extended xbrl taxonomies and financial …€¦ · aaa ais mid-year meeting. ... the work described...

EXTENDED XBRL TAXONOMIES AND

FINANCIAL ANALYSTS' INFORMATION

Joseph Johnston

Department of Accountancy

City University of Hong Kong

[email protected]

First Draft: May 5th 2015

This Draft: April 14th 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Preliminary Draft – Please do not quote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 11502114).

All errors remain my own.

mailto:[email protected]

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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.

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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).

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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

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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.

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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

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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.

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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.

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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 non-

comparable items is useful in valuation in general while another 35% believed tagging non-

comparable 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.

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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.

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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 taxonomies6and 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.

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.

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.

𝐸𝑟𝑟𝑜𝑟𝑖,𝑡 𝑜𝑟 𝐷𝑖𝑠𝑝𝑖,𝑡 = 𝛼0 + 𝛼1𝐸𝑥𝑡𝑒𝑛𝑑𝑅𝑎𝑡𝑒𝑖,𝑡 + 𝛼2𝑆𝑖𝑧𝑒𝑖,𝑡−1 + 𝛼3𝐴𝑔𝑒𝑖,𝑡−1

+ 𝛼4𝑆𝑡𝑑𝑅𝑂𝐸𝑖,𝑡−1 + 𝛼5𝐼𝑛𝑡𝑎𝑛𝑔𝑖,𝑡−1 + 𝛼6𝑅𝑂𝐴𝑖,𝑡−1

+ 𝛼7𝐶𝑜𝑟𝑟𝑖,𝑡−1 + 𝛼8𝐸𝑃𝑆𝑖,𝑡−1 + 𝛼9𝑄4𝑖,𝑡 + 𝛼10𝐿𝑛𝐶𝑜𝑣𝑖,𝑡

+ 𝛼11𝐿𝑎𝑟𝑔𝑒𝐹𝑖𝑙𝑒𝑟𝑖,𝑡 + 𝛼12𝐴𝑐𝑐𝐹𝑖𝑙𝑒𝑟𝑖,𝑡 + 𝛼13𝐻𝑜𝑟𝑖𝑧𝑜𝑛𝑖,𝑡

+ 𝛼14𝑉𝑖𝑜𝑙𝑎𝑡𝑒𝑖,𝑡 + 𝑆𝑜𝑓𝑡𝑤𝑎𝑟𝑒 𝐸𝑓𝑓𝑒𝑐𝑡𝑠+ 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 + 𝑌𝑒𝑎𝑟 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 + 𝑒𝑖,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.

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.

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

2014.18 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 21,198

18 I start in 2010 because only 76 filings are available for 2009 filings primarily due to the restriction of identifiable sections.

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 2014. 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

0.006 (0.002) and the average (median) forecast range is 0.005 (0.001). The average (median)

forecast inter-quartile range is 0.003 (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%.

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 0.037 (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 0.058 respectively and are significant at

least at the 10% level. The respective correlations are 0.081, 0.068, and 0.058 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 -0.002, 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 0.006 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 0.016

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.

18

Specifically, the Kleibergen-Paap Rank LM statistic ranges from 11.660 to 85.375 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.2021 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 -0.055, -0.034, and -0.065, 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 -0.043, -0.027,

and -0.051 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 -0.130, -0.078, and -0.155 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.

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 (-0.530 compared to -0.311), analysts’

forecast standard deviations (-0.884 compared to -0.557), and forecast range (-0.896 compared

to -0.526). 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 -0.445 and -0.597 for extensions on the balance sheet and income statement. The

corresponding coefficients for the standard deviation of analysts’ forecasts is -0.820 and -1.075

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).

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 cross-

sectional 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.

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 -0.108) and forecast dispersion (coefficient of -0.057).

For firms with low uncertainty, I do not find a significant correlation between extension rate

and forecast error (coefficient of -0.000), 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 -0.108) and forecast dispersion

(coefficient of -0.049). 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 -0.064

and -0.050, respectively. Firms with lower volatility in ROE still have a negative association

between extension use and forecast error and dispersion, with coefficients of -0.020 and -0.007

respectively. Furthermore, the difference between these two groups is negative and significant

(coefficients and -0.044 and -0.043 respectively), suggesting that extension rate is more

strongly associated with analysts’ information when overall information uncertainty is high.

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 -0.046 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 -0.050 and -0.034). 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

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:

𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠𝑖,𝑡 = 𝛼𝑖 + 𝛽𝑖𝑅𝐸𝑇𝑖,𝑡 + 𝑒𝑖,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�̂�and 𝛽�̂�

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�̂� and 𝛽�̂�) 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�̂� and 𝛽�̂�) given the economic events of firm i. Formally, I

calculate:

𝐸[𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠]𝑖,𝑖,𝑡 = �̂�𝑖 + �̂�𝑖𝑅𝐸𝑇𝑖,𝑡 (5)

𝐸[𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠]𝑖,𝑗,𝑡 = �̂�𝑗 + �̂�𝑗𝑅𝐸𝑇𝑖,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: http://www.mit.edu/~rverdi/acctcomp_2013.sas.

http://www.mit.edu/~rverdi/acctcomp_2013.sas

24

comparability between firm i and firm j (CompAccti,j,t), I calculate the negative mean absolute

deviation between 𝐸[𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠]𝑖,𝑖,𝑡 and 𝐸[𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠]𝑖,𝑗,𝑡 for the 16 prior quarters:

𝐶𝑜𝑚𝑝𝐴𝑐𝑐𝑡𝑖,𝑗,𝑡 = −

1

16∗ ∑ |𝐸[𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠]𝑖,𝑖,𝑡 − 𝐸[𝐸𝑎𝑟𝑛𝑖𝑛𝑔𝑠]𝑖,𝑗,𝑡|

𝑡

𝑡−15

(7)

This provides a measure of comparability for each i,j pair within the same 2-digit SIC industry.

To come up with a firm-specific measure of comparability, I use two aggregation methods

suggested by DeFranco et al. (2011). First, I rank CompAccti,j,t for each firm i and quarter t. I

then take the average of the top four firms to come up with comparability for firm i for its four

most comparable firms (CompAcct4). The second measure of comparability (CompAcctInd) is

the mean CompAccti,j,t for all firms within the same 2-digit SIC industry as firm i. The results

for CompAcct4 and CompAcctInd are presented in Panels D and E, respectively, of Table 9.

When I use CompAcct4 as my measure of comparability, I find that firms’ high

comparability have an insignificant coefficient -0.012 for the relation between extension rate

and forecast error, while firms with low comparability have a significant negative coefficient

of -0.066. The difference of 0.053 is significant at the 10% level. We find a similar pattern for

the relation between extension rate and the standard deviation of analysts’ forecasts.

Specifically, we find that the coefficient for the low comparability group is -0.012 (significant

at the 10% level) while the coefficient is -0.045 for the high comparability group and the

difference of 0.034 is significant at the 5% level.

The results are even more striking when I use the average comparability of all of the

firms within the industry (AccCompInd) as shown in Panel E of Table 9. For the low

comparability firms, the coefficient on extension rate is 0.005 (-0.007) for analysts’ forecast

error (standard deviation) and is insignificantly different from zero. For firms that are highly

comparable to their industry peers, the coefficient on extension rate is -0.086 (-0.049) for

analysts’ forecast (standard deviation) and is significant at the 1% level. The difference of 0.091

25

(0.042) is also significantly different from zero at the 1% level. Taken together, these results

suggest that when a firm is highly comparable to its peers, there is little or no relationship

between extension rate and analysts’ information. However, when the firm is not comparable

to its peers, then XBRL extensions are related to a better information environment, highlighting

the idiosyncratic nature of the extensions.

