Abstract
One approach to achieving comparable financial statements is to adhere to identical (or converged) standards, methods, models, and estimates. However, adherence to identical standards, methods, models, and estimates is impractical and contrary to current trends in standard setting. As an alternative, the FASB has proposed that satisfying the fundamental characteristics of relevance and representational faithfulness should result in higher comparability. Under this assumption, a focus on increasing the quality of the financial information that is generated by firms would be an effective means of improving comparability in financial reporting. We inquire whether this proposition is reflected in the practices of U.S. firms. Our analysis corroborates the intuition of the FASB and the notion that distinct characteristics of financial information influence accounting comparability. Our results also suggest that accounting principles can enhance comparability by encouraging high-quality valuations across diverse asset and liability classes on balance sheets and high-quality estimates of operating performance in income statements.
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Notes
Here, we are referring to uniformity in the standards, not uniformity in accounting outcomes which could lead to low comparability.
A few studies also examine the relation between comparability and value relevance. Kim et al. (2018) and Chen et al. (2020a, b) examine the relationship between comparability and the value relevance of earnings. Hinson et al. (2019) report a tradeoff in the comparability and relevance of segment reporting. None of these studies examine accruals, specifically. They are also silent on how comparability is related to the persistence of financial statement components or timeliness of investors’ pricing of that information.
See Dechow et al. (2010) for a broader discussion of the literature’s approach to investigating accounting quality.
Financial statement components can deviate from economic fundamentals due to both error and opportunism. In our study, we are agnostic about the source of any deviation.
This logic relies on a maintained hypothesis that, on average, a given firm’s peers will tend towards high quality accounting given the nature of GAAP. It is possible that a firm’s peers generate low quality financial statements, on average. This would bias against finding the proposed positive link between comparability and the relevance and RF characteristics.
We generate multiple measures of income statement and balance sheet RF and relevance. We also separately discuss these two fundamental characteristics because they reflect distinct theoretical constructs. However, empirically separating RF and relevance is a non-trivial task. Even FASB SFAC 8 notes, “Empirical accounting researchers have accumulated considerable evidence supporting relevant and faithfully represented financial information through correlation with changes in the market prices of entities’ equity or debt instruments. However, such studies have not provided techniques for empirically measuring faithful representation apart from relevance.” Likewise, the empirical proxies that we use are almost certainly imperfect measures of RF and relevance.
Following De Franco et al. (2011), we only use firm i’s stock returns to compute predicted earnings. This design choice follows from our desire to generate a firm-specific annual measure of comparability that compares a single firm’s predicted income conditional on its underlying economics (i.e., stock return) under both its own accounting function and under a peer’s accounting function. Similarly, we would anchor on firm j’s returns for its annual measure. If, instead, we were interested in a specific pair of firms, it would be reasonable to use the firm-pair average predicted values conditional on both firms’ stock returns. See De Franco et al. (2011) for a more in-depth discussion of the assumptions that go into their variable measurement.
Our measure of earnings’ relevance to default risk assessment is novel as far as we are aware. Thus, it has not been validated. However, our use of the measure is conceptually similar to prior research that examines the sensitivity of credit ratings to constructs such as accruals (Demirtas and Cornaggia 2013), earnings volatility (Duh et al. 2012), book-tax differences (Ayers et al. 2010), and corporate social responsibility (CSR) (Attig et al. 2013).
Our inferences are unchanged if we use raw values. However, standardized measures permit effect size comparisons across measures with difference scales.
All reported results are robust to using the median or 75th percentile as cutoffs to separate low- and high-comparability firms.
All reported results based on annual cross-sectional regressions are robust to using pooled OLS with year and industry effects and standard errors clustered by firm.
We also use two approaches to improve covariate balance between high and low comparability firms. First, we propensity score match without replacement by estimating the likelihood that any given firm would be a high-comparability firm conditional on market value, book-to-market ratio, operating cash flows, ROA, and stock-return momentum. Second, we entropy-balance the full sample by assigning a weight to each low-comparability observation to minimize the differences in means between the high- and low-comparability samples across the five characteristics. Both matching approaches successfully reduce or remove differences between low- and high-comparability firms, and the results using these approaches lead to the same inferences as those reported (untabulated).
All reported results are robust to excluding firms with negative book-to-market ratios or excluding financial firms.
We require that a firm have at least one year of earnings or cash flow data available during the four-year window for inclusion in these tests.
In untabulated analyses, we repeat the tests of H2 after controlling for additional partitioning variables (i.e., size, book-to-market ratio, return volatility, negative return years, earnings-loss years, and extreme earnings) by including, one at a time, each one’s main effect and accrual interactions. All results are robust to these additional partitioning variables.
All results reported for year-ahead stock returns are robust to the additional partitioning variables noted above.
Barth et al. (2012) reverse the regression specification from De Franco et al. (2011) and take stock returns as the outcome of interest and earnings-based variables as predictors. Their full set of predictors includes (1) net income, (2) change in net income, (3) an indicator of loss (i.e., negative net income), (4) loss interacted with net income, and (5) loss interacted with change in net income. All income variables are scaled by initial market value. Barth et al. (2012) estimate their comparability regressions by using annual data from a different research setting. We adapt their regressions to follow the quarterly approach from De Franco et al. (2011). In other words, we follow the approach that we described in Sect. 3.1 while using this alternative regression structure.
Ross et al. (2019) measure accounting closeness based on the covariation of accounting outputs between firms. They measure the accounting closeness of income (cash flow) between firm i and firm j as the average of the time-series correlation of firm i’s and j’s income (cash flow) and the R2 from a time series regression of firm i’s income (cash flow) on firm j’s income (cash flow). A higher correlation between accounting outputs is interpreted as higher accounting closeness. We adapt their design to be consistent with our variable construction based on De Franco et al. (2011). See Ross et al. 20190for additional discussion of how they construct their variables.
Tests based on the Ross et al. (2019) measure use a shorter time time-period due to the requirement for cash flow information from the Statement of Cash Flows.
Untabulated results based on total accruals also confirm those of our primary tests.
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Neel, M., Safdar, I. Financial statement relevance, representational faithfulness, and comparability. Rev Quant Finan Acc 62, 309–339 (2024). https://doi.org/10.1007/s11156-023-01205-9
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DOI: https://doi.org/10.1007/s11156-023-01205-9