63 Pages Posted: 29 Jan 2015 Last revised: 25 Jun 2019
Date Written: May 30, 2019
The goal of this study is twofold. First, we assess whether the accrual-generating process is adequately described by a linear model with respect to a range of underlying determinants examined by prior literature. On this point, we document substantial departures from linearity across the distributions of accrual determinants, including measures of size, performance, and growth. Second, we employ a recently developed multivariate matching approach (entropy balancing) to adjust for determinants in place of relying on a linear model. Entropy balancing identifies weights for the control sample to equalize the distribution of determinants across treatment and control samples. In simulations drawing random samples from deciles where a linear model displays poor fit, we find that entropy balancing significantly improves accrual model specification by reducing coefficient bias relative to linear and propensity-score matched models. Consistent with entropy balancing retaining sufficient power, we find that entropy-balanced estimates are able to detect seeded accrual manipulations in these same deciles. Our empirical analysis of accruals around new equity issuances bears out these improvements.
Keywords: discretionary accruals; entropy balancing; propensity score matching; covariate balance; initial public offerings; seasoned equity offerings
JEL Classification: M04
Suggested Citation: Suggested Citation