Predicting corporate policies using downside risk: A machine learning approach

Doron Avramov, Minwen Li, Hao Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

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.

Original languageAmerican English
Pages (from-to)1-26
Number of pages26
JournalJournal of Empirical Finance
Volume63
DOIs
StatePublished - Sep 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 Elsevier B.V.

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