that XBRL adoption is related to greater stock returns around filing dates (Efendi, Park, and Subramaniam 2010), lower information risk (Kim, Lim, and No 2012), more quantitative disclosures (Blankespoor 2012), lower cost of capital (Li et al. 2014), better analysts information (Li et al. 7

8 2014, Liu et al. 2014a), 3 more informative stock prices (Dong, Li, Lin, and Ni 2015), more favorable lending terms (Chen, Kim, and Zhou 2013), and lower earnings management (Lim et al. 2013). However, these studies only consider the implementation of XBRL, not the use of extended taxonomies, with the exception of Lim et al. (2013). Early evidence suggests that XBRL filings contain several errors. These errors include a validity test (Boritz and No 2008) as well as improper use of extensions (Debreceny et al. 2010, 2011). However, the error rate on average seems to be declining over time (Du, Vasarhelyi, and Zheng 2013, Bartley, Chen, and Taylor 2011, SEC 2014) with the exception of small firms, where the improper use of extensions seems to be increasing (SEC 2014). 4 Studies also find that firms with executives who have expertise in information systems tend to have fewer extended tags, suggesting that extended tags are used out of ignorance (Boritz and No 2013). While there is ample evidence that there are errors in XBRL filings, it is not clear whether these errors on average impede analysts information collection activities. Prior literature also suggests that XBRL extension use is related to earnings management. The basic argument is that firms may strategically choose extended tags for accounts that have been managed, which makes it more difficult to identify earnings management. Consistent with this logic, Lim et al. (2013) find that the use of more standard tags is generally negatively related to earnings management while more extended tag use is positively related to earnings management. To the extent that firms are using extended tags to mask earnings management, then XBRL extensions would be detrimental to analysts information. Both practitioners and academic studies have criticized the use of extensions because the use of extended tags makes it more difficult to compare the performance of firms within an 3 Interestingly, Liu, Yao, Sia, and Wei (2014b) find that early adopters of XBRL in China have significantly higher forecast errors (lower forecast accuracy) due to the uncertainty regarding the quality of the data. However, their sample only runs to 2006, as XBRL has been mandatory in China since 2004, when XBRL was relatively new to the investing community. 4 The SEC (2014) observed that small firms had an increasing use of improper extensions mainly due to the software commonly used by these firms. Because my sample requires analysts following, many of the small firms are not in my sample. Specifically, only 6.1% of my sample is classified as non-accelerated filers. Furthermore, in my analysis, I control for software fixed effects. 8

9 industry. In a guide to XBRL for investors, the CFA warns of the threat to comparability that XBRL extensions pose in that it is difficult to compare extended items across firms (CFA 2011). The counter argument is that having extended tags allows investors to highlight items that are unique to firms, making it easier to compare common items across firms. For example, in a 2011 survey 45% of respondents who were aware of XBRL indicated that tagging noncomparable items is useful in valuation in general while another 35% believed tagging noncomparable items would make it easier for data aggregators to normalize data (CFA 2011). From an empirical standpoint, Dhole et al. (2015) find that more extended tags reduce comparability. If XBRL extensions pose a significant threat to analysts information, then more extended tags should increase analysts forecast errors and forecast dispersion. More recently, a few studies have examined the association between the use of extended items and the firm s information environment. Hoitash and Hoitash (2014) examine the relation between document extensions and financial reporting quality and audit fees, arguing that extensions capture the reporting complexity of the firm. They find consistent with this notion that firms with more extensions tend to have more restatements, a greater likelihood of internal control weaknesses, higher abnormal accruals, and higher audit fees. On the other hand, Li and Nwaeze (2015) examine document extension rates and the firm s financial information environment, using market based measures of the firms information environment. 5 They find that in the early years, extensions were negatively related to the firm s information environment while in later years, extensions were positively related to the firm s information environment. I reconcile these results by explicitly considering the endogeneity of XBRL extensions. 5 Specifically, they use event return volatility, information efficiency, change in standard deviation of daily stock returns, and bid-ask spread to proxy for the firm s information environment. In unreported test, I find that extension rates are negatively related to changes in standard deviation of daily stock returns and changes in bid-ask spread using IV methods. However, I do not find any significant relation between extension rates and event return volatility or information efficiency when I use the IV technique. 9

