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Accruals, Cash Flows and the Post‐Earnings‐Announcement Drift

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  • Lakshmanan Shivakumar

Abstract

Several prior studies have shown that cash flows have significantly greater impact on stock prices than accruals. We examine the implications of these findings for the post‐earnings‐announcement‐drift anomaly. We argue that, if investors under‐react to earnings news, then the larger price impact of cash flows causes the cash flow component of earnings news to predict future returns better than the accruals component. Consistent with this argument, we show that unexpected cash flows are more positively related to future returns, than are unexpected accruals. Also, unexpected cash flows are found to predict future returns above and beyond that predicted by earnings surprises. Finally, we show that a strategy that decomposes earnings news into its components significantly outperforms strategies based on earnings news alone. The results support under‐reaction explanations for the drift.

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  • Lakshmanan Shivakumar, 2006. "Accruals, Cash Flows and the Post‐Earnings‐Announcement Drift," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 33(1‐2), pages 1-25, January.
    • Handle: RePEc:bla:jbfnac:v:33:y:2006:i:1-2:p:1-25
    DOI: 10.1111/j.1468-5957.2006.01425.x
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    Cited by:

    1. Fink, Josef, 2021. "A review of the Post-Earnings-Announcement Drift," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    2. Lu, Hsueh-Tien, 2018. "Voluntary disclosure and the pricing of earnings components," Journal of Behavioral and Experimental Finance, Elsevier, vol. 20(C), pages 64-73.
    3. Hyunjung Choi & Haeyoung Ryu, 2021. "Corporate Social Responsibility and Post Earnings Announcement Drift: Evidence from Korea," Sustainability, MDPI, vol. 13(11), pages 1-13, June.
    4. Laswad, Fawzi & Baskerville, Rachel F., 2007. "An analysis of the value of cash flow statements of New Zealand pension schemes," The British Accounting Review, Elsevier, vol. 39(4), pages 347-355.
    5. Zhao, Chencheng & Yuan, Xianghui & Long, Jun & Jin, Liwei & Guan, Bowen, 2023. "Financial indicators analysis using machine learning: Evidence from Chinese stock market," Finance Research Letters, Elsevier, vol. 58(PD).
    6. repec:grz:wpsses:2020-04 is not listed on IDEAS

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