IDEAS home Printed from https://ideas.repec.org/p/usg/econwp/201808.html
   My bibliography  Save this paper

Textual Sentiment, Option Characteristics, and Stock Return Predictability

Author

Listed:
  • Yi-Hsuan Chen, Cathy
  • Fengler, Matthias
  • Härdle, Wolfgang Karl
  • Liu, Yanchu

Abstract

We distill sentiment from a huge assortment of NASDAQ news articles by means of machine learning methods and examine its predictive power in single-stock option markets and equity markets. We provide evidence that single-stock options react to contemporaneous sentiment. Next, examining return predictability, we discover that while option variables indeed predict stock returns, sentiment variables add further informational content. In fact, both in a regression and a trading context, option variables orthogonalized to public and sentimental news are even more informative predictors of stock returns. Distinguishing further between overnight and trading-time news, we find the first to be more informative. From a statistical topic model, we uncover that this is attributable to the differing thematic coverage of the alternate archives. Finally, we show that sentiment disagreement commands a strong positive risk premium above and beyond market volatility and that lagged returns predict future returns in concentrated sentiment environments.

Suggested Citation

  • Yi-Hsuan Chen, Cathy & Fengler, Matthias & Härdle, Wolfgang Karl & Liu, Yanchu, 2018. "Textual Sentiment, Option Characteristics, and Stock Return Predictability," Economics Working Paper Series 1808, University of St. Gallen, School of Economics and Political Science.
    • Handle: RePEc:usg:econwp:2018:08
    as

