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

84 Pages Posted: 20 Jan 2019 Last revised: 24 Sep 2020

See all articles by Alejandro Lopez-Lira

Alejandro Lopez-Lira

BI Norwegian Business School; University of Pennsylvania - Finance Department; University of Pennsylvania - The Wharton School

Date Written: September 22, 2020

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 (September 22, 2020). 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)

BI Norwegian Business School ( email )

Nydalsveien 37
Oslo, 0442
Norway

University of Pennsylvania - Finance Department ( email )

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

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

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