10 Hypothesis Development While prior literature is largely in agreement about the benefit of XBRL in lowering information processing costs, there is little that is already known about how the use of extended taxonomies influences analysts information. Many filers contend that extended taxonomies are needed to allow firms to apply informational context to data items. If firms use extended tags to convey information imbedded in the data, then analysts would have better information about the future earnings of the firm, and there would be less divergence of opinion among analysts. On the other hand, firms may use extended tags out of ignorance (Boritz et al. 2013) or error (Debreceny et al. 2010), which would likely have no informative content at all. However, if managers use extended tags opportunistically (Lim et al. 2013) or if extension use severely compromises comparability (Dhole et al. 2015), then extension use would be detrimental to analysts information and increase forecast dispersion and forecast errors. Because in the cross section the different uses of extensions likely exist, it is an empirical question as to which use dominates. This leads to the first hypothesis, stated in the null: H1) The use of extended tags in XBRL documents is unrelated to analysts forecast dispersion and analysts forecast error. To tease out the different motives in the use of extended taxonomies, I look at the extension use for particular parts of the XBRL document. I consider three parts of the filing where the informativeness of the extension is likely to vary. First, I consider extensions in the footnote disclosures of the XBRL instance document. Items tagged as disclosure notes are unlikely to influence valuation models used by analysts. Furthermore, disclosure notes are likely the target of direct search by analysts based on keyword searches. If managers are using extended tags to convey information to the market, this is the primary place where it would occur. On the other hand, more complex firms may require more footnote disclosures. Thus I present a null hypothesis with respect to extensions in the textual disclosures of the firm. H2) The use of extended tags in footnote disclosure is unrelated to analysts forecast dispersion and forecast error. 10

11 Next I consider the use of extensions with the financial statements. While financial statement items may provide additional detail about the financial performance and position of the firm, it is also likely that the numbers on the financial statements have been manipulated. The importance of these items is greater because they are more likely the target of direct input into valuation models, which is the type of interoperability that XBRL has targeted. At this level, extension could be either informative or opportunistic. Thus I retain a null form hypothesis with respect to extensions within the financial statements. H3) The use of extended tags within the financial statements is unrelated to analysts forecast dispersion and forecast error. III. Methodology In this section I describe the methodology used to test the association between the use of extended tags in financial reporting and analysts information. First, I describe how I measure the use of extended tags. Second, I discuss the instrumental variables used in my study. Finally, I describe the tests used to address my hypotheses. Measuring Extensions My first task is to identify the use of extended tags for a given filing. I search the SEC XBRL RSS monthly XBRL feed for 10-K and 10-Q filings to get the filings in each quarter. I then identify extended tags using the namespace prefix for each item of interest. Specifically, namespace references that include sec.gov, fasb.org, w3.org, or xbrl.org are considered official taxonomies 6 and all others are considered extended (unofficial) taxonomies. To measure extension use, I divide the number of extended items in a particular section by the number of total items in that section. 7 This has the interpretation of examining what happens if I take one standard tag and make it an extended tag, holding the total number of items constant. 6 I include w3.org because this is the namespace for the World Wide Web consortium which specifies some of the requirements for XML documents in general. 7 In counting, I only consider the item s name and not the context. Therefore, if an extended item is included in comparative statements, I only count it once as opposed to counting it once for each comparative statement in which it is included. 11

