A New Measure Of Disclosure Quality: The Level Of Disaggregation Of Accounting Data In Annual Reports
University of Texas at Austin – Red McCombs School of Business
Up-and-Coming Small- and Mid-cap Portfolio Managers #MICUS (Morningstar Conference)
National University of Singapore
University of California-Irvine
August 26, 2015
We construct a new, parsimonious, measure of disclosure quality – disaggregation quality (DQ) – and offer validation tests. DQ captures the level of disaggregation of accounting data through a count of non-missing Compustat line items, and reflects the extent of details in firms’ annual reports. Conceptually DQ differs from existing disclosure measures in that it captures the ‘fineness’ of data and is based on a comprehensive set of accounting line items in annual reports. Unlike existing measures which are usually applicable for a subset of firms or are based on a subset of information items, DQ can be generated for the universe of Compustat industrial firms. We conduct three sets of validation tests by examining DQ’s association with variables predicted by prior literature to be associated with information quality. DQ is negatively (positively) associated with analyst forecast dispersion (accuracy), negatively associated with bid-ask spreads and cost of equity. These associations continue to hold after we control for firm fundamentals. Taken together, results from this battery of validation tests are consistent with our measure capturing disclosure quality.
A New Measure Of Disclosure Quality: The Level Of Disaggregation Of Accounting Data In Annual Reports – Introduction
We construct a new measure of disclosure quality, disaggregation quality (DQ), based on the level of disaggregation of financial data items in firms’ annual reports, and provide validation tests. We base DQ on the theoretical premise that finer information is of higher quality (Blackwell 1951). Greater disaggregation leads to more, finer information available to investors. More detailed disclosure reduces information asymmetry, arguably increases the precision of the information in the financial statements, and provides investors with more information for valuation and mitigates mispricing (Fairfield, Sweeney and Yohn 1996; Jegadeesh and Livnat 2006). Greater disaggregation also enhances the credibility of firms’ financial report as it gives managers less degrees of freedom to manage the reported numbers (Hirst, Koonce, and Venkataraman 2007; D’Souza, Ramesh and Shen 2010), enhancing the contracting and stewardship role of accounting information. Reasoning along this line, we argue that a greater degree of disaggregation represents higher disclosure quality.
DQ is parsimonious and applicable to all Compustat industrial firms: we count the number of non-missing financial items reported in firms’ annual reports, including items both in the financial statements and in the footnotes, as captured by Compustat. A higher count of nonmissing accounting data items represents higher disclosure quality. Despite a vast empirical literature on disclosure in general and voluntary disclosure in particular, there is surprisingly no overall measure of disclosure quality based on a comprehensive set of accounting data as reported in financial reports.
DQ is conceptually very different from existing measures of disclosure quality, which are either voluntary disclosure measures such as management forecasts and conference calls, or researcher self-constructed indices (e.g., Botosan 1997; Francis, Nanda, and Olsson 2008), or analyst ratings such as the now-discontinued AIMR scores, or the narrative quality of MD&A in annual reports, such as the Fog Index (Li, 2008). DQ differs from all the above measures in that DQ captures the ‘fineness’ of data, as reflected in the level of disaggregation of accounting data items in the financial statements.
We capture the degree of disaggregation in GAAP line items in firms’ annual reports by counting the number of non-missing Compustat items, with a bigger number representing higher disclosure quality. In constructing DQ we take multiple steps to mitigate the impact of Compustat’s systematic coding scheme on the count of missing items: Compustat can code an item as missing when a firm does not report it, or when a firm does not have it because the item is irrelevant (e.g., inventory to an internet company). Our empirical screening mechanisms help purge cases where an item irrelevant to a firm’s operations is coded as missing by Compustat. Assuming Compustat’s data collection is not systematically biased2, missing data items (after our adjustments, to be discussed in detail in Section 3) would suggest that the firm does not provide the associated information in its annual reports. In other words, the number of non-missing items for each firm-year captures how detailed firms’ financial statements are and can be used as an overall measure of disclosure quality of the company’s annual reports filed that year.
We employ the nesting feature of the Balance Sheet and, to a lesser extent, the Income Statement (i.e., the individual accounts, such as accounts receivable/payable, add up to total assets/total liabilities and shareholders’ equity; on the income statement, current income tax and deferred income tax add up to total income tax expense) and implement multiple screening mechanisms to purge the effects of the Compustat coding scheme in counting missing items on the two financial statements. We do not have a DQ measure for the Statement of Cash Flows because the variation in the number of missing items from the Statement of Cash Flows is minimal.
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