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Entropy-balanced accruals

Author

Listed:
  • Jeff L. McMullin

    (Indiana University)

  • Bryce Schonberger

    (University of Rochester)

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.

Suggested Citation

  • Jeff L. McMullin & Bryce Schonberger, 2020. "Entropy-balanced accruals," Review of Accounting Studies, Springer, vol. 25(1), pages 84-119, March.
    • Handle: RePEc:spr:reaccs:v:25:y:2020:i:1:d:10.1007_s11142-019-09525-9
    DOI: 10.1007/s11142-019-09525-9
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