12 My next task is to identify the different levels for the rate of extension. First, I consider the use of extension items within the whole XBRL filing. For this document level extension rate, I consider each unique item reported in the XBRL filing within each section defined in the presentation linkbase. 8 This is not a straightforward task, as firms have discretion over the names of the sections of the report. However, many firms follow the convention of naming their documents using a document identifying number followed by Document, Statement, or Disclosure, followed by the actual section name as shown in the traditional filing. 9 In counting the tags for the whole filing, I only consider the sections that are indicated as either Disclosure or Statement 10 using section titles provided in the presentation linkbase of the XBRL filing. Second, I consider the extension rates within the disclosure and statement sections of the filings. I count the number of unique items (disregarding context) within each section of the XBRL. 11 Again, I use the section titles to identify whether a section of the report is a part of the disclosure or part of the financial statements. Finally, I consider the extension rate within the individual financial statements. I rely on the statement name to identify the Balance Sheet, the Cash Flow Statement, and the Income Statement. 12 The statement of Changes in Owner s Equity has a number of variations and is often not reported as a statement. I therefore cannot reliably identify this statement. With respect to the Income Statement, firms have latitude to report either one Comprehensive Statement of Income or two separate statements, the Statement of Income and the Statement of Other Comprehensive Income. 13 To the extent that 8 A presentation linkbase lists the sections of a report and what items should be included in that section. 9 Here Document refers to items related to the filing as a whole. These are items such as the filing date, the CIK of the firm making the filing, the form type, etc. which are sometimes referred to as Document and Entity Items (DEI). 10 Including all tags within the whole filing provides qualitatively similar results. 11 I exclude sections indicated as Parenthetical. 12 I use a number of keywords and regular expressions to identify the statements by name. The program used to identify the statements is available upon request. 13 There is also the possibility that other comprehensive income is in the statement of equity, though this option was eliminated by Topic 220 for fiscal periods from December 15, 2011 onward. 12

13 I can identify a separate Statement of Other Comprehensive Income, I exclude the related tags from the count for the Income Statement. 14 Instrumental Variables Because extension use is likely endogenously determined, I use instrumental variables to estimate the relationship between extension rates and financial analysts forecast error and dispersion. A good instrumental variable should be related to the independent variables of interest but unrelated to the dependent variables after considering other variables in the model. The first variable I consider is the number of standard items in the US GAAP taxonomy used in the filing. This will be related to extension rate because as standard items are added to the taxonomy, firms will not need to extend their firm-specific taxonomies as much. Furthermore, there is no reason to believe that the number of standard items in the standard taxonomy will be related to analysts forecast error or dispersion except through extension rates themselves. 15 Empirical Model I use the following model to test the association between extension use and analysts forecast error and analysts forecast dispersion. Error i,t or Disp i,t = α 0 + α 1 ExtendRate i,t + α 2 Size i,t 1 + α 3 Age i,t 1 + α 4 StdROE i,t 1 + α 5 Intang i,t 1 + α 6 ROA i,t 1 + α 7 Corr i,t 1 + α 8 EPS i,t 1 + α 9 Q4 i,t + α 10 LnCov i,t + α 11 LargeFiler i,t + α 12 AccFiler i,t + α 13 Horizon i,t + α 14 Violate i,t + Software Effects + Industry Effects + Year Effects + e i,t (1) I model analysts forecast error (Error) and analysts dispersion (Disp) similar to Li et al. (2014). Since my focus is on quarterly filings, I measure the analysts forecast error as the magnitude of the median one period ahead quarterly EPS forecast in the 30-day following the 14 In an unreported test, I combine tags identified as either the Income Statement or the Statement of Other Comprehensive Income and find similar results. 15 While there is no reason to believe that the number of standard tags is related to the firm s information environment, I cannot test this assumption because the system is exactly identified and not over-identified. 13