Earnings Management

I use two different variables related to earnings management. The first is the magnitude of

abnormal accrual from the modified-Jones Model (Dechow, Sloan, and Sweeney 1995).

Specifically, I estimate the following model:25

𝑇𝑜𝑡𝐴𝑐𝑐𝑖,𝑡 = 𝛼0 + 𝛼1∆𝑅𝐸𝑉𝑖,𝑡 + 𝛼2𝑃𝑃𝐸𝑖,𝑡 + 𝑒𝑖,𝑡 (8)

Where TotAcc is total accruals defined as income before extraordinary items less cash flow

from operations before extraordinary items. ∆𝑅𝐸𝑉 is the annual change in revenue and PPE is

gross property, plan, and equipment. Equation (4) is run annually by Fama-French 48 industry,

and the coefficients are used to estimate abnormal accruals using the following equation.

𝐴𝑏𝐴𝑐𝑐𝑖,𝑡 = 𝑇𝑜𝑡𝐴𝑐𝑐𝑖,𝑡 − (�̂�0 + �̂�1(∆𝑅𝐸𝑉𝑖,𝑡 − ∆𝑅𝐸𝐶𝑖,𝑡) + �̂�2𝑃𝑃𝐸𝑖,𝑡) (9)

Where ∆𝑅𝐸𝐶 is the annual change in accounts receivable. My first measure of earnings

management is the absolute value of AbAcc, which I denote in the table as Abs(AbAcc).

My second measure of earnings management is the Dechow and Dichev (2002) accruals

quality measured following Francis, LaFond, Olsson, and Schipper (2005). For each year and

Fama-French 48 industry, I run the following regression:26

𝑇𝐶𝐴𝑐𝑐𝑖,𝑡 = 𝛼0 + 𝛼1𝐶𝐹𝑂𝑡−1 + 𝛼2𝐶𝐹𝑂𝑡 + 𝛼3𝐶𝐹𝑂𝑡+1 + 𝛼4∆𝑅𝐸𝑉𝑖,𝑡 + 𝛼5𝑃𝑃𝐸𝑖,𝑡 + 𝑒𝑖,𝑡 (10)

Where TCAcc is measured as income before extraordinary items less operating cash flows

before extraordinary items less PPE, CFO is cash flow from operations before extraordinary

items, and all other variables are defined as in equation 4. I then take the standard deviation of

25 All variables, including the intercept, are scaled by lagged total assets. 26 Again all variables including the intercept are scaled by lagged assets.

26

the residual from equation 5 over the years t-1 to t-5.27 I denote my second measure of earnings

management as AQ.

If managers use extensions to mask earnings management, then the correlation between

extensions and analysts’ forecast error and forecast dispersion should be less negative for firms

with high values Abs(AbAcc) and AQ. In panel F of Table 9, I use Abs(AbAcc) to partition my

sample. Firms with high magnitudes of abnormal accruals have negative and significant

coefficients on extension rate of -0.069 and -0.046 for analysts’ forecast error and standard

deviation respectively, while firms with low magnitudes of abnormal accruals have significant

coefficients of -0.028 and -0.023 for forecast error and dispersion, respectively. However, the

differences between the high and low magnitude of abnormal accruals is insignificant

(coefficients of -0.041 and -0.023, respectively).

When I examine AQ as my partitioning variable, I find similar results, as reported in

panel G of table 9. Specifically, for firms with high AQ, the coefficients on extension rate are

-0.086 and -0.047 on analysts’ forecast error and dispersion while the corresponding

coefficients with low AQ are -0.007, which is insignificantly different from zero, and -0.021.

For forecast error, I find that the difference of the coefficients for the two groups is significant

at the 5% level (with a coefficient of -0.079) and for standard deviation, I find an insignificant

difference of -0.26. Taken together with the results from panel F, there is no evidence to suggest

that the informativeness of extensions is compromised for firms managing earnings.28

Number of Total and Extended Tags

Hoitash and Hoitash (2014) find that both the total number of tags and the number of

extended tags is negatively related to the firm’s information environment. In my main analysis,

27 This lags the measure by one more year to take into account the potential look ahead bias in using CFO in t+1 in equation

5. 28 In reported analysis, I separate AQ into its innate and discretionary components following Francis et al. (2005) and find that extension rate is more negatively related to analysts’ forecast error and dispersion for high levels of both components, though not significantly so.

27

I use the extension rate as my main variable of interest, where the denominator is the total

number of tags in the filing. My findings may be a driven by total number of tags being in the

denominator. I therefore examine the natural log of the total number of tags and of the extended

tags in a filing.

Table 10 presents the first stage regression of total tags and extended tags on the number

of standard tags and other control variables. In the first column, I find a positive and significant

relation between the number of standard tags in the taxonomy and the total tags in the filings.

This is consistent with the notion that when firms have more choices on tags, they increase the

tags they use. Similar to the results reported in Table 5, I find a negative association between

the number of standard tags defined in the taxonomy and the number of extended tags defined

by the firm. Kleibergen-Paap Rank LM statistic is 45.394 for the total number of tags and

142.137 for the number of extended tags indicating that the number of standard tags defined in

a taxonomy is strongly associated with both the total number of tags in a filing and the extended

tags in a filing.

Table 11 presents the OLS and 2SLS results for the total number of tags used in the filing.

Under OLS, we find positive coefficient the total 0.003, 0.001, and 0.003 for Error, StdDev,

and Range, respectively. When I use 2SLS, I continue to find a significant positive association

between the total tags in an XBRL filing and Error, StdDev, and Range, with coefficients of

0.020, 0.012, and 0.024. This is consistent with Hoitash and Hoitash (2014) and confirms their

suggestion of using the total number of tags in a filing as a measure or reporting complexity.

I report the results for the number of extended tags in Table 12. Consistent with Hoitash

and Hoitash (2014) and with the OLS analysis in Table 6, I find a positive and significant

association between the number of extensions within the filing and Error, StdDev, and Range

(coefficients of 0.001, 0.000, and 0.001). However, when I use 2SLS, I find a negative and

significant association between the number of extensions within a filing Error, StdDev, and

28

Range with coefficients of -0.007, -0.005, and -0.009, which is consistent with the 2SLS

analysis in Table 6 and the notion that firms use extended items to try to convey idiosyncratic

information. Together, these results also suggest that the total tags in a filing might be a more

preferred measure of complexity as opposed to the number of extended tags.

VI. Conclusion

In this paper, I examine the association between XBRL extensions and analysts’ information

environment. The SEC requires firms to present their financial reports in XBRL format which

electronically tags financial report data. However, the SEC allows firm to use either a standard

tag or a firm specific (extended) tag for any given data item. The intent of allowing the

extensions is to enable firms to convey information about items that might not be standard.

However, there has been skepticism related to the use of these tags such that extensions may

make the firm’s disclosures more opaque. To provide evidence on the use of extensions in

XBRL, I analyze the association between the extension rate and analysts’ forecast error and

analysts forecast dispersion.

