IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v48y2016i51p4942-4960.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2016.1167830
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2016.1167830?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. repec:bla:jfinan:v:59:y:2004:i:2:p:711-753 is not listed on IDEAS
    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. repec:bla:jfinan:v:43:y:1988:i:2:p:467-91 is not listed on IDEAS
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sushant Chari & Purva Hegde Desai & Nilesh Borde & Babu George, 2023. "Aggregate News Sentiment and Stock Market Returns in India," JRFM, MDPI, vol. 16(8), pages 1-18, August.
    2. Fassas, Athanasios P. & Siriopoulos, Costas, 2021. "Implied volatility indices – A review," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 303-329.
    3. Yang, Shanxiang & Liu, Zhechen & Wang, Xinjie, 2020. "News sentiment, credit spreads, and information asymmetry," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    4. Cao, Zhen & Han, Liyan & Wei, Xinbei & Zhang, Qunzi, 2022. "Fear in commodity return prediction," Finance Research Letters, Elsevier, vol. 46(PB).
    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.
    8. Montes, Gabriel Caldas & Nicolay, Rodolfo & Pereira, Flavio, 2022. "Does fiscal sentiment matter for sovereign risk?," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 18-30.
    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.
    10. Szczygielski, Jan Jakub & Charteris, Ailie & Bwanya, Princess Rutendo & Brzeszczyński, Janusz, 2024. "Google search trends and stock markets: Sentiment, attention or uncertainty?," International Review of Financial Analysis, Elsevier, vol. 91(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Smales, Lee A., 2014. "News sentiment in the gold futures market," Journal of Banking & Finance, Elsevier, vol. 49(C), pages 275-286.
    2. Smales, Lee A., 2015. "Time-variation in the impact of news sentiment," International Review of Financial Analysis, Elsevier, vol. 37(C), pages 40-50.
    3. Khuu, Joyce & Durand, Robert B. & Smales, Lee A., 2016. "Melancholia and Japanese stock returns – 2003 to 2012," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 424-437.
    4. Smales, Lee A., 2015. "Asymmetric volatility response to news sentiment in gold futures," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 34(C), pages 161-172.
    5. Ferdinand Graf, 2011. "Mechanically Extracted Company Signals and their Impact on Stock and Credit Markets," Working Paper Series of the Department of Economics, University of Konstanz 2011-18, Department of Economics, University of Konstanz.
    6. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    7. Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2014. "Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics," Papers 1412.3948, arXiv.org, revised Dec 2015.
    8. David E. Allen & Michael McAleer & Abhay K. Singh, 2019. "Daily market news sentiment and stock prices," Applied Economics, Taylor & Francis Journals, vol. 51(30), pages 3212-3235, June.
    9. David E Allen & Michael McAleer & Abhay K Singh, 2017. "An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series," Applied Economics, Taylor & Francis Journals, vol. 49(7), pages 677-692, February.
    10. Fabrizio Lillo & Salvatore Miccich� & Michele Tumminello & Jyrki Piilo & Rosario N. Mantegna, 2015. "How news affects the trading behaviour of different categories of investors in a financial market," Quantitative Finance, Taylor & Francis Journals, vol. 15(2), pages 213-229, February.
    11. Wei, Yu-Chen & Lu, Yang-Cheng & Chen, Jen-Nan & Hsu, Yen-Ju, 2017. "Informativeness of the market news sentiment in the Taiwan stock market," The North American Journal of Economics and Finance, Elsevier, vol. 39(C), pages 158-181.
    12. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    13. 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.
    14. Liu, Jun & Wu, Kai & Zhou, Ming, 2023. "News tone, investor sentiment, and liquidity premium," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 167-181.
    15. Justina Deveikyte & Helyette Geman & Carlo Piccari & Alessandro Provetti, 2020. "A Sentiment Analysis Approach to the Prediction of Market Volatility," Papers 2012.05906, arXiv.org.
    16. Rui Fan & Oleksandr Talavera & Vu Tran, 2020. "Social media bots and stock markets," European Financial Management, European Financial Management Association, vol. 26(3), pages 753-777, June.
    17. David E. Allen & Michael McAleer & Abhay K. Singh, 2014. "Machine news and volatility: The Dow Jones Industrial Average and the TRNA sentiment series," Working Papers in Economics 14/04, University of Canterbury, Department of Economics and Finance.
    18. Katherine B. Ensor & Yu Han & Barbara Ostdiek & Stuart M. Turnbull, 2020. "Dynamic jump intensities and news arrival in oil futures markets," Journal of Asset Management, Palgrave Macmillan, vol. 21(4), pages 292-325, July.
    19. Stefan Feuerriegel & Helmut Prendinger, 2018. "News-based trading strategies," Papers 1807.06824, arXiv.org.
    20. Takayuki Mizuno & Takaaki Ohnishi & Tsutomu Watanabe, 2015. "Novel and topical business news and their impact on stock market activities," UTokyo Price Project Working Paper Series 055, University of Tokyo, Graduate School of Economics.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:applec:v:48:y:2016:i:51:p:4942-4960. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.