IDEAS home Printed from https://ideas.repec.org/a/eee/ecofin/v58y2021ics1062940821001388.html
   My bibliography  Save this article

The influence and predictive powers of mixed-frequency individual stock sentiment on stock returns

Author

Listed:
  • Wang, Ruina
  • Li, Jinfang

Abstract

In this article, we construct mixed-frequency individual stock sentiment using MIDAS model. We first investigate the influence power of mixed-frequency individual stock sentiment on excess returns. The results indicate that the higher the frequency of individual stock sentiment is, the better it explains the variation of excess returns, that mixed-frequency individual stock sentiment, especially mixed high-frequency sentiment, exerts greater influence on excess returns than the same frequency one and that the mixed-frequency sentiment has a stronger explanatory power to the variation of excess returns than size factor, book-to-market factor, profitability factor and investment factor do. Then, we study the predictive content of mixed-frequency individual stock sentiment. The results show that the higher the frequency of individual stock sentiment is, the better the forecast performs. Moreover, by comparing the corresponding statistics in influence and predictive power models, we find that the influence power of mixed-frequency individual stock sentiment is more significant than its predictive power.

Suggested Citation

  • Wang, Ruina & Li, Jinfang, 2021. "The influence and predictive powers of mixed-frequency individual stock sentiment on stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
  • Handle: RePEc:eee:ecofin:v:58:y:2021:i:c:s1062940821001388
    DOI: 10.1016/j.najef.2021.101522
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1062940821001388
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.najef.2021.101522?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. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    2. Yang, Chunpeng & Gao, Bin, 2014. "The term structure of sentiment effect in stock index futures market," The North American Journal of Economics and Finance, Elsevier, vol. 30(C), pages 171-182.
    3. Frazzini, Andrea & Lamont, Owen A., 2008. "Dumb money: Mutual fund flows and the cross-section of stock returns," Journal of Financial Economics, Elsevier, vol. 88(2), pages 299-322, May.
    4. Baker, Malcolm & Stein, Jeremy C., 2004. "Market liquidity as a sentiment indicator," Journal of Financial Markets, Elsevier, vol. 7(3), pages 271-299, June.
    5. 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.
    6. Michael P. Clements & Ana Beatriz Galvao, 2009. "Forecasting US output growth using leading indicators: an appraisal using MIDAS models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(7), pages 1187-1206.
    7. Yang, Chunpeng & Zhou, Liyun, 2016. "Individual stock crowded trades, individual stock investor sentiment and excess returns," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 39-53.
    8. Li, Jinfang, 2020. "The momentum and reversal effects of investor sentiment on stock prices," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    9. Changyun Wang, 2003. "Investor sentiment, market timing, and futures returns," Applied Financial Economics, Taylor & Francis Journals, vol. 13(12), pages 891-898.
    10. Lee, Charles M C & Shleifer, Andrei & Thaler, Richard H, 1991. "Investor Sentiment and the Closed-End Fund Puzzle," Journal of Finance, American Finance Association, vol. 46(1), pages 75-109, March.
    11. Bin Gao & Chunpeng Yang, 2018. "Investor Trading Behavior and Sentiment in Futures Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(3), pages 707-720, February.
    12. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    13. Gao, Bin & Yang, Chunpeng, 2017. "Forecasting stock index futures returns with mixed-frequency sentiment," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 69-83.
    