I also suggest that more complex firms may have a greater demand for extended tags.

Therefore, an examination of the relation between XBRL extensions and characteristics that

may be related to complexity indicates that the extension rate may be endogenous to the system.

I suggest using the total number of tags to define the standard taxonomy used by the firm as an

instrument for the extension rate. The extension rate is related to the number of standard tags

within a taxonomy in that the more standard tags available, the less a firm would need to create

its own extension. It is hard to conceive of a situation where the number of standard tags defined

in a taxonomy would be related to the firm’s information environment except through the

extension rate after controlling for fiscal year.

Using a sample of 21,800 firm-quarter observations for the fiscal years running from 2010

to 2014, I find that the association between XBRL extension rate and analysts’ forecast error

29

and analysts’ forecast dispersion is positive when using OLS but is negative when using 2SLS.

This suggests that the use of XBRL extensions is positively associated with the firm’s

information environment and that XBRL are generally used to convey information about the

firm. I also find that there is a stronger association for extensions in the financial statements

relative to extensions in footnote disclosures. In examining extensions within the individual

financial statements, I find that extensions within the cash flow statement have a stronger

association with both analysts’ forecast error and dispersion, whereas extensions within the

balance sheet have the weakest.

In additional analysis, I fail to find evidence that the association between XBRL extension

use and analysts’ information varies with accrual management measures, which is inconsistent

with managers using XBRL extensions to mask earnings management. On the other hand, I do

find that the association between XBRL extension use and analysts’ information does vary with

stock returns volatility and with earnings volatility. This is consistent with managers using

extensions when their information environment is more uncertain.

The results of this study have implications for regulators considering the use of extended

taxonomies for XBRL filings. I find evidence that the extension rate is significantly associated

with lower forecast error and forecast dispersion. This is consistent with managers using their

discretion to try to convey information about the firm to investors. Regulators around the world

should not be overly concerned with allowing managers to use extended tags, but should rather

focus on improving the standard tags to ensure that common line items, especially at the

industry level, are available for firms to use.

30

Appendix A

Example of XBRL Extension Use

As an example of the use of an extended item in an XBRL filing, consider the June 30th 2013

quarterly filing for Seaworld Entertainment. In that filing, there is a piece of XML code that

includes ‘xmlns:seas=”http://www.seaworld.com/20130630”’ which is the namespace

declaration allowing the document to contain unique elements as defined by Seaworld. Within

the XBRL instance document, there is an item with the tag

“seas:FoodMerchandiseAndOtherRevenue.” The “seas:” is the namespace prefix that refers to

the earlier “xmlns:seas=” declaration.29 The namespace prefix can be used to identify whether

an item in the XBRL instance document is extended or not.

Below is an example of the use of an extended tag from the June 30th 2013 Quarterly

filing for Seaworld Entertainment Inc and Six Flags Entertainment. For simplicity, I only show

the components of revenue for each company. The extended items are indicated with asterisks.

Seaworld Six Flags

Admissions $255,001 Theme park admissions $199,666

Food, merchandise and other* 156,291

Theme park food, merchandise

and other* 149,669

Sponsorship, licensing and

other fees* 10,582

Accommodations revenue 3,784

Total revenues 411,292 Total revenues 363,701

* Extended Items

Both Seaworld and Six Flags use a standard tag for their main source of income, namely

“AdmissionsRevenues.” The only difference is the label (specified in a “Label linkbase”) that

each company uses. Both companies also create a new (extended) tag for Food and

Merchandise, which is a large component of revenue. Seaworld uses the tag

“FoodMerchandiseAndOtherRevenue” whereas Six Flags uses the tag

29 The current main schema for US GAAP is declared with something similar to ‘xmlns:us-gaap=”http://xbrl.fasb.org/us-

gaap/2015-01-31”’. While “us-gaap” is the preferred namespace reference, there is nothing keeping firms from declaring a

namespace of “‘xmlns:usgaapofficial=”http://xbrl.fasb.org/us-gaap/2015-01-31” and then using the prefix “usgaapofficial:”

throughout the instance document.

http://www.seaworld.com/20130630

http://xbrl.fasb.org/us-gaap/2015-01-31



31

“FoodAndBeverageMerchandiseAndOtherRevenue.” Six Flags also uses a standard tag for

Accommodation Revenue and creates an extended tag for Sponsorship, Licensing, and Other

Fees, despite there already being a standard Licensing Revenue tag. Also, both companies use

standard tags for their Total Revenues items (“Revenues”). Depending on how the program

reading the XBRL is designed, the standard tags may be more accessible, since their tag names

are known from the standard taxonomy. While Food and Merchandise probably provides

additional information about the sources of revenue, this revenue may not be easily found by

investors. Since it is not easily found, managers may try to use these items to mask manipulated

revenues. Ex ante, it is not clear whether firms’ use of extended tags convey new information

or make it more difficult to identify the source of particular line items. I elaborate on this in the

hypothesis development.

32

Appendix B

Variable Definition

Error = the magnitude of the median one period ahead quarterly EPS in the 30-

day following the filing minus the respective actual quarterly EPS,

scaled by the stock price just prior to the filing;

StdDev = the standard deviation of one-period ahead quarterly EPS forecast in the

30-day period following the XBRL filing scaled by the stock price just

prior to the filing;

Range = the range of one-period ahead quarterly EPS forecast in the 30-day period

following the XBRL filing scaled by the stock price just prior to the

filing;

ExtendRate = the number of extended items in the XBRL filing divided by the

number of total items in the XBRL filing;

Disc. ExtendRate = the number of extended items in all sections identified as footnote

disclosures divided by the number of total items in all sections

identified as footnote disclosures;

Stm. ExtendRate = the number of extended items in all sections identified as financial

statements divided by the number of total items in all sections

identified as financial statements;

BS ExtendRate = the number of extended items in the section identified as the balance

sheet divided by the number of total items in the section identified as

the balance sheet;

CF ExtendRate = the number of extended items in the section identified as the cash flow

statement divided by the number of total items in the sections identified

as the cash flow statement;

IS ExtendRate = the number of extended items in all sections identified as an income

statement (either the statement of income or the comprehensive

statement of income or the statement of other comprehensive income)

divided by the number of total items in all sections identified as an

income statement;

Size = the natural log of total assets at the beginning of the quarter;

Age = the natural log of the number of years the firm has been in Compustat;

StdROE = the standard deviation of return on equity over the previous four quarters;

Intang = 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;

ROA = the return on assets for the quarter related to the XBRL filing;

Corr = the correlation between ROE and stock returns over the previous eight

quarters;

EPS = earnings per share for the quarter related to the XBRL filing;

Q4 = an indicator variable equal to one if the filing is for the fourth quarter;

33

LnCov = the log of analysts’ following in the quarter prior to the filing;

Horizon = The natural log of the average number of days between the forecast issue

date and the report date for the one period ahead EPS forecasts within 30

days of the quarterly filing;

LargeFiler = an indicator variable equal to one if the filer is a large accelerated filer;

AccFiler = an indicator variable equal to one if the filer is an accelerated filer;

Violate = an indicator variable equal to one if the XBRL filing violates any of the

filings rules as indicated by XBRLcloud.com.

