Predicting Corporate Policies Using Downside Risk: A Machine Learning Approach

65 Pages Posted: 15 Feb 2016 Last revised: 15 Jan 2021

See all articles by Minwen Li

Minwen Li

Tsinghua Universitiy - School of Economics and Management

Hao Wang

Tsinghua University

Doron Avramov

Interdisciplinary Center (IDC) Herzliyah

Date Written: October 18, 2020

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.

Keywords: risk shock, corporate policies, texual analysis, LASSO

JEL Classification: G3

Suggested Citation

Li, Minwen and Wang, Hao and Avramov, Doron, Predicting Corporate Policies Using Downside Risk: A Machine Learning Approach (October 18, 2020). Journal of Empirical Finance, Forthcoming, Available at SSRN: https://ssrn.com/abstract=2732564 or http://dx.doi.org/10.2139/ssrn.2732564

Minwen Li

Tsinghua Universitiy - School of Economics and Management ( email )

324 Weilun Building
nagement
Beijing, 100084
China
(86)10-62793685 (Phone)

Hao Wang

Tsinghua University ( email )

318 Weilun Building
Tsinghua University
Beijing, 100084
China
86 10 62797482 (Phone)
86 10 62794554 (Fax)

Doron Avramov (Contact Author)

Interdisciplinary Center (IDC) Herzliyah ( email )

P.O. Box 167
Herzliya, 46150
Israel

HOME PAGE: http://faculty.idc.ac.il/davramov/

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