A New Measure of Disclosure Quality: The Level of Disaggregation of Accounting Data in Annual Reports

Abstract

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 nonmissing 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) and 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.

Imperfect Accounting and Reporting Bias

“…We assume that a larger NItems indicates greater transaction complexity and creates more opportunities for errors. Chen, Miao, and Shevlin [] find that NItems is positively related to disclosure quality after controlling for transaction complexity. An overall positive correlation between NItems and Error % suggests that, while higher disclosure quality could mean fewer errors, the primary effect of NItems on Error % is through transaction complexity.…”

When Companies Use Their Wiggle Room, Which Investors Care?

“…Exposure to the FF-3 small-minus-big portfolio, computed using a rolling 12 quarters window (Winsorized at 1%) Fama and French (1996) σ PWR Time series volatility of quarterly PWR, for the period a given investor is in the dataset Author (continued) Long-term effective cash tax rate, that captures the average cah taxes paid per dollar of pre-tax earnings over five years, as described in Dyreng et al (2008) Compustat DQ Disaggregation quality of financial statements, which captures the level of detail with which anual reports are presemted, as described in Chen et al (2015) Compustat DTAX Discretionary permanent tax differences, which capture the residual after estimating the permanent book-tax differences, as described in Frank et al (2009) Compustat EM Earnings management proxy, computed as the average industry-year ranking of discretionary accruals using the following models for estimated total accruals: Dechow et al (1995), Jones (1991), and Kothari et al (2005) Compustat highVIX Indicator for quarters when the mean VIX index lies above the 90th percentile FRED just beat Indicator for beating quarterly median analyst forecasts by no more than 1 USD cent, computed according to Bhojraj et al (2009) I/B/E/S Sent ⊥ Quarterly average of orthogonalized market sentiment, as described in Baker and Wurgler (2006) Baker and Wurgler (2006) #tax havens Indicator for above industry-average number of subsidiaries in tax haven countries, scaled by the logarithm of firms' assets Dyreng and Lindsey (2009) ∅VIX Quarterly average of the CBOE Volatility Index (VIX) FRED (continued) Table 1: Variable definitions (ctd.) WR Exploited regulatory "wiggle room" (WR), computed as the average industry-year percentile rankings of: earnings management (EM), minus disaggregation quality of financial statements (DQ), long-term effective cash tax rate (CashETR), and discretionary permanent tax differences (DTAX), together with an indicator for above industry-average number of subsidiaries in tax haven countries (scaled by firm size), and an indicator for beating median analyst forecasts by no more than 1 USD cent (beat) Author #WRcomp Number of proxies that are available for the computation of WR for a given fiscal year Author Table 2: Firm-level summary statistics The table provides descriptive statistics for firm-level WR and for the proxies used for its computation, displayed in absolute terms (instead of percentile rankings).…”

“…The extent of detail with which a firm presents its financial results is measured by the disaggregation quality (DQ) of financial statements proposed by Chen et al (2015). If a firm wants to hide poor performance or worrisome positions, it will tend to aggregate several balance sheet items or income statement lines which reduces DQ.…”

“…In order to assess WR exploitation at the individual holdings level, I use several proxies: (1) earnings management (Jones, 1991;Dechow et al, 1995;Kothari et al, 2005), (2) disaggregation quality of financial statements (Chen et al, 2015), (3) long-term effective cash tax rate (Dyreng et al, 2008), (4) discretionary permanent tax differences (Frank et al, 2009), (5) subsidiaries in tax haven countries (Dyreng and Lindsey, 2009), and (6) beating the median consensus earnings forecast by no more than one USD cent (Bhojraj et al, 2009). To identify aggressive firms, I construct percentile rankings of (1) -(4) for every industry-year combination.…”

Value relevance and codes of ethics: an empirical analysis of Italian listed companies

“…A sizeable proportion of the literature focuses on the quality of financial disclosure, considered as the main means by which stakeholders can understand the firm's current economic and financial conditions and how it proposes to achieve future objectives (Tucker, 2015;SEC, 2003;Singhvi and Desai, 1971;Chen et al, 2015;Lobo and Zhou, 2001). According to a large part of the academic literature (Barth et al, 2001;Holthausen and Watts, 2001), the most important quality parameter that makes accounting information useful to investors is the concept of 'value relevance', which states that an accounting amount is only 'value relevant' if it provides investors with information relevant to their evaluating of the firm through its stock price.…”