14 filing minus the respective actual quarterly EPS, scaled by price just prior to the filings date. I measure dispersion using two variables. First, I measure dispersion using the standard deviation of analysts one period ahead quarterly EPS forecasts (StdDev) issued 30-days following the firm s quarterly filing scaled by the most recent prices just prior to the filing. However, this measure requires at least three forecasts to be issued within a 30-day period and therefore reduces sample size significantly. As an alternative, I measure dispersion as the range (Range) of one-period ahead quarterly EPS forecast issued in the 30-day period following the quarterly filing, scaled by price just prior to the filings date. 16 The variable of interest is ExtendRate, which is the ratio of the extended items to the total number of items in the quarterly filings. I include a number of control variables consistent with prior literature (Li et al. 2014; Liu et al. 2014a). Because larger firms have a better information environment as well as more complex operations which may require more XBRL elements in their filings, I include the log of total assets at the beginning of the quarter (Size) and the number of years the firm has been in Compustat (Age). To proxy for the uncertainty in quarterly earnings, I use the standard deviation of return on equity (StdROE) and the correlation between ROE and stock returns (Corr) over the previous four quarters. Firms with more intangibles are typically more difficult to predict future earnings. As such, I include the sum of research and development and advertising expenses from the previous quarter divided by deflated by market value at the beginning of the quarter (Intang). I also control for return on assets (ROA) to control for firm performance and earnings per share for the current year (EPS) to control for earnings surprises. I include an indicator variable to capture differences in forecast errors for the fourth quarter as well as differences in the extension rates between quarterly filings and annual fillings. 17 I 16 In unreported tests, I use the inter-quartile range and find similar results. 17 While an indicator variable would control for the differences in the average extension rate between quarterly and annual filings. However, this does not control for the possibility that the relationship between extension rate and analysts information may be different between quarterly and annual filings. In unreported test, I use an interaction between the fourth quarter indicator and the extension rates and do not find any such difference. I also partition the sample by annual vs quarterly filings and do not find any significant difference for the coefficient on extension rates for these two groups. 14

15 control for analysts attention using the log of analysts following (LnCov) in the 30 days prior to the filing. The mandatory filing of XBRL was implemented in three stages. Furthermore, firms in these categories are more likely to be stable firms with better information environments. Therefore, I include two dummy variables to capture filing status, one for firms classified as Large Accelerated Filers (LargeFiler) and one for firms classified Accelerated Filers (AccFiler) as these firms were required to adopt XBRL in 2009 and 2010 respectively and therefore have more experience with XBRL Filings. Because estimates issued closer to the fiscal quarter end dates are generally less uncertain, I include the log of the average number of days between the forecast issue date and the relevant fiscal period end dates (Horizon). Some firms may have errors in their XBRL filings that would result in problems parsing the XBRL data properly. I control for this possibility by including an indicator variable (Violate) for firms that violate any technical rules in their filings as reported by XBRLCloud.com. The software used to create XBRL filings may encourage the use of extensions, and it may also be more compatible with analysts internal software. To control for this, I use fixed effect for the software used to create the XBRL filings. I also have fixed effects for Fama-French 48 industry and year. IV. Empirical Analysis Sample Statistics My sample starts with the 103,202 10K and 10Q XBRL filings for fiscal years 2010 through Because I obtain analysts information from I/B/E/S, financial statement numbers from Compustat, and stock prices and returns from CRSP, I lose 47,451 observations from which I cannot match firm identifiers within the databases. Of the remaining 55,751 observations, 19 filings cannot be properly parsed to collect document extension rates, and 455 filings do not clearly indicate the sections of the report. I cannot correctly identify the Balance Sheet, Cash Flow Statement, or Income Statement for an additional 1,749 filings. I further lose 21, I start in 2010 because only 76 filings are available for 2009 filings primarily due to the restriction of identifiable sections. 15

