Aggregation Bias in Estimates of Conditional Conservatism: Theory and Evidence

Abstract

This paper documents a study about the influence of the aggregation effect on the estimates of models based on the original Basu model – specifically the Ball, Kothari, and Nikolaev model (Ball et al., 2013b). We provide an analytical study of the effect, showing that it can produce two biases: an omitted-variable bias and a truncated-sample bias. Using separate proxies for good and bad news for each company and year, we estimate the empirical sign and magnitude of those biases. Our results show that the estimates of conditional conservatism based on regressions of (unexpected) earnings on (unexpected) returns, as in Ball et al. (2013b), are contaminated by substantial aggregation bias. More specifically, the aggregation effect causes these models to underestimate good-news timeliness and overestimate bad-news timeliness, thereby overestimating differential timeliness. Moreover, when we use proxies that provide better control for the aggregation effect, the differential timeliness coefficient tends to 0, showing that the influence of conditional conservatism on the returns–earnings relationship is at best marginal.