    Download full text from publisher

    File URL: http://ux-tauri.unisg.ch/RePEc/usg/econwp/EWP-1808.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jun Pan & Allen M. Poteshman, 2006. "The Information in Option Volume for Future Stock Prices," The Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 871-908.
    2. Dennis, Patrick & Mayhew, Stewart, 2002. "Risk-Neutral Skewness: Evidence from Stock Options," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 37(3), pages 471-493, September.
    3. Buncic, Daniel & Gisler, Katja I.M., 2016. "Global equity market volatility spillovers: A broader role for the United States," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1317-1339.
    4. Henk Berkman & Cameron Truong, 2009. "Event Day 0? After‐Hours Earnings Announcements," Journal of Accounting Research, Wiley Blackwell, vol. 47(1), pages 71-103, March.
    5. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    6. Engelberg, Joseph E. & Reed, Adam V. & Ringgenberg, Matthew C., 2012. "How are shorts informed?," Journal of Financial Economics, Elsevier, vol. 105(2), pages 260-278.
    7. Sibley, Steven E. & Wang, Yanchu & Xing, Yuhang & Zhang, Xiaoyan, 2016. "The information content of the sentiment index," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 164-179.
    8. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    9. Jennifer Conrad & Robert F. Dittmar & Eric Ghysels, 2013. "Ex Ante Skewness and Expected Stock Returns," Journal of Finance, American Finance Association, vol. 68(1), pages 85-124, February.
    10. Carlin, Bruce I. & Longstaff, Francis A. & Matoba, Kyle, 2014. "Disagreement and asset prices," Journal of Financial Economics, Elsevier, vol. 114(2), pages 226-238.
    11. Moshirian, Fariborz & Nguyen, Huong Giang (Lily) & Pham, Peter Kien, 2012. "Overnight public information, order placement, and price discovery during the pre-opening period," Journal of Banking & Finance, Elsevier, vol. 36(10), pages 2837-2851.
    12. Stambaugh, Robert F. & Yu, Jianfeng & Yuan, Yu, 2012. "The short of it: Investor sentiment and anomalies," Journal of Financial Economics, Elsevier, vol. 104(2), pages 288-302.
    13. Chen, Cathy Yi-Hsuan & Chiang, Thomas C. & Härdle, Wolfgang Karl, 2018. "Downside risk and stock returns in the G7 countries: An empirical analysis of their long-run and short-run dynamics," Journal of Banking & Finance, Elsevier, vol. 93(C), pages 21-32.
    14. Xing, Yuhang & Zhang, Xiaoyan & Zhao, Rui, 2010. "What Does the Individual Option Volatility Smirk Tell Us About Future Equity Returns?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(3), pages 641-662, June.
    15. Michael J. Barclay, 2003. "Price Discovery and Trading After Hours," The Review of Financial Studies, Society for Financial Studies, vol. 16(4), pages 1041-1073.
    16. repec:bla:jfinan:v:59:y:2004:i:3:p:1235-1258 is not listed on IDEAS
    17. Yu, Jialin, 2011. "Disagreement and return predictability of stock portfolios," Journal of Financial Economics, Elsevier, vol. 99(1), pages 162-183, January.
    18. Chang, Bo Young & Christoffersen, Peter & Jacobs, Kris, 2013. "Market skewness risk and the cross section of stock returns," Journal of Financial Economics, Elsevier, vol. 107(1), pages 46-68.
    19. Alexander David, 2008. "Heterogeneous Beliefs, Speculation, and the Equity Premium," Journal of Finance, American Finance Association, vol. 63(1), pages 41-83, February.
    20. Pekka Malo & Ankur Sinha & Pekka Korhonen & Jyrki Wallenius & Pyry Takala, 2014. "Good debt or bad debt: Detecting semantic orientations in economic texts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 782-796, April.
    21. Paul C. Tetlock, 2010. "Does Public Financial News Resolve Asymmetric Information?," The Review of Financial Studies, Society for Financial Studies, vol. 23(9), pages 3520-3557.
    22. Harrison Hong & Jeremy C. Stein, 2007. "Disagreement and the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 109-128, Spring.
    23. Miller, Edward M, 1977. "Risk, Uncertainty, and Divergence of Opinion," Journal of Finance, American Finance Association, vol. 32(4), pages 1151-1168, September.
    24. H. Henry Cao & Joshua D. Coval & David Hirshleifer, 2002. "Sidelined Investors, Trading-Generated News, and Security Returns," The Review of Financial Studies, Society for Financial Studies, vol. 15(2), pages 615-648, March.
    25. Varian, Hal R, 1985. "Divergence of Opinion in Complete Markets: A Note," Journal of Finance, American Finance Association, vol. 40(1), pages 309-317, March.
    26. 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.
    27. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    28. Bollerslev, Tim & Osterrieder, Daniela & Sizova, Natalia & Tauchen, George, 2013. "Risk and return: Long-run relations, fractional cointegration, and return predictability," Journal of Financial Economics, Elsevier, vol. 108(2), pages 409-424.
    29. Snehal Banerjee, 2011. "Learning from Prices and the Dispersion in Beliefs," The Review of Financial Studies, Society for Financial Studies, vol. 24(9), pages 3025-3068.
    30. Wang, Xunxiao & Wu, Chongfeng & Xu, Weidong, 2015. "Volatility forecasting: The role of lunch-break returns, overnight returns, trading volume and leverage effects," International Journal of Forecasting, Elsevier, vol. 31(3), pages 609-619.
    31. Kim, Jun Sik & Ryu, Doojin & Seo, Sung Won, 2014. "Investor sentiment and return predictability of disagreement," Journal of Banking & Finance, Elsevier, vol. 42(C), pages 166-178.