16 filings because I lack the data for my dependant variables, forecast error and forecast dispersion. For 10,530 of the remaining observations, I lack data for my control variables which results in a final sample of 22,800 firm-quarter observations from 3,051 firms. Table 1 presents a summary of the sample selection. Table 2 presents the distribution of the standard tags used across different fiscal years as well as the number of tags defined in each taxonomy. Given that the data is quarterly and the SEC generally allows firms to use the most recent two taxonomies, the distribution is to be expected. For example, in 2013, 3,732 observations use the 2012 taxonomy and 3,696 observations use the 2013 taxonomy while only 54 and 5 observations use the 2011 and 2014 taxonomies, respectively. Table 2 also shows that the number of standard tags has increased over time from 13,452 in 2009 to 17,433 in Thus, there is substantial variation in the number of tags cross-sectionally so as not to be absorbed by any year fixed effects. Table 3 presents simple statistic from the sample. The average (median) forecast error is (0.002) and the average (median) forecast range is (0.001). The average (median) forecast inter-quartile range is (0.001). For my sample, the average (median) extension rate is 17.3% (16.2%) which is slightly lower than the 20.55% average extension rate reported by Li and Nwaeze (2015). This may be explained by the declining use of extensions over time. When I look at the extension rate for the separate sections of the report, I find that extension are more frequent in the disclosure section, with an average of 21.6% of disclosure items being extended (median of 20.4%) compared to about only 6.9% of financial statement items being extended (median of 5.6%). Examining the individual financial statements, I note that the cash flow statement tends to have the most extensions, with an average 8.7% and a median of 7.1% of the items being extended. The balance sheet has the least extensions with an average of 4.5% and a median of 3.1%. The average extension rate for the Income Statement is 6.8% with a median of 5.0%. 16

17 Table 4 presents the correlation coefficients for the sample. I find that the document, disclosure, and overall statement extension rates have Pearson s correlation coefficients with analysts forecast error of 0.033, 0.031, and (See Table 4 column 1) and are significant at least at the 10% level. The correlation coefficient between these rates and the standard deviation of analysts forecast are 0.080, 0.068, and respectively and are significant at least at the 10% level. The respective correlations are 0.081, 0.068, and for the range of analysts forecasts. For the individual financial statements, I find the correlation coefficients for the balance sheet, cash flow statement, and income statement with analysts forecast error are 0.007, 0.058, and , although only the extension rate for the cash flow statement is significant at the 10% level. The correlation coefficients for these statements and forecast dispersion are 0.020, 0.087, and respectively, but again only the correlation for the cash flow statement is significant. Turning to the correlation between these statement level extension rates and the range of analysts forecasts, the coefficients are 0.025, 0.074, and respectively, with only the extension rate on the cash flow statement and the income statement being significant. First Stage Regression Table 5 presents the first stage regression of our various extension rates on the number of tags in the standard taxonomy. In the first column, we use the document extension rate and in the second and third columns, we break that extension rate down into the Disclosure and Statement level extension rate. The fourth through sixth columns present the results for the Balance Sheet, Cash Flow Statement, and Income Statement extension rates respectively. Consistent with our conjecture that use of more standard tags is associated with lower extension rates, we find a negative coefficient on the number of standard tags for all our models. In general the coefficient is highly significant and passes the under-identification test, suggesting that the total number of standard tags in the relevant taxonomy is highly correlated with the extension rates. 17

18 Specifically, the Kleibergen-Paap Rank LM statistic ranges from to and is significant at the 1% level for all of our models. 19 This suggests that the total number of tags in the standard taxonomy used by the firm is strongly associated with the extension rate. Analysts Information and XBRL Extension Rate In Table 6, I present the results for the regression of analysts forecast error and forecast dispersion using both OLS regression and the IV (2SLS) regressions on the section extension rates for the whole document The first three columns present the results for the OLS estimates of the regression of Error, StdDev, and Range on the overall extension rate. We find significant coefficients of 0.010, 0.004, and 0.009, respectively. This positive relation would lead one to conclude that extensions degrade the firm s information environment either because of complexity, lack of comparability, or erroneous or misleading use. However, using 2SLS, we find significant coefficients of , , and , respectively, suggesting that extensions generally improve the analysts information. The positive relation found in using OLS is likely driven by the complexity of the firm that both requires firms to use extended items and increases financial analysts forecast error and dispersion. Table 7 presents the 2SLS results of the regression of analysts forecast dispersion on extension rates for the footnote disclosures and the financial statement separately. The first three columns contain the results for extensions within the footnote disclosures. Consistent with the results for the overall extension rate, we find significant coefficients of , , and for Error, StdDev, and Range, respectively. In the last three columns, the variable of interest is the extension rate within the financial statements. Again, the results are negative and significant with coefficients of , , and for Error, StdDev, and Range, 19 These statistics are for the full sample. When I use the sub-sample that has valid standard deviation observations, I come to a similar conclusion. When examining the standard deviation of estimates as a measure of dispersion, I only use the available observations in both the first and second stage. However, for brevity, I only report the first stage for the full sample. 20 All of my analysis uses standard errors clustered by firm as suggested by Petersen (2009). 21 Throughout the analysis, I do not report the adjusted R squared because it is not interpretable in the context of 2SLS. 18

