Predicting Returns With Text Data / Zheng Tracy Ke, Bryan T. Kelly, Dacheng Xiu.

Format:

Book

Author/Creator:

Ke, Zheng Tracy.

Contributor:

National Bureau of Economic Research.

Kelly, Bryan T.

Xiu, Dacheng.

Series:

Working Paper Series (National Bureau of Economic Research) no. w26186.

NBER working paper series no. w26186

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2019.

Summary:

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.

Notes:

Print version record

August 2019.