    32. Diamond, Douglas W. & Verrecchia, Robert E., 1987. "Constraints on short-selling and asset price adjustment to private information," Journal of Financial Economics, Elsevier, vol. 18(2), pages 277-311, June.
    33. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    34. Hailiang Chen & Prabuddha De & Yu (Jeffrey) Hu & Byoung-Hyoun Hwang, 2014. "Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media," The Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1367-1403.
    35. Moskowitz, Tobias J. & Ooi, Yao Hua & Pedersen, Lasse Heje, 2012. "Time series momentum," Journal of Financial Economics, Elsevier, vol. 104(2), pages 228-250.
    36. Bing Han, 2008. "Investor Sentiment and Option Prices," The Review of Financial Studies, Society for Financial Studies, vol. 21(1), pages 387-414, January.
    37. Cheolbeom Park, 2005. "Stock Return Predictability and the Dispersion in Earnings Forecasts," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2351-2376, November.
    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. Packham, Natalie, 2018. "Optimal contracts under competition when uncertainty from adverse selection and moral hazard are present," IRTG 1792 Discussion Papers 2018-033, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    2. Qingliang Fan & Wei Zhong, 2018. "Nonparametric Additive Instrumental Variable Estimator: A Group Shrinkage Estimation Perspective," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(3), pages 388-399, July.
    3. Cai, Zongwu & Fang, Ying & Lin, Ming & Su, Jia, 2018. "Inferences for a Partially Varying Coefficient Model With Endogenous Regressors," IRTG 1792 Discussion Papers 2018-047, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    4. Joshua Zoen Git Hiew & Xin Huang & Hao Mou & Duan Li & Qi Wu & Yabo Xu, 2019. "BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability," Papers 1906.09024, arXiv.org, revised Jul 2022.
    5. Wang, Honglin & Yu, Fan & Zhou, Yinggang, 2018. "Property Investment and Rental Rate under Housing Price Uncertainty: A Real Options Approach," IRTG 1792 Discussion Papers 2018-051, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    6. Yan, Ji Gao, 2018. "Complete Convergence and Complete Moment Convergence for Maximal Weighted Sums of Extended Negatively Dependent Random Variables," IRTG 1792 Discussion Papers 2018-040, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    7. Zhong, Wei & Liu, Xi & Ma, Shuangge, 2018. "Variable selection and direction estimation for single-index models via DC-TGDR method," IRTG 1792 Discussion Papers 2018-050, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    8. Guo, Li & Tao, Yubo & Härdle, Wolfgang Karl, 2018. "Understanding Latent Group Structure of Cryptocurrencies Market: A Dynamic Network Perspective," IRTG 1792 Discussion Papers 2018-032, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    9. Kalkbrener, Michael & Packham, Natalie, 2018. "Correlation Under Stress In Normal Variance Mixture Models," IRTG 1792 Discussion Papers 2018-035, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    10. Packham, Natalie & Woebbeking, Fabian, 2018. "A factor-model approach for correlation scenarios and correlation stress-testing," IRTG 1792 Discussion Papers 2018-034, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    11. Chiu, Hsin-Yu & Chiang, Mi-Hsiu & Kuo, Wei-Yu, 2018. "Predicative Ability of Similarity-based Futures Trading Strategies," IRTG 1792 Discussion Papers 2018-045, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    12. Guo, Shaojun & Li, Dong & Li, Muyi, 2018. "Strict Stationarity Testing and GLAD Estimation of Double Autoregressive Models," IRTG 1792 Discussion Papers 2018-049, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    13. Xiaojia Bao & Qingliang Fan, 2020. "The impact of temperature on gaming productivity: evidence from online games," Empirical Economics, Springer, vol. 58(2), pages 835-867, February.
    14. Packham, Natalie & Kalkbrener, Michael & Overbeck, Ludger, 2018. "Default probabilities and default correlations under stress," IRTG 1792 Discussion Papers 2018-037, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    15. Kuczmaszewska, Anna & Yan, Ji Gao, 2018. "On complete convergence in Marcinkiewicz-Zygmund type SLLN for random variables," IRTG 1792 Discussion Papers 2018-041, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    16. Koziuk, Andzhey & Spokoiny, Vladimir, 2018. "Toolbox: Gaussian comparison on Eucledian balls," IRTG 1792 Discussion Papers 2018-028, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    17. Chen, Haiqiang & Li, Yingxing & Lin, Ming & Zhu, Yanli, 2018. "A Regime Shift Model with Nonparametric Switching Mechanism," IRTG 1792 Discussion Papers 2018-048, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    18. Yatracos, Yannis G., 2018. "Residual'S Influence Index (Rinfin), Bad Leverage And Unmasking In High Dimensional L2-Regression," IRTG 1792 Discussion Papers 2018-060, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    19. Nasekin, Sergey & Chen, Cathy Yi-Hsuan, 2018. "Deep learning-based cryptocurrency sentiment construction," IRTG 1792 Discussion Papers 2018-066, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    20. Zbonakova, Lenka & Li, Xinjue & Härdle, Wolfgang Karl, 2018. "Penalized Adaptive Forecasting with Large Information Sets and Structural Changes," IRTG 1792 Discussion Papers 2018-039, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

