Research / BFI Working PaperMay 14, 2019

Predicting Returns with Text Data

Dacheng Xiu, Zheng Tracy Ke, Bryan Kelly

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.

More Research From These Scholars

BFI Working Paper Jul 24, 2023

Financial Machine Learning

Bryan T. Kelly, Dacheng Xiu
Topics:  Technology & Innovation, Financial Markets
BFI Working Paper Mar 31, 2023

Prediction When Factors are Weak

Stefano Giglio, Dacheng Xiu, Dake Zhang
Topics:  Uncategorized
BFI Working Paper Jul 7, 2021

Test Assets and Weak Factors

Stefano Giglio, Dacheng Xiu, Dake Zhang
Topics:  Financial Markets