American Economic Association

Journal of Business & Economic Statistics Session: Machine Learning and Financial Econometrics

Paper Session

Tuesday, Jan. 5, 2021 10:00 AM - 12:00 PM (EST)

Hosted By: Econometric Society

Chair: Christian Hansen, University of Chicago

Predicting Returns with Text Data

Bryan Kelly

Yale University

View Abstract

Abstract

We introduce a new text-mining methodology that extracts sentiment information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionary-based methods), our supervised learning framework constructs a sentiment score that is specifically adapted to the problem of return prediction. Our method proceeds in three steps: 1) isolating a list of sentiment terms via predictive screening, 2) assigning sentiment weights to these words via topic modeling, and 3) aggregating terms into an article-level sentiment score via penalized likelihood. We derive theoretical guarantees on the accuracy of estimates from our model with minimal assumptions. In our empirical analysis, we text-mine one of the most actively monitored streams of news articles in the financial system|the Dow Jones Newswires|and show that our supervised sentiment model excels at extracting return-predictive signals in this context.

Discussant(s)

Nitish Sinha

Federal Reserve Board

Steven Kou

Boston University

Markus Pelger

Stanford University

JEL Classifications

G1 - Asset Markets and Pricing