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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.

References

Baker, S.; Bloom, N.; and Davis, S.. “Measuring Economic Policy Uncertainty.” Quarterly Journal of Economics, 131 (2016), 1593–1636.Google Scholar
Barber, B., and Odean, T.. “All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors.” Review of Financial Studies, 21 (2008), 785–818.Google Scholar
Bekaert, G., and Hoerova, M.. “The VIX, the Variance Premium and Stock Market Volatility.” Journal of Econometrics, 183 (2014), 181–192.Google Scholar
Bisias, D.; Flood, M. D.; Lo, A. W.; and Valavanis, S.. “A Survey of Systemic Risk Analytics.” Annual Review of Financial Economics, 4 (2012), 255–296.Google Scholar
Bollen, N., and Whaley, R.. “Does Net Buying Pressure Affect the Shape of Implied Volatility Functions?” Journal of Finance, 59 (2004), 711–753.Google Scholar
Bollerslev, T.; Tauchen, R.; and Zhou, H.. “Expected Stock Returns and Variance Risk Premia.” Review of Financial Studies, 22 (2009), 4463–4492.Google Scholar
Broadie, M.; Chernov, M.; and Johannes, M.. “Understanding Index Option Returns.” Review of Financial Studies, 22 (2009), 4493–4529.Google Scholar
Campbell, J., and Thompson, S.. “Predicting Excess Returns Out of Sample: Can Anything Beat the Historical Average?” Review of Financial Studies, 21 (2008), 1509–1531.Google Scholar
Corwin, S., and Coughenour, J.. “Limited Attention and the Allocation of Effort in Securities Trading.” Journal of Finance, 63 (2008), 3031–3067.Google Scholar
Da, Z.; Engelberg, J.; and Gao, P.. “The Sum of All FEARS Investor Sentiment and Asset Prices.” Review of Financial Studies, 28 (2014), 1–32.Google Scholar
Daniel, K.; Hirshleifer, D.; and Teoh, S. H.. “Investor Psychology in Capital Markets: Evidence and Policy Implications.” Journal of Monetary Economics, 49 (2002), 139–209.Google Scholar
Das, S.“Text and Context: Language Analytics in Finance.” Foundations and Trends in Finance, 8 (2014), 145–261.Google Scholar
Dellavigna, S., and Pollet, J. M.. “Investor Inattention and Friday Earnings Announcements.” Journal of Finance, 64 (2009), 709–749.Google Scholar
Ehrmann, M., and Jansen, D.-J.. “The Pitch Rather Than the Pit: Investor Inattention, Trading Activity, and FIFA World Cup Matches.” Journal of Money, Credit and Banking, 49 (2017), 807–821.Google Scholar
Fama, E., and French, K.. “Dividend Yields and Expected Stock Returns.” Journal of Financial Economics, 22 (1988), 3–25.Google Scholar
Fama, E., and French, K.. “A Five-Factor Asset Pricing Model.” Journal of Financial Economics, 116 (2015), 1–22.Google Scholar
Fang, L., and Peress, J.. “Media Coverage and the Cross-Section of Stock Returns.” Journal of Finance, 64 (2009), 2023–2052.Google Scholar
Frazzini, A.“The Disposition Effect and Underreaction to News.” Journal of Finance, 61 (2006), 2017–2046.Google Scholar
Garcia, D.“Sentiment During Recessions.” Journal of Finance, 68 (2013), 1267–1300.Google Scholar
Glasserman, P., and Mamaysky, H.. “Investor Information Choice with Macro and Micro Information.” Working Paper, Columbia University (2018).Google Scholar
Heston, S., and Sinha, N.. “News versus Sentiment: Predicting Stock Returns from News Stories.” Financial Analysts Journal, 73 (2017), 67–83.Google Scholar
Hirshleifer, D.; Hou, K.; Teoh, S. H.; and Zhang, Y.. “Do Investors Overvalue Firms with Bloated Balance Sheets?” Journal of Accounting and Economics, 38 (2004), 297–331.Google Scholar
Huberman, G., and Regev, T.. “Contagious Speculation and a Cure for Cancer: A Nonevent That Made Stock Prices Soar.” Journal of Finance, 56 (2001), 387–396.Google Scholar
