Contextualizing Profitability
Chicago Booth Research Paper No. 23-11
University of Chicago, Becker Friedman Institute for Economics Working Paper No. 2023-76
53 Pages Posted: 25 May 2023 Last revised: 13 Jan 2024
Date Written: June 01, 2024
Abstract
We study the role of context in asset pricing by focusing on the narratives surrounding profitability. Using a large language model, we incorporate narrative context into the measurement of profitability. Contextualized profitability outperforms conventional profitability measures both in statistical and economic terms. Its predictive power stems from the model's ability to learn transitory vs. persistent variation in profits. Furthermore, the factor based on contextualized profitability is superior in pricing portfolios of assets and eliminates alpha in small extreme growth portfolios - the biggest challenge facing the five-factor model (Fama and French, 2015). Our results imply that incorporating narrative context not only improves investment strategies but also enhances the asset pricing tests.
Keywords: Contextual information, asset pricing, machine learning, neural networks, large language models, BERT, Transformer, operating profitability, factor models
JEL Classification: C13, C45, C55, C58, G11, G12, M41
Suggested Citation: Suggested Citation