34

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37

Table 1

Sample Selection

Attrition Sample

Size

10K and 10Q XBRL filings between 2010 and 2014 103,202

Matched with Compustat, IBES, and CRSP (47,451) 55,751

Missing extension data (2,223) 53,528

Missing analysts’ forecast data (21,198) 32,330

Missing controls (10,530) 21,800

Table 2

Sample Frequency by Schema and Fiscal Year

Schema

2010

2011

2012

2013

2014

Total

Obs.

Total

Tags

us-gaap - 2009 544 492 16 0 0 1,052 13,452

us-gaap - 2011 2 1,941 1,798 54 0 3,795 15,725

us-gaap - 2012 0 7 3,555 3,732 172 7,466 17,087

us-gaap - 2013 0 0 10 3,696 3,912 7,618 17,736

us-gaap - 2014 0 0 0 5 1,864 1,869 17,433

Total 546 2440 5379 7487 5,948 21,800

38

Table 3

Summary Statistics

Mean Std Q1 Median Q4

Error 0.006 0.014 0.001 0.002 0.005

StdDev 0.003 0.005 0.001 0.001 0.003

Range 0.005 0.010 0.000 0.001 0.005

ExtendRate 0.173 0.083 0.113 0.162 0.221

Disc. ExtendRate 0.216 0.099 0.145 0.204 0.275

Stm. ExtendRate 0.069 0.055 0.028 0.056 0.096

BS ExtendRate 0.045 0.053 0.000 0.031 0.069

CF ExtendRate 0.087 0.077 0.031 0.071 0.125

IS ExtendRate 0.068 0.075 0.000 0.050 0.105

Size 7.789 1.984 6.496 7.863 9.101

Age 3.055 0.670 2.565 2.996 3.638

StdROE 0.107 0.473 0.007 0.015 0.038

Intang 0.005 0.013 0.000 0.000 0.005

ROA 0.002 0.047 0.001 0.009 0.020

CORR 0.063 0.406 -0.235 0.079 0.373

EPS 0.463 0.807 0.034 0.343 0.760

Q4 0.226 0.418 0.000 0.000 0.000

LnCov 2.114 0.871 1.609 2.197 2.773

LargeFiler 0.689 0.463 0.000 1.000 1.000

AccFiler 0.250 0.433 0.000 0.000 1.000

Horizon 3.670 0.679 3.555 3.892 4.025

Violate 0.208 0.406 0.000 0.000 0.000 This table presents summary statistics for my sample of 21,800 firm-quarter observations for the period from

2010 to 2014, with the exception of StdDev, which is only available for 12,473 firm-quarter observations. All

variables are defined in Appendix B.

Table 4

Pearson Correlations Coefficients

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

(1) Error 1.000

(2) StdDev 0.579 1.000

(3) Range 0.385 0.961 1.000

(4) ExtendRate 0.033 0.080 0.081 1.000

(5) Disc. ExtendRate 0.031 0.068 0.074 0.961 1.000

(6) Stm. ExtendRate 0.037 0.058 0.055 0.656 0.512 1.000

(7) BS ExtendRate 0.007 0.020 0.025 0.502 0.408 0.730 1.000

(8) CF ExtendRate 0.058 0.087 0.074 0.557 0.435 0.849 0.463 1.000

(9) IS ExtendRate -0.002 0.006 0.016 0.469 0.363 0.716 0.380 0.393 1.000

(10) Size -0.209 -0.183 -0.064 0.356 0.312 0.231 0.217 0.146 0.224 1.000

(11) Age -0.127 -0.124 -0.097 0.034 0.043 -0.046 0.027 -0.080 -0.004 0.388 1.000

(12) StdROE 0.124 0.178 0.145 -0.001 0.006 -0.004 -0.037 0.019 -0.006 -0.168 -0.061

(13) Intang 0.188 0.239 0.138 -0.074 -0.061 -0.085 -0.107 -0.033 -0.090 -0.348 -0.103

(14) ROA -0.288 -0.363 -0.249 -0.037 -0.045 -0.034 0.024 -0.073 0.018 0.364 0.181

(15) CORR 0.002 -0.008 -0.005 -0.048 -0.045 -0.033 -0.035 -0.024 -0.027 -0.062 -0.035

(16) EPS -0.218 -0.262 -0.187 0.058 0.052 0.019 0.052 -0.020 0.044 0.374 0.210

(17) Q4 -0.026 0.025 -0.018 0.144 0.030 0.081 0.026 0.086 0.043 0.008 -0.013

(18) LnCov -0.253 -0.192 0.008 0.102 0.082 0.035 0.045 -0.022 0.102 0.592 0.145

(19) LargeFiler -0.254 -0.249 -0.103 0.127 0.103 0.058 0.070 0.008 0.101 0.617 0.271

(20) AccFiler 0.165 0.223 0.101 -0.134 -0.117 -0.062 -0.077 -0.019 -0.084 -0.496 -0.245

(21) Horizon -0.058 -0.104 0.020 -0.106 -0.029 -0.083 -0.034 -0.086 -0.040 0.090 0.074

(22) Violate -0.007 -0.011 -0.025 -0.024 -0.053 0.008 -0.004 0.024 -0.023 0.006 -0.023

40

Table 4 (cont.)

(12) (13) (14) (15) (16) (17) (18) (19) (20) (21)

(12) StdROE 1.000

(13) Intang 0.187 1.000

(14) ROA -0.295 -0.468 1.000

(15) CORR -0.023 -0.013 0.015 1.000

(16) EPS -0.100 -0.189 0.456 -0.007 1.000

(17) Q4 -0.013 -0.016 0.022 0.002 0.023 1.000

(18) LnCov -0.072 -0.146 0.237 -0.044 0.255 0.035 1.000

(19) LargeFiler -0.118 -0.223 0.324 -0.041 0.305 0.025 0.605 1.000

(20) AccFiler 0.080 0.141 -0.222 0.033 -0.259 -0.025 -0.463 -0.860 1.000

(21) Horizon -0.006 -0.025 0.054 -0.014 0.053 -0.656 0.090 0.132 -0.086 1.000

(22) Violate 0.002 -0.038 -0.012 0.005 -0.001 0.118 -0.076 -0.041 0.020 -0.104 This table presents Pearson’s correlation coefficients for my sample of 21,800 firm-quarter observations for the period from 2010 to 2014, with the exception of StdDev, which is only

available for 12,473 firm-quarter observations. Correlations in bold are significant at least at the 10% level. All variables are defined in Appendix B.

41

Table 5

First Stage Regression of Extension Rates Number of Standard Elements

ExtendRate

Disc. ExtendRate

Stm.