    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. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2022. "Media-expressed tone, option characteristics, and stock return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    2. Andreou, Panayiotis C. & Kagkadis, Anastasios & Philip, Dennis & Tuneshev, Ruslan, 2018. "Differences in options investors’ expectations and the cross-section of stock returns," Journal of Banking & Finance, Elsevier, vol. 94(C), pages 315-336.
    3. Akbas, Ferhat & Boehmer, Ekkehart & Jiang, Chao & Koch, Paul D., 2022. "Overnight returns, daytime reversals, and future stock returns," Journal of Financial Economics, Elsevier, vol. 145(3), pages 850-875.
    4. Junjun Ma & Xindan Li & Lei Lu & Weixing Wu & Xiong Xiong, 2022. "Individual investors' dispersion in beliefs and stock returns," Financial Management, Financial Management Association International, vol. 51(3), pages 929-953, September.
    5. Hillert, Alexander & Jacobs, Heiko & Müller, Sebastian, 2018. "Journalist disagreement," Journal of Financial Markets, Elsevier, vol. 41(C), pages 57-76.
    6. Al-Nasseri, Alya & Menla Ali, Faek, 2018. "What does investors' online divergence of opinion tell us about stock returns and trading volume?," Journal of Business Research, Elsevier, vol. 86(C), pages 166-178.
    7. Jia, Yun & Yang, Chunpeng, 2017. "Disagreement and the risk-return relation," Economic Modelling, Elsevier, vol. 64(C), pages 97-104.
    8. Seo, Sung Won & Kim, Jun Sik, 2015. "The information content of option-implied information for volatility forecasting with investor sentiment," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 106-120.
    9. 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.
    10. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, December.
    11. Zhou, Liyun & Yang, Chunpeng, 2019. "Stochastic investor sentiment, crowdedness and deviation of asset prices from fundamentals," Economic Modelling, Elsevier, vol. 79(C), pages 130-140.
    12. Eric. W. K. See-To & Yang Yang, 2017. "Market sentiment dispersion and its effects on stock return and volatility," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 283-296, August.
    13. Bennett, Donyetta & Mekelburg, Erik & Strauss, Jack & Williams, T.H., 2024. "Unlocking the black box of sentiment and cryptocurrency: What, which, why, when and how?," Global Finance Journal, Elsevier, vol. 60(C).
    14. Ingrid E. Fisher & Margaret R. Garnsey & Mark E. Hughes, 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 157-214, July.
    15. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    16. Chin‐Ho Chen, 2021. "Investor sentiment, misreaction, and the skewness‐return relationship," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(9), pages 1427-1455, September.
    17. Elyas Elyasiani & Luca Gambarelli & Silvia Muzzioli, 2016. "Fear or greed? What does a skewness index measure?," Department of Economics 0102, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    18. Prajwal Eachempati & Praveen Ranjan Srivastava, 2021. "Accounting for unadjusted news sentiment for asset pricing," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 13(3), pages 383-422, May.
    19. Bevilacqua, Mattia & Tunaru, Radu, 2021. "The SKEW index: Extracting what has been left," Journal of Financial Stability, Elsevier, vol. 53(C).
    20. Huang, Dashan & Li, Jiangyuan & Wang, Liyao, 2021. "Are disagreements agreeable? Evidence from information aggregation," Journal of Financial Economics, Elsevier, vol. 141(1), pages 83-101.

    More about this item

    Keywords

    investor disagreement; option markets; overnight information; stock return predictability; textual sentiment; topic model; trading-time information;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:usg:econwp:2018:08. 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/vwasgch.html .

    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.