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

89 Pages Posted: 20 Jan 2019 Last revised: 3 Dec 2019

See all articles by (Jose) Alejandro Lopez-Lira

(Jose) Alejandro Lopez-Lira

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

Date Written: November 2019

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, design an econometric test to classify them as either systematic or idiosyncratic, 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, (Jose) Alejandro, Risk Factors That Matter: Textual Analysis of Risk Disclosures for the Cross-Section of Returns (November 2019). Available at SSRN: https://ssrn.com/abstract=3313663 or http://dx.doi.org/10.2139/ssrn.3313663

(Jose) 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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