ExtendRate BS

ExtendRate CF

ExtendRate IS

ExtendRate

Total Taxonomy Tags -0.013 -0.017 -0.006 -0.006 -0.005 -0.007 (-9.78)*** (-10.10)*** (-6.02)*** (-6.77)*** (-3.47)*** (-4.86)***

Size 0.015 0.016 0.007 0.004 0.009 0.007 (13.23)*** (11.21)*** (7.86)*** (4.69)*** (7.04)*** (5.32)***

Age -0.008 -0.007 -0.008 -0.003 -0.012 -0.005 (-3.72)*** (-2.51)** (-4.97)*** (-1.86)* (-5.25)*** (-2.19)**

StdROE 0.005 0.005 0.002 -0.001 0.004 0.003 (2.25)** (2.06)** (1.28) (-0.66) (1.97)** (1.60)

Intang 0.231 0.226 0.101 0.006 0.151 0.172 (2.51)** (2.02)** (1.51) (0.11) (1.56) (1.92)*

ROA -0.179 -0.198 -0.090 -0.032 -0.151 -0.045 (-6.09)*** (-5.61)*** (-4.43)*** (-1.71)* (-4.98)*** (-1.76)*

CORR -0.003 -0.003 -0.001 -0.002 -0.001 -0.001 (-1.19) (-1.16) (-0.86) (-1.20) (-0.64) (-0.33)

EPS -0.002 -0.001 -0.001 0.000 -0.002 -0.002 (-1.46) (-0.74) (-0.90) (0.17) (-1.17) (-0.95)

Q4 0.021 -0.000 0.005 -0.001 0.010 0.003 (11.70)*** (-0.15) (4.08)*** (-0.63) (5.53)*** (1.43)

LnCov -0.006 -0.007 -0.003 -0.002 -0.006 -0.000 (-3.29)*** (-2.84)*** (-2.30)** (-0.96) (-3.11)*** (-0.14)

LargeFiler -0.022 -0.029 -0.008 -0.008 -0.009 -0.001 (-3.90)*** (-4.50)*** (-1.89)* (-2.00)** (-1.67)* (-0.22)

AccFiler -0.015 -0.021 -0.002 -0.004 -0.002 0.001 (-3.19)*** (-3.82)*** (-0.65) (-1.25) (-0.34) (0.20)

Horizon -0.001 0.000 -0.003 -0.002 -0.003 -0.002 (-0.60) (0.02) (-2.58)** (-2.19)** (-1.99)** (-1.72)*

Violate 0.003 0.001 0.002 0.001 0.003 -0.002 (1.50) (0.45) (1.22) (1.06) (1.58) (-1.25)

Adjusted R2 0.372 0.317 0.248 0.170 0.181 0.160

KP Rank LM Statistic 80.868 85.375 33.894 42.638 11.660 22.859

P-value <0.001 <0.001 <0.001 <0.001 0.001 <0.001

This table presents first stage regression of XBRL extension rates on the total taxonomy tags for my sample of 21,800 firm-quarter for the period

42

from 2010 to 2014. The dependent variables are indicated in the column heading. ExtendRate is the number of extended items in the XBRL filing

divided by the number of total items in the XBRL filing; Disc. ExtendRate is the number of extended items in all sections identified as footnote

disclosures divided by the number of total items in all sections identified as footnote disclosures; Stm. ExtendRate is the number of extended items

in all sections identified as financial statements divided by the number of total items in all sections identified as financial statements; BS

ExtendRate is the number of extended items in the section identified as the balance sheet divided by the number of total items in the section

identified as the balance sheet; CF ExtendRate is the number of extended items in the section identified as the cash flow statement divided by the

number of total items in the sections identified as the cash flow statement; IS ExtendRate is the number of extended items in all sections identified

as an income statement (either the statement of income or the comprehensive statement of income) divided by the number of total items in all

sections identified as an income statement; The instrumental variable is the total number of standard tags defined by the official US GAAP

taxonomy used in the filing. All other variables are defined in Appendix B. ***, **, * indicate significance at the 1%, 5%, and 10% levels of

significance. T-statistics are presented in parentheses and are adjusted for arbitrary heteroskedasticity and standard errors are clustered by firm.

Industry, Year, and Software fixed effects are included in the model but are not reported for brevity.

43

Table 6

Regression of Analysts’ Forecast Properties on Extension Rate

OLS 2SLS

Error StdDev Range Error StdDev Range

ExtendRate 0.010 0.004 0.009 -0.055 -0.034 -0.065 (4.23)*** (4.52)*** (5.58)*** (-4.03)*** (-5.01)*** (-5.00)***

Size 0.001 0.000 0.000 0.002 0.001 0.001 (3.62)*** (2.32)** (1.68)* (5.59)*** (5.25)*** (5.20)***

Age -0.001 -0.000 -0.001 -0.001 -0.001 -0.001 (-3.47)*** (-1.56) (-2.77)*** (-4.56)*** (-3.10)*** (-4.09)***

StdROE 0.001 0.001 0.001 0.001 0.001 0.002 (2.28)** (4.02)*** (4.50)*** (2.74)*** (4.62)*** (4.59)***

Intang 0.104 0.058 0.054 0.119 0.066 0.072 (4.67)*** (5.73)*** (3.62)*** (5.14)*** (5.77)*** (4.17)***

ROA -0.041 -0.020 -0.028 -0.053 -0.026 -0.042 (-7.37)*** (-7.15)*** (-7.48)*** (-8.53)*** (-7.18)*** (-8.06)***

CORR -0.000 -0.000 0.000 -0.000 -0.000 -0.000 (-0.75) (-0.47) (0.31) (-1.19) (-0.75) (-0.43)

EPS -0.001 -0.000 -0.001 -0.001 -0.001 -0.001 (-5.49)*** (-4.30)*** (-6.13)*** (-5.45)*** (-4.32)*** (-5.76)***

Q4 -0.002 -0.000 -0.000 -0.001 0.000 0.001 (-4.85)*** (-2.85)*** (-0.06) (-1.75)* (0.60) (3.68)***

LnCov -0.003 -0.001 0.001 -0.003 -0.001 0.000 (-9.13)*** (-5.65)*** (3.55)*** (-9.04)*** (-5.66)*** (1.04)

LargeFiler -0.006 -0.001 -0.001 -0.008 -0.002 -0.002 (-6.47)*** (-3.10)*** (-1.41) (-7.24)*** (-4.30)*** (-3.31)***

AccFiler -0.004 -0.000 0.001 -0.005 -0.001 -0.000 (-4.40)*** (-0.72) (2.04)** (-5.07)*** (-1.49) (-0.03)

Horizon -0.001 -0.001 0.001 -0.001 -0.001 0.001 (-1.94)* (-3.43)*** (5.91)*** (-2.16)** (-3.72)*** (4.53)***

Violate -0.000 0.000 0.000 0.000 0.000 0.000 (-0.01) (0.53) (1.61) (0.53) (1.46) (2.14)**

This table presents regression of analysts’ forecast properties on XBRL extension rates for my sample of 21,800 firm-quarter observations (12,473

firm-quarter observations for models that include StdDev) for the period from 2010 to 2014. The first three columns present the OLS estimates

whereas the last three columns present the 2SLS estimates. The dependent variables are indicated in the column heading. Error is the magnitude of

the median one period ahead quarterly EPS in the 30-day following the filing minus the respective actual quarterly EPS, scaled by the stock price

44

just prior to the filing; StdDev is the standard deviation of one-period ahead quarterly EPS forecast in the 30-day period following the XBRL

filing scaled by the stock price just prior to the filing; and Range is the range of one-period ahead quarterly EPS forecast in the 30-day period

following the XBRL filing scaled by the stock price just prior to the filing. The independent variables of interest is the document extension rate

(ExtendRate) defined as the number of extended items in the XBRL filing divided by the number of total items in the XBRL filing; All other

variables are defined in Appendix B. ***, **, * indicate significance at the 1%, 5%, and 10% levels of significance. T-statistics are presented in

parentheses and are adjusted for arbitrary heteroscedasticity, and standard errors are clustered by firm. Industry, Year, and Software fixed effects

are included in the model but are not reported for brevity.

