TY - JOUR
T1 - Predicting corporate policies using downside risk
T2 - A machine learning approach
AU - Avramov, Doron
AU - Li, Minwen
AU - Wang, Hao
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/9
Y1 - 2021/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85106963110&partnerID=8YFLogxK
U2 - 10.1016/j.jempfin.2021.04.009
DO - 10.1016/j.jempfin.2021.04.009
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AN - SCOPUS:85106963110
SN - 0927-5398
VL - 63
SP - 1
EP - 26
JO - Journal of Empirical Finance
JF - Journal of Empirical Finance
ER -