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The Quality of Accruals and Earnings: The Role of Accrual Estimation Errors
Abstract
This paper suggests a new measure of one aspect of the quality of working capital accruals and earnings. One role of accruals is to shift or adjust the recognition of cash flows over time so that the adjusted numbers (earnings) better measure firm performance. However, accruals require assumptions and estimates of future cash flows. We argue that the quality of accruals and earnings is decreasing in the magnitude of estimation error in accruals. We derive an empirical measure of accrual quality as the residuals from firm-specific regressions of changes in working capital on past, present, and future operating cash flows. We document that observable firm characteristics can be used as instruments for accrual quality (e.g., volatility of accruals and volatility of earnings). Finally, we show that our measure of accrual quality is positively related to earnings persistence.
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Cited by
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Costs of Equity and Earnings Attributes
Francis, J., LaFond, R., Olsson, P. M., & Schipper, K. (2004) · The Accounting Review · doi:10.2308/accr.2004.79.4.967 · Cited by 2,377
Dhaliwal, D. S., Radhakrishnan, S., Tsang, A., & Yang, Y. G. (2012) · The Accounting Review · doi:10.2308/accr-10218 · Cited by 1,916
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Metadata
- Authors
- Dechow, P. M., & Dichev, I. D.
- Venue
- The Accounting Review
- Publisher
- American Accounting Association
- Published
- 2002-03-01
- Volume
- 77
- Issue
- s-1
- Pages
- 35-59
- ISSN
- 0001-4826 (ISSN-L)
- 1558-7967
- OpenAlex
- W3122547505
- Cited by
- 5,152
- References
- 0
- FWCI
- 86.06
- Type
- journal article
- Language
- en
- Open access
- closed
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- Dechow, P. M., & Dichev, I. D. (2002). The Quality of Accruals and Earnings: The Role of Accrual Estimation Errors. The Accounting Review. https://doi.org/10.2308/accr.2002.77.s-1.35
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
