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Accruals and the Prediction of Future Cash Flows
Open accessAbstract
Building on the Dechow et al. (1998) model of the accrual process, this study investigates the role of accruals in predicting future cash flows. The model shows that each accrual component reflects different information relating to future cash flows; aggregate earnings masks this information. As predicted, disaggregating accruals into major components—change in accounts receivable, change in accounts payable, change in inventory, depreciation, amortization, and other accruals—significantly enhances predictive ability. Each accrual component, including depreciation and amortization, is significant with the predicted sign in predicting future cash flows, incremental to current cash flow. The cash flow and accrual components of current earnings have substantially more predictive ability for future cash flows than several lags of aggregate earnings. The inferences are robust to alternative specifications, including controlling for operating cash cycle and industry membership.
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Cited by
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The economic consequences associated with integrated report quality: Capital market and real effects
Barth, M. E., Cahan, S. F., Chen, L., & Venter, E. R. (2017) · Accounting, Organizations and Society · doi:10.1016/j.aos.2017.08.005 · Cited by 602
Self-citationThe Predictive Value of Expenses Excluded from Pro Forma Earnings
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Iatridis, G. (2010) · International Review of Financial Analysis · doi:10.1016/j.irfa.2010.02.004 · Cited by 339
Metadata
- Authors
- Barth, M. E., Cram, D. P., & Nelson, K. K.
- Venue
- The Accounting Review
- Publisher
- American Accounting Association
- Published
- 2001-01-01
- Volume
- 76
- Issue
- 1
- Pages
- 27-58
- ISSN
- 0001-4826 (ISSN-L)
- 1558-7967
- OpenAlex
- W3122987060
- Cited by
- 921
- References
- 0
- FWCI
- 70.50
- Type
- journal article
- Language
- en
- Open access
- green
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- Barth, M. E., Cram, D. P., & Nelson, K. K. (2001). Accruals and the Prediction of Future Cash Flows. The Accounting Review. https://doi.org/10.2308/accr.2001.76.1.27
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
