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Time-varying relationship of news sentiment, implied volatility and stock returns

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  • Lee A. Smales

Abstract

I examine the relationship between aggregate news sentiment, S&P 500 index (SPX) returns, and changes in the implied volatility index (VIX). I find a significant negative contemporaneous relationship between changes in VIX and both news sentiment and stock returns. This relationship is asymmetric whereby changes in VIX are larger following negative news and/or stock market declines. Vector autoregression (VAR) analysis of the dynamics and cross-dependencies between variables reveals a strong positive relationship between previous and current period changes in implied volatility and stock returns, while current period and lagged news sentiment has a significant positive (negative) relationship with stock returns (changes in VIX). I develop a simple trading strategy whereby high (low) levels of implied volatility signal attractive opportunities to take short (long) positions in the underlying index, while extremely negative (positive) news sentiment signals opportunities to enter short (long) index positions. The investor fear gauge (VIX) appears to perform better than news sentiment measures in forecasting future returns.

Suggested Citation

  • Lee A. Smales, 2016. "Time-varying relationship of news sentiment, implied volatility and stock returns," Applied Economics, Taylor & Francis Journals, vol. 48(51), pages 4942-4960, November.
  • Handle: RePEc:taf:applec:v:48:y:2016:i:51:p:4942-4960
    DOI: 10.1080/00036846.2016.1167830
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    1. Nicolas P. B. Bollen & Robert E. Whaley, 2004. "Does Net Buying Pressure Affect the Shape of Implied Volatility Functions?," Journal of Finance, American Finance Association, vol. 59(2), pages 711-753, April.
    2. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    3. Woodruff, Catherine S & Senchack, A J, Jr, 1988. " Intradaily Price-Volume Adjustments of NYSE Stocks to Unexpected Earnings," Journal of Finance, American Finance Association, vol. 43(2), pages 467-491, June.
    4. Smales, Lee A., 2014. "Non-scheduled news arrival and high-frequency stock market dynamics," Research in International Business and Finance, Elsevier, vol. 32(C), pages 122-138.
    5. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2011. "When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 321-340, March.
    6. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
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    Cited by:

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    5. Tom Marty & Bruce Vanstone & Tobias Hahn, 2020. "News media analytics in finance: a survey," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(2), pages 1385-1434, June.
    6. Wright, Calvin & Swidler, Steve, 2023. "Abnormal trading volume, news and market efficiency: Evidence from the Jamaica Stock Exchange," Research in International Business and Finance, Elsevier, vol. 64(C).
    7. Dahmene, Meriam & Boughrara, Adel & Slim, Skander, 2021. "Nonlinearity in stock returns: Do risk aversion, investor sentiment and, monetary policy shocks matter?," International Review of Economics & Finance, Elsevier, vol. 71(C), pages 676-699.
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    9. Economou, Fotini & Panagopoulos, Yannis & Tsouma, Ekaterini, 2018. "Uncovering asymmetries in the relationship between fear and the stock market using a hidden co-integration approach," Research in International Business and Finance, Elsevier, vol. 44(C), pages 459-470.

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