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

58 Pages Posted: 20 Jan 2019 Last revised: 29 Jun 2022

See all articles by Alejandro Lopez-Lira

Alejandro Lopez-Lira

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

Date Written: June 9, 2022

Abstract

I exploit unsupervised machine learning and natural language processing techniques to elicit the risk factors that firms themselves identify in their annual reports. I quantify the firms' exposure to each identified risk and construct factor mimicking portfolios that proxy for each undiversifiable source of risk. The portfolios are priced in the cross-section and contain information above and beyond the commonly used multi-factor representations. A model that uses only firm identified risk factors (FIRFs) performs at least as well as traditional factor models, despite not using any information from past prices or returns.

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 9, 2022). 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

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