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

59 Pages Posted: 20 Jan 2019 Last revised: 13 Mar 2023

See all articles by Alejandro Lopez-Lira

Alejandro Lopez-Lira

University of Florida - Department of Finance, Insurance and Real Estate

Date Written: January 3, 2023

Abstract

I use machine learning to extract the risk factors identified by firms in their annual disclosures, quantify firms' exposure to each risk, and construct mimicking portfolios that proxy for each risk. These portfolios are priced and contain information beyond what is accounted for by common multi-factor representations. A second-level factor model categorizes risks into systematic and idiosyncratic and helps construct pricing factors. These firm-identified risk factors perform at least as well as classic models when pricing a broad set of assets. These results suggest that firm-side risks, as in production-based models, are valuable in pricing the cross-section.

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 (January 3, 2023). Jacobs Levy Equity Management Center for Quantitative Financial Research Paper, Available at SSRN: https://ssrn.com/abstract=3313663 or http://dx.doi.org/10.2139/ssrn.3313663

Alejandro Lopez-Lira (Contact Author)

University of Florida - Department of Finance, Insurance and Real Estate ( email )

P.O. Box 117168
Gainesville, FL 32611
United States

HOME PAGE: http://alejandrolopezlira.site/

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