19 respectively. While I cannot statistically test the difference between these extensions in the disclosure and the statement sections, an examination of the standardized coefficients gives a sense of their importance in determining analysts forecast error and dispersion. These standardized coefficients indicate that extensions within the financial statements have a stronger association with analysts forecast error ( compared to ), analysts forecast standard deviations ( compared to ), and forecast range ( compared to ). The weight of the evidence is consistent with XBRL extensions improving analysts information, with extensions in the financial statements being especially helpful. Next I turn to the individual financial statements. In Table 8, I find that extensions within the balance sheet, cash flow statement, and the income statement are negatively related to both analysts forecast error and the standard deviation of forecasts. 22 The standard coefficient on cash flow is the largest in magnitude for both the analysts forecast error (-0.870) and forecast standard deviation (-1.376). In comparison, the standard coefficient for analysts forecast error is and for extensions on the balance sheet and income statement. The corresponding coefficients for the standard deviation of analysts forecasts is and for the balance sheet and income statement. The larger weight on extensions in the cash flow statement may be a manifestation of the degree to which idiosyncratic items are reported in the cash flow statement while items on the balance sheet and income statement, because they have been around longer, tend to reflect more common items. I leave it to future research to further examine the role of extended items on the cash flow statement vis-à-vis the balance sheet and income statement. 22 I exclude Range in Table 8 for brevity. Untabulated results produce similar results (i.e. extensions on the cash flow statement has the strongest negative association while extensions on the balance sheet has the weakest). 19

20 V. Additional Analysis Cross-sectional determinants of the relationship between analysts information and extension rate. In my main analysis, I find that on average XBRL extension use is negatively related to analysts forecast and dispersion, implying that manager s decisions improve the firm s information environment. In this section, I examine some cross-sectional variables that may determine the association between analysts information and XBRL extension use. 23 These cross-sectional variables fall into four categories based on the characterization extension use in the literature. I begin by examining how the relation between extension rate and analysts information varies with information uncertainty. If firms use extensions to convey information, then the relationship between extensions and analysts information should be stronger when information is more uncertain. The second characterization is that XBRL extensions are commonly used erroneously (Debreceny et al. 2010, Boritz and No 2008, Bartley et al. 2011, SEC 2014) and therefore compromise the usefulness of XBRL. A similar criticism is that extensions compromise the comparability of financial statements across firms (Dhole et al. 2015). I therefore examine how comparability moderates the relation between extension rates and analysts information. The last category is related to earnings management, since firms that are managing earnings may try to mask make their reporting opaquer through the use of extensions (Lim et al. 2014). Table 9 presents the results for the IV regression of analysts forecast error and forecast dispersion where the observations are split into groups based on the yearly median of the crosssectional variable in question. For brevity, I only report the results for the overall document extension rate, and I do not report the coefficients for the control variables, although they are included in the model. I also only report the results for forecast error and standard deviation. 23 To avoid any potential reserves causality, I measure all cross-sectional variables as of the last fiscal year prior to the quarterly filling. 20

21 Information Uncertainty I include two information uncertainty measures to examine the cross-sectional relationship between XBRL extension use and analysts forecast error and dispersion. The first is the standard deviation of daily stock returns (StdRet) over the 12-month period ending at the beginning the current fiscal year end. The second is the standard deviation of return on equity (StdROE) over the four quarters ending at the beginning of the current fiscal year end. Panels A and B of Table 8 report the results for the cross-sectional variables of StdRet and StdROE. If managers are using their discretion to be informative, then the correlation between XBRL and analysts forecast error and dispersion should be greater when the information is generally more uncertain. Using the standard deviation of returns as a measure of information uncertainty, I find that firms with uncertainty have a negative and significant correlation between extension rate and analysts forecast (coefficient of ) and forecast dispersion (coefficient of ). For firms with low uncertainty, I do not find a significant correlation between extension rate and forecast error (coefficient of ), but I do find a negative and significant relation between extension rate and forecast dispersion. The difference between the high and low groups is significant for both forecast error (coefficient of ) and forecast dispersion (coefficient of ). Thus firms with returns volatility generally have a stronger relation between extension use and analysts information. I find a similar story when I examine the standard deviation of ROE, as reported in Panel B. Specifically, firms with higher volatility in ROE have a negative and significant correlation between extension use and forecast error and forecast dispersion, with coefficients of and , respectively. Firms with lower volatility in ROE still have a negative association between extension use and forecast error and dispersion, with coefficients of and respectively. Furthermore, the difference between these two groups is negative and significant (coefficients and and respectively), suggesting that extension rate is more strongly associated with analysts information when overall information uncertainty is high. 21

22 XBRL Errors in filings To test the extent to which errors in XBRL compromise the usefulness of XBRL extensions, I divide the sample based on whether there are observable errors in the filing. I use the data from XBRLCloud.com to determine if a filing has any type of error. While erroneous extension use may not directly trigger a technical error, filers with technical errors are more likely to also make mistakes in utilizing extended tags. For my sample, there are 4,539 filings with some sort of technical violation. I report the results for firms with and without technical errors in Panel C of Table 9. For firms with a technical error, we find an insignificant coefficient on extension rate for both analysts forecast error and forecast dispersion (coefficients of and 0.024). Firms without a technical error have a negative and significant association between extension rates and analysts forecast error and dispersion (coefficients of and ). However, the differences in the coefficients between the two groups are insignificant. While this is consistent with errors in XBRL filings eroding the usefulness of XBRL extensions, this analysis comes with one important caveat. For the firms that have technical errors, the Kleibergen-Paap Rank LM statistic is insignificant. It may be that firms with technical errors are indeed selecting extension rates erroneously and therefore the number of standard tags defined in the taxonomy has no relation to the extension rate for these firms. Therefore, the IV method may not be valid for these firms and I therefore, caution the reader against making a strong conclusion based on these results. Comparability One of the criticisms against allowing extensions is that they may compromise comparability between two firms (CFA Institute 2011, Dhole et al 2015). In this section I examine how differences in comparability mediate the relationship between extension rates and analysts forecast error and dispersion. When firms are generally comparable to their peers, the use of extensions may actually erode analysts information because it is more difficult to 22

23 assess the comparability of individual line items. On the other hand, when firms are less comparable, the use of extensions highlights this and makes it easier for analysts to identify the line items that are not comparable to their peers. Following Dhole et al. (2015), I use two measures of accounting-based comparability. Both measures start by estimating the firm-specific accounting function that map economic events, as measured by stock returns, into earnings. Following DeFranco. Kothari, and Verdi (2011), 24 I estimate the following equation by firm over the past 16 quarters: Earnings i,t = α i + β i RET i,t + e i,t (4) Where Earnings is quarterly income before extraordinary items scaled by market value at the beginning of the quarter and RET is the stock return for the quarter. The estimates of α i and β i are firm specific parameters that map returns to earnings for firm i. Next, we estimate what firm i s earnings would have been under firm i s accounting function (α i and β i ) given the economic events of firm i (Returni,t). Next, I estimate what firm i s earnings would have been under firm j s accounting function (α j and β j ) given the economic events of firm i. Formally, I calculate: E[Earnings] i,i,t = α i + β iret i,t (5) E[Earnings] i,j,t = α j + β jret i,t (6) for all pairs i,j within the same 2-digit SIC industry. The difference between E[Earnings] i,i,t and E[Earnings] i,j,t comes about because firm i and firm j use different accounting methods to capture economic events. In order to mitigate measurement error, to get a measure of 24 I am grateful to Rodrigo Verdi for providing the SAS code here: 23