45

Table 7

Regression of Analysts’ Forecast Properties on Extension Rates by Section

Disclosures Statements


Extension Rate -0.043 -0.027 -0.051 -0.130 -0.078 -0.155 (-4.05)*** (-5.06)*** (-5.07)*** (-3.57)*** (-4.18)*** (-4.22)***

Size 0.001 0.001 0.001 0.002 0.001 0.001 (5.73)*** (5.17)*** (5.16)*** (4.81)*** (4.24)*** (4.31)***

Age -0.001 -0.000 -0.001 -0.002 -0.001 -0.002 (-4.22)*** (-2.60)*** (-3.66)*** (-4.73)*** (-3.53)*** (-4.37)***

StdROE 0.001 0.001 0.002 0.001 0.001 0.002 (2.71)*** (4.60)*** (4.59)*** (2.51)** (4.21)*** (4.09)***

Intang 0.116 0.063 0.068 0.120 0.067 0.072 (5.04)*** (5.63)*** (4.03)*** (4.93)*** (5.52)*** (3.73)***

ROA -0.051 -0.025 -0.040 -0.055 -0.026 -0.044 (-8.51)*** (-7.20)*** (-8.12)*** (-7.94)*** (-6.71)*** (-7.13)***

CORR -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 (-1.18) (-0.84) (-0.39) (-1.15) (-0.85) (-0.47)

EPS -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 (-5.41)*** (-4.24)*** (-5.73)*** (-4.74)*** (-3.51)*** (-4.60)***

Q4 -0.002 -0.001 -0.000 -0.001 -0.000 0.001 (-5.10)*** (-3.25)*** (-1.05) (-2.96)*** (-1.52) (1.88)*

LnCov -0.003 -0.001 0.000 -0.004 -0.001 0.000 (-9.11)*** (-5.62)*** (1.37) (-8.24)*** (-4.61)*** (0.51)

LargeFiler -0.008 -0.002 -0.002 -0.007 -0.002 -0.002 (-7.34)*** (-4.54)*** (-3.41)*** (-6.56)*** (-3.39)*** (-2.49)**

AccFiler -0.005 -0.001 -0.000 -0.005 -0.000 0.001 (-5.16)*** (-1.76)* (-0.18) (-4.41)*** (-0.77) (0.81)

Horizon -0.001 -0.001 0.001 -0.001 -0.001 0.000 (-2.03)** (-3.49)*** (4.98)*** (-2.84)*** (-4.05)*** (1.70)*

Violate 0.000 0.000 0.000 0.000 0.000 0.001 (0.22) (1.07) (1.67)* (0.65) (1.43) (1.95)*

Standardized

Coefficient -0.311 -0.557 -0.526 -0.530 -0.884 -0.896

This table presents the 2SLS regression estimates of analysts’ forecast properties on XBRL extension rates for my sample of 21,800 firm-quarter

observations (12,473 firm-quarter observations for models that include StdDev) for the period from 2010 to 2014. The first three columns use the

46

extension rate within footnote disclosures as the independent variable of interest. The last three columns use the extension rate within financial

statement as the independent variable of interest. The dependent variables are indicated in the column heading. Error is the magnitude of the median

one-period ahead quarterly EPS in the 30-day following the filing minus the respective actual quarterly EPS, scaled by the stock price just prior to

the filing; StdDev is the standard deviation of one-period ahead quarterly EPS forecast in the 30-day period following the XBRL filing scaled

by the stock price just prior to the filing; and Range is the range of one-period ahead quarterly EPS forecast in the 30-day period following the

XBRL filing scaled by the stock price just prior to the filing. All other variables are defined in Appendix B. The last row reports the standardized

(beta) coefficients for the extension rate variable. ***, **, * indicate significance at the 1%, 5%, and 10% levels of significance. T-statistics are

presented in parentheses and are adjusted for arbitrary heteroscedasticity, and standard errors are clustered by firm. Industry, Year, and Software

fixed effects are included in the model but are not reported for brevity.

47

Table 8

Regression of Analysts’ Forecast Properties on Extension Rates by Statement

Balance Sheet Cash Flow Statement Income Statement

Error StdDev Error StdDev Error StdDev

Extension Rate -0.115 -0.074 -0.154 -0.088 -0.108 -0.067 (-3.72)*** (-4.22)*** (-2.72)*** (-2.95)*** (-3.30)*** (-3.54)***

Size 0.001 0.001 0.002 0.001 0.001 0.001 (5.10)*** (3.79)*** (3.54)*** (3.23)*** (4.56)*** (3.67)***

Age -0.001 -0.001 -0.003 -0.001 -0.002 -0.001 (-4.04)*** (-2.83)*** (-3.58)*** (-2.77)*** (-3.95)*** (-2.41)**

StdROE 0.001 0.001 0.002 0.001 0.001 0.001 (2.03)** (3.35)*** (2.65)*** (4.00)*** (2.62)*** (3.83)***

Intang 0.107 0.055 0.130 0.077 0.125 0.074 (4.62)*** (4.70)*** (4.52)*** (4.79)*** (5.07)*** (5.44)***

ROA -0.047 -0.022 -0.066 -0.032 -0.048 -0.022 (-7.77)*** (-5.96)*** (-6.06)*** (-5.51)*** (-7.42)*** (-5.84)***

CORR -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 (-1.32) (-1.45) (-1.00) (-0.67) (-0.85) (-0.32)

EPS -0.001 -0.001 -0.002 -0.001 -0.002 -0.001 (-4.45)*** (-3.22)*** (-3.91)*** (-2.94)*** (-4.86)*** (-3.19)***

Q4 -0.002 -0.001 -0.000 0.000 -0.002 -0.001 (-4.98)*** (-3.46)*** (-0.44) (0.71) (-3.93)*** (-2.52)**

LnCov -0.003 -0.001 -0.004 -0.002 -0.003 -0.001 (-8.18)*** (-4.06)*** (-6.56)*** (-3.93)*** (-7.53)*** (-3.74)***

LargeFiler -0.007 -0.002 -0.008 -0.003 -0.006 -0.001 (-6.84)*** (-3.72)*** (-5.54)*** (-2.95)*** (-5.90)*** (-1.41)

AccFiler -0.005 -0.001 -0.005 -0.001 -0.004 0.000 (-4.71)*** (-1.23) (-3.77)*** (-1.03) (-4.11)*** (0.45)

Horizon -0.001 -0.001 -0.001 -0.001 -0.001 -0.001 (-2.67)*** (-4.02)*** (-2.59)*** (-3.04)*** (-2.62)*** (-3.68)***

Violate 0.000 0.000 0.000 0.000 -0.000 -0.000 (0.54) (1.18) (1.05) (1.57) (-0.58) (-0.12)

Standardized

Coefficient -0.445 -0.820 -0.870 -1.376 -0.597 -1.075

This table presents the 2SLS regression estimates of analysts forecast properties on XBRL extension rates for my sample of 21,800 firm-quarter

observations(12,473 firm-quarter observations for models that include StdDev) for the period from 2010 to 2014. The first two columns use the extension

48

rate within the balance sheet as the independent variable of interest. The next two columns use the extension rate within cash flow statement as the

independent variable of interest. The last two columns use the extension rate within income statement as the independent variable of interest. The

dependent variables are indicated in the column heading. Error is the magnitude of the median one period ahead quarterly EPS in the 30-day

following the filing minus the respective actual quarterly EPS, scaled by the stock price just prior to the filing and StdDev is the standard deviation

of one-period ahead quarterly EPS forecast in the 30-day period following the XBRL filing scaled by the stock price just prior to the filing. All

other variables are defined in Appendix B. The last row reports the standardized (beta) coefficients for the extension rate variable. ***, **, * indicate

significance at the 1%, 5%, and 10% levels of significance. T-statistics are presented in parentheses and are adjusted for arbitrary heteroscedasticity,

and standard errors are clustered by firm. Industry, Year, and Software fixed effects are included in the model but are not reported for brevity.

