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Dissecting stock price momentum using financial statement analysis

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  • Anwer S. Ahmed
  • Irfan Safdar

Abstract

The literature on stock price momentum documents that past price performance predicts future price performance (over the next 3–12 months). We argue that past price performance can be driven either by fundamentals or by non‐fundamental reasons and financial statement analysis (FSA) can help distinguish between these drivers of past returns. We find that price momentum reverses where fundamentals are inconsistent with past price performance, allowing us to develop an investment strategy that outperforms a pure momentum strategy over 80 percent of the time. Overall, we document robust evidence on the usefulness of FSA for enhancing momentum strategies.

Suggested Citation

  • Anwer S. Ahmed & Irfan Safdar, 2018. "Dissecting stock price momentum using financial statement analysis," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 3-43, November.
  • Handle: RePEc:bla:acctfi:v:58:y:2018:i:s1:p:3-43
    DOI: 10.1111/acfi.12358
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    Cited by:

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    2. Jian Wang & Yanhuang Huang & Hongrui Feng & Xingjian Li & Shu Yan, 2023. "CEO incentive compensation and stock price momentum," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(S1), pages 975-1028, April.
    3. Andreas G. Koutoupis & Christos G. Kampouris & Athanasia V. Sakellaridou, 2022. "Can Financial Strength Indicators Form A Profitable Investment Strategy? The Case Of F-Score in Europe," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 21(3), pages 355-372, September.
    4. Irfan Safdar & Michael Neel & Babatunde Odusami, 2022. "Accounting information and left-tail risk," Review of Quantitative Finance and Accounting, Springer, vol. 58(4), pages 1709-1740, May.
    5. Zhengxin Joseph Ye & Bjorn W. Schuller, 2020. "Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning," Papers 2009.03094, arXiv.org.
    6. Memis, Halil I. & Wessels, Ulrich, 2024. "Dissecting value-growth strategies conditioned on expectation errors," The Quarterly Review of Economics and Finance, Elsevier, vol. 93(C), pages 155-163.
    7. Christian Walkshäusl, 2020. "Piotroski’s FSCORE: international evidence," Journal of Asset Management, Palgrave Macmillan, vol. 21(2), pages 106-118, March.

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