Risk Factors That Matter: Textual Analysis of Risk Disclosures for the Cross-Section of Returns

65 Pages Posted: 20 Jan 2019 Last revised: 8 Aug 2019

See all articles by Alejandro Lopez-Lira

Alejandro Lopez-Lira

University of Pennsylvania - The Wharton School; University of Pennsylvania - Finance Department

Date Written: June 1, 2019

Abstract

Using unsupervised machine learning, I introduce interpretable and economically relevant risk factors that characterize the cross-section of returns better than the leading factor models, furthermore, I do not use any information from the past returns to select the risk factors. I exploit natural language processing techniques to identify from the firms’ risk disclosures the types of risks that firms face, quantify how much each firm is exposed to each type of risk, and employ the firms’ exposure to each type of risk to construct a 4-factor model. The risk factors roughly correspond to Technology and Innovation Risk, Demand Risk, Production Risk and International Risk.

Keywords: Cross-Section of Returns, Factor Models, Machine Learning, Big Data, LDA, Text Analysis, NLP

Suggested Citation

Lopez Lira, Alejandro, Risk Factors That Matter: Textual Analysis of Risk Disclosures for the Cross-Section of Returns (June 1, 2019). Available at SSRN: https://ssrn.com/abstract=3313663 or http://dx.doi.org/10.2139/ssrn.3313663

Alejandro Lopez Lira (Contact Author)

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

University of Pennsylvania - Finance Department ( email )

The Wharton School
3620 Locust Walk
Philadelphia, PA 19104
United States

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