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Does Unusual News Forecast Market Stress?

Published online by Cambridge University Press:  25 April 2019

Paul Glasserman Open the ORCID record for Paul Glasserman [Opens in a new window]  and
Harry Mamaysky Open the ORCID record for Harry Mamaysky [Opens in a new window]
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Abstract

An increase in “unusual” news with negative sentiment predicts an increase in stock market volatility. Unusual positive news forecasts lower volatility. Our analysis is based on over 360,000 articles on 50 large financial companies, published during the period of 1996–2014. Unusualness interacted with sentiment forecasts company-specific and aggregate volatility several months ahead. Furthermore, unusual news is reflected more slowly in aggregate volatility than company-specific volatility. News measures from articles explicitly about the “market,” which are more easily accessible to investors, do not forecast volatility. The observed responses of volatility to news may be explained by attention constraints on investors.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 54 , Issue 5 , October 2019 , pp. 1937 - 1974
Copyright
Copyright © Michael G. Foster School of Business, University of Washington 2019 

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Footnotes

1

This paper was produced while Glasserman was a consultant to the OFR. We thank the anonymous referees and Jennifer Conrad (the editor) for very helpful comments. We acknowledge the excellent research assistance of Il Doo Lee. We thank Geert Bekaert, Kent Daniel, Tara Sinclair, Paul Tetlock, and seminar participants at the Summer 2015 Consortium for Systemic Risk Analytics conference, Columbia University, the Office of Financial Research, the High Frequency Finance and Analytics conference at the Stevens Institute, the IAQF/Thalesians seminar, the Imperial College London Quantitative Finance Seminar, the Princeton Quant Trading Conference, the Columbia–Bloomberg Machine Learning in Finance Workshop, BNY Mellon’s Machine Learning Day, the 2016 Philadelphia Fed Conference on Real-Time Data Analysis, the 2016 SIAM Financial Mathematics Conference, the 2017 Society for Quantitative Analysts Conference, and the 2018 Cornell Tech Symposium for valuable comments. We thank the Thomson Reuters Corp. for graciously providing the data that was used in this study. We use the Natural Language Toolkit in Python for all text processing applications in the paper. For empirical analysis we use the R programming language for statistical computing.

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