49

Table 9

Cross-sectional differences in the association between XBRL Extensions and Analysts’ Forecast Error and Forecast Dispersion

Panel A: StdRet

Error StdDev

High Low Diff High Low Diff

Extension Rate -0.108 -0.000 -0.108 -0.057 -0.008 -0.049

(-3.11)*** (-0.04) (-3.02)*** (-3.73)*** (-1.96)** (-3.13)***

Observations 10,397 10,404 5,700 6,121

Kleibergen-Paap Rank LM 23.695 62.322 22.778 49.289

P-value <0.001 <0.001 <0.001 <0.001

Panel B: StdROE

Error StdDev


Extension Rate -0.064 -0.020 -0.044 -0.050 -0.007 -0.043

(-2.72)*** (-1.94)* (-1.72)* (-3.76)*** (-2.06)** (-3.16)***

Observations 10,653 10,654 6,157 6,013


P-value <0.001 <0.001 <0.001 <0.001

Panel C: XBRL Technical Errors

Error StdDev

Tech. Error No Tech Error Diff Tech. Error No Tech Error Diff

Extension Rate -0.046 -0.050 0.004 0.024 -0.034 0.057

(-0.32) (-3.82)*** (0.02) (0.08) (-5.07)*** (0.19)

Observations 4,539 17,261 2,505 9,968


P-value 0.275 <0.001 0.857 <0.001 Continued on the next page

50

Table 9 (Continued)

Panel D: AcctComp4

Error StdDev


Extension Rate -0.012 -0.066 0.053 -0.012 -0.045 0.034

(-1.14) (-2.46)** (1.86)* (-1.89)* (-3.05)*** (2.07)**

Observations 6,775 6,787 3,850 3,819


P-value <0.001 <0.001 <0.001 <0.001

Panel E: AcctCompInd

Error StdDev


Extension Rate 0.005 -0.086 0.091 -0.007 -0.049 0.042

(0.42) (-2.95)*** (2.88)*** (-1.23) (-3.45)*** (2.75)***

Observations 6,791 6,771 3,789 3,880


P-value <0.001 <0.001 <0.001 <0.001

Panel F: Abs(AbAcc)

Error StdDev


Extension Rate -0.069 -0.028 -0.041 -0.046 -0.023 -0.023

(-1.93)* (-1.97)** (-1.05) (-2.44)** (-2.64)*** (-1.08)

Observations 7,203 7,202 4,005 4,467


P-value <0.001 <0.001 <0.001 <0.001 Continued on the next page

51

Table 9 (Continued)

Panel G: AQ

Error StdDev


Extension Rate -0.086 -0.007 -0.079 -0.047 -0.021 -0.026

(-2.72)*** (-0.52) (-2.22)** (-2.74)*** (-2.36)** (-1.36)

Observations 7,330 7,323 4,139 4,618


P-value <0.001 <0.001 <0.001 <0.001

This table presents regression of analysts’ forecast properties on XBRL over all extension rates for my sample for the period from 2010 to 2014.

Subsamples are formed based on the annual median value of the respective partitioning variable. Firm-quarter observations vary with the different

sub-samples and are reported in the table. Panel A uses Abs(AbAcc), the magnitude of the Modified-Jones Model abnormal accruals for the previous

fiscal year, as the partitioning variable. The partitioning variable in Panel B is the AQ which is the accruals estimation error measured following

Francis et al. (2005). The partitioning variables in Panel C and D are the standard deviation of returns (StdRet) measured over the previous fiscal

year and the standard deviation of ROE (StdROE) measured over the four quarter period ending at the beginning of the current fiscal year. The

dependent variables are indicated in the column heading. Error is the magnitude of the median one-period ahead quarterly EPS in the 30-day

following the filing minus the respective actual quarterly EPS, scaled by the stock price just prior to the filing; and StdDev is the standard deviation

of one-period ahead quarterly EPS forecast in the 30-day period following the XBRL filing scaled by the stock price just prior to the filing. The

independent variables of interest is the document extension rate (ExtendRate) defined as the number of extended items in the XBRL filing divided

by the number of total items in the XBRL filing; All other variables are defined in Appendix B. ***, **, * indicate significance at the 1%, 5%, and

10% levels of significance. T-statistics are presented in parentheses and are adjusted for arbitrary heteroscedasticity, and standard errors are clustered

by firm. Industry, Year, and Software fixed effects are included in the model but are not reported for brevity. Control variables indicated in equations

1 and 2 are included in the model but not reported.

Table 10

First Stage Regression of Number of Tags on the Number of Standard Tags

Total Tags Extended Tags

Total Taxonomy Tags 0.037 -0.028 (6.93)*** (-2.53)**

Size 0.110 0.193 (22.34)*** (19.25)***

Age -0.030 -0.081 (-3.40)*** (-4.51)***

StdROE 0.019 0.048 (2.55)** (2.48)**

Intang 0.182 1.496 (0.51) (1.79)*

ROA -0.266 -1.323 (-2.50)** (-5.11)***

CORR -0.003 -0.020 (-0.32) (-1.08)

EPS -0.029 -0.045 (-4.72)*** (-3.62)***

Q4 0.613 0.758 (74.95)*** (46.64)***

LnCov -0.063 -0.093 (-7.76)*** (-5.67)***

LargeFiler -0.027 -0.116 (-1.26) (-2.69)***

AccFiler -0.040 -0.102 (-2.20)** (-2.67)***

Horizon -0.016 -0.017 (-3.19)*** (-1.57)

Violate 0.081 0.104 (11.72)*** (6.54)***

Adjusted R2 0.641 0.503

KP Rank LM Statistic 45.394 142.137

P-value <0.001 <0.001

This table presents first stage regression of XBRL extension rates on the total taxonomy tags for my

sample of 21,800 firm-quarter for the period from 2010 to 2014. The dependent variables are indicated

in the column heading. Total Tags is the total number of unique tags defined within the filings, and

Extended Tags is the number of unique extended items used in a filing. The instrumental variable (Total

Taxonomy Tags) is the total number of standard tags defined by the official US GAAP taxonomy used

in the filing. All other variables are defined in Appendix B. ***, **, * indicate significance at the 1%,

5%, and 10% levels of significance. T-statistics are presented in parentheses and are adjusted for

arbitrary heteroscedasticity, and standard errors are clustered by firm. Industry, Year, and Software

fixed effects are included in the model but are not reported for brevity

Table 11

Regression of Analysts’ Forecast Properties on the Total Number of Tags

OLS 2SLS


Total Tags 0.003 0.001 0.003 0.020 0.012 0.024

(5.80)*** (4.54)*** (6.84)*** (3.95)*** (4.51)*** (4.79)***

Size 0.000 0.000 0.000 -0.002 -0.001 -0.002

(2.37)** (1.58) (0.39) (-2.65)*** (-3.62)*** (-4.10)***

Age -0.001 -0.000 -0.001 -0.000 0.000 -0.000

(-3.42)*** (-1.59) (-2.74)*** (-1.18) (0.88) (-0.01)

