Entropy-balanced accruals

Jeff L. McMullin () and Bryce Schonberger ()
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Jeff L. McMullin: Indiana University
Bryce Schonberger: University of Rochester

Review of Accounting Studies, 2020, vol. 25, issue 1, No 3, 84-119

Abstract: Abstract This study assesses whether the accrual-generating process is adequately described by a linear model with respect to a range of underlying determinants examined by prior literature. We document substantial departures from linearity across the distributions of accrual determinants, including measures of size, performance, and growth. To incorporate non-linear relations, 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 its estimates detect seeded accrual manipulations and explain variation in accruals around equity issuances.

Keywords: Abnormal accruals; Nonlinearity; Entropy balancing; Propensity-score matching; Covariate balance; Initial public offerings; Seasoned equity offerings; M4 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (114)

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DOI: 10.1007/s11142-019-09525-9

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