Jegadeesh, N., and Wu, D.. “Word Power: A New Approach for Content Analysis.” Journal of Financial Economics, 110 (2013), 712–729.Google Scholar
Jiao, P.; Veiga, A.; and Walther, A.. “Social Media, News Media and the Stock Market.” Working Paper, University of Oxford (2016).Google Scholar
Jung, J., and Shiller, R. J.. “Samuelson’s Dictum and the Stock Market.” Economic Inquiry, 43 (2005), 221–228.Google Scholar
Jurafsky, D., and Martin, J. H.. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2nd ed. Upper Saddle River, NJ: Prentice Hall (2008).Google Scholar
Kacperczyk, M. T.; Van Nieuwerburgh, S.; and Veldkamp, L.. “A Rational Theory of Mutual Funds’ Attention Allocation.” Econometrica, 84 (2016), 571–626.Google Scholar
Kogan, S.; Levin, D.; Routledge, B.; Sagi, J.; and Smith, N.. “Predicting Risk from Financial Reports with Regression.” In Proceedings of the North American Association for Computational Linguistics Human Language Technologies Conference (May/June 2009), 272–280.Google Scholar
Kogan, S.; Routledge, B.; Sagi, J.; and Smith, N.. “Information Content of Public Firm Disclosures and the Sarbanes-Oxley Act.” Working Paper, University of Texas (2011).Google Scholar
Loughran, T., and McDonald, B.. “When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks.” Journal of Finance, 66 (2011), 35–65.Google Scholar
Loughran, T., and McDonald, B.. “Textual Analysis in Finance and Accounting: A Survey.” Journal of Accounting Research, 54 (2016), 1187–1230.Google Scholar
Manela, A., and Moreira, A.. “News Implied Volatility and Disaster Concerns.” Journal of Financial Economics, 123 (2017), 137–162.Google Scholar
Maćkowiak, B., and Wiederholt, M.. “Optimal Sticky Prices Under Rational Inattention.” American Economic Review, 99 (2009), 769–803.Google Scholar
Nickell, S.“Biases in Dynamic Models with Fixed Effects.” Econometrica, 49 (1981), 1417–1426.Google Scholar
Peng, L., and Xiong, W.. “Investor Attention, Overconfidence, and Category Learning.” Journal of Financial Economics, 80 (2006), 563–602.Google Scholar
Pfaff, B.“VAR, SVAR and SVEC Models: Implementation within R Package Vars.” Journal of Statistical Software, 27 (2008), 1–32.Google Scholar
Poon, S.-H., and Granger, C.. “Forecasting Volatility in Financial Markets: A Review.” Journal of Economic Literature, 41 (2003), 478–539.Google Scholar
Routledge, B.; Sacchetto, S.; and Smith, N.. “Predicting Merger Targets and Acquirers from Text.” Working Paper, Tepper School of Business, Carnegie Mellon University (2013).Google Scholar
Sicherman, N.; Lowenstein, G.; Seppi, D. J.; and Utkus, S.. “Financial Attention.” Review of Financial Studies, 29 (2016), 863–897.Google Scholar
Sims, C.“Implications of Rational Inattention.” Journal of Monetary Economics, 50 (2003), 665–690.Google Scholar
Sims, C.“Rational Inattention and Monetary Economics.” Handbook of Monetary Policy. Elsevier (2015).Google Scholar
Solomon, D. H.; Soltes, E.; and Sosyura, D.. “Winners in the Spotlight: Media Coverage of Fund Holdings as a Driver of Flows.” Journal of Financial Economics, 113 (2014), 53–72.Google Scholar
Tetlock, P.“Giving Content to Investor Sentiment: The Role of Media in the Stock Market.” Journal of Finance, 62 (2007), 1139–1168.Google Scholar
Tetlock, P.“All the News That’s Fit to Reprint: Do Investors React to Stale Information?” Review of Financial Studies, 24 (2011), 1481–1512.Google Scholar
Tetlock, P.; Saar-Tsechansky, M.; and Macskassy, S.. “More Than Words: Quantifying Language to Measure Firms’ Fundamentals.” Journal of Finance, 63 (2008), 1437–1467.Google Scholar
Welch, I., and Goyal, A.. “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction.” Review of Financial Studies, 21 (2008), 1455–1508.Google Scholar
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