StdROE 0.001 0.001 0.001 0.001 0.000 0.001

(2.26)** (4.09)*** (4.50)*** (1.54) (2.57)** (3.14)***

Intang 0.106 0.059 0.056 0.103 0.056 0.052

(4.77)*** (5.83)*** (3.75)*** (4.59)*** (5.34)*** (3.31)***

ROA -0.042 -0.021 -0.029 -0.038 -0.019 -0.024

(-7.63)*** (-7.24)*** (-7.67)*** (-6.34)*** (-6.31)*** (-5.41)***

CORR -0.000 -0.000 0.000 -0.000 -0.000 0.000

(-0.82) (-0.52) (0.23) (-0.58) (-0.25) (0.40)

EPS -0.001 -0.000 -0.001 -0.001 -0.000 -0.001

(-5.27)*** (-4.14)*** (-5.90)*** (-2.74)*** (-0.73) (-2.17)**

Q4 -0.003 -0.001 -0.001 -0.014 -0.008 -0.015

(-6.83)*** (-4.94)*** (-4.74)*** (-4.37)*** (-4.65)*** (-4.72)***

LnCov -0.003 -0.001 0.001 -0.002 -0.000 0.002

(-8.69)*** (-5.20)*** (3.98)*** (-3.88)*** (-0.04) (5.34)***

LargeFiler -0.006 -0.001 -0.001 -0.006 -0.001 -0.000

(-6.60)*** (-3.19)*** (-1.64) (-5.48)*** (-1.30) (-0.44)

AccFiler -0.004 -0.000 0.001 -0.004 0.000 0.002

(-4.43)*** (-0.70) (1.97)** (-3.48)*** (0.50) (2.67)***

Horizon -0.001 -0.001 0.001 -0.000 -0.000 0.001

(-1.86)* (-3.27)*** (6.08)*** (-0.95) (-0.57) (6.01)***

Violate -0.000 -0.000 0.000 -0.002 -0.001 -0.002

(-0.69) (-0.03) (0.59) (-3.23)*** (-3.66)*** (-3.61)***

54

This table presents regression of analysts’ forecast properties on XBRL extension rates for my sample of 21,800 firm-quarter observations (12,473 firm-quarter

observations for models that include StdDev) for the period from 2010 to 2014. The first three columns present the OLS estimates whereas the last three columns

present the 2SLS estimates. The dependent variables are indicated in the column heading. Error is the magnitude of the median one-period ahead quarterly EPS

in the 30-day following the filing minus the respective actual quarterly EPS, scaled by the stock price just prior to the filing; StdDev is the standard deviation

of one-period ahead quarterly EPS forecast in the 30-day period following the XBRL filing scaled by the stock price just prior to the filing; and Range is

the range of one-period ahead quarterly EPS forecast in the 30-day period following the XBRL filing scaled by the stock price just prior to the filing. The

independent variables of interest is the total number of unique tags defined within the filings. All other variables are defined in Appendix B. ***, **, * indicate

significance at the 1%, 5%, and 10% levels of significance. T-statistics are presented in parentheses and are adjusted for arbitrary heteroscedasticity, and standard

errors are clustered by firm. Industry, Year, and Software fixed effects are included in the model but are not reported for brevity.

55

Table 12

Regression of Analysts’ Forecast Properties on the Number of Extended Tags

OLS 2SLS


Extended Tags 0.001 0.000 0.001 -0.007 -0.005 -0.009

(2.18)** (3.04)*** (3.02)*** (-3.73)*** (-5.17)*** (-4.97)***

Size 0.000 0.000 0.000 0.000 0.000 0.000

(2.30)** (1.50) (0.30) (2.78)*** (2.11)** (1.21)

Age -0.001 -0.000 -0.001 -0.001 -0.000 -0.001

(-3.34)*** (-1.48) (-2.64)*** (-4.00)*** (-2.45)** (-3.50)***

StdROE 0.001 0.001 0.001 0.001 0.001 0.002

(2.24)** (4.01)*** (4.48)*** (2.41)** (4.38)*** (4.41)***

Intang 0.105 0.058 0.055 0.115 0.063 0.067

(4.73)*** (5.79)*** (3.69)*** (5.07)*** (5.80)*** (4.14)***

ROA -0.041 -0.020 -0.029 -0.048 -0.024 -0.037

(-7.46)*** (-7.18)*** (-7.55)*** (-8.31)*** (-7.46)*** (-8.17)***

CORR -0.000 -0.000 0.000 -0.000 -0.000 -0.000

(-0.77) (-0.49) (0.29) (-1.14) (-0.70) (-0.32)

EPS -0.001 -0.000 -0.001 -0.001 -0.000 -0.001

(-5.28)*** (-4.14)*** (-5.91)*** (-5.06)*** (-3.85)*** (-5.48)***

Q4 -0.003 -0.001 -0.001 -0.005 -0.002 -0.004

(-6.21)*** (-4.29)*** (-3.89)*** (-7.14)*** (-6.65)*** (-6.66)***

LnCov -0.003 -0.001 0.001 -0.003 -0.001 0.001

(-8.71)*** (-5.27)*** (3.94)*** (-8.19)*** (-4.08)*** (4.00)***

LargeFiler -0.006 -0.001 -0.001 -0.007 -0.002 -0.002

(-6.55)*** (-3.10)*** (-1.54) (-6.85)*** (-3.83)*** (-2.38)**

AccFiler -0.004 -0.000 0.001 -0.005 -0.001 0.001

(-4.41)*** (-0.67) (2.03)** (-4.58)*** (-1.01) (1.21)

Horizon -0.001 -0.001 0.001 -0.001 -0.001 0.001

(-1.87)* (-3.31)*** (6.05)*** (-1.68)* (-2.60)*** (5.86)***

Violate -0.000 0.000 0.000 -0.000 -0.000 -0.000

(-0.62) (0.04) (0.72) (-1.30) (-0.68) (-0.67)

56

This table presents regression of analysts’ forecast properties on XBRL extension rates for my sample of 21,800 firm-quarter observations (12,473 firm-quarter

observations for models that include StdDev) for the period from 2010 to 2014. The first three columns present the OLS estimates whereas the last three columns

present the 2SLS estimates. The dependent variables are indicated in the column heading. Error is the magnitude of the median one-period ahead quarterly EPS

in the 30-day following the filing minus the respective actual quarterly EPS, scaled by the stock price just prior to the filing; StdDev is the standard deviation

of one-period ahead quarterly EPS forecast in the 30-day period following the XBRL filing scaled by the stock price just prior to the filing; and Range is

the range of one-period ahead quarterly EPS forecast in the 30-day period following the XBRL filing scaled by the stock price just prior to the filing. The

independent variables of interest are the natural log of the number of unique extended tags defined in the filing, All other variables are defined in Appendix B.

***, **, * indicate significance at the 1%, 5%, and 10% levels of significance. T-statistics are presented in parentheses and are adjusted for arbitrary

heteroscedasticity, and standard errors are clustered by firm. Industry, Year, and Software fixed effects are included in the model but are not reported for brevity.

extended xbrl taxonomies and financial …€¦ · aaa ais mid-year meeting. ... the work described...

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