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Predicting corporate policies using downside risk: A machine learning approach

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  • Avramov, Doron
  • Li, Minwen
  • Wang, Hao

Abstract

This paper develops a text-based downside risk measure using corporate annual reports and assesses its ability to forecast future corporate policies. The forward-looking measure dynamically captures adverse firm conditions evolving from economic fundamentals. When the measure is below its sample average, leverage, investment, R&D, employment, and dividends consistently fall. When the measure rises, firms increase cash holdings. The proposed measure also delivers robust and persistent forecasts based on in-sample and out-of-sample LASSO regressions.

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  • Avramov, Doron & Li, Minwen & Wang, Hao, 2021. "Predicting corporate policies using downside risk: A machine learning approach," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 1-26.
    • Handle: RePEc:eee:empfin:v:63:y:2021:i:c:p:1-26
    DOI: 10.1016/j.jempfin.2021.04.009
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    2. Easterwood, John C. & Paye, Bradley S. & Xie, Yutong, 2021. "Firm uncertainty and corporate policies: The role of stock return skewness," Journal of Corporate Finance, Elsevier, vol. 69(C).
    3. Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021. "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series 205, University of Pavia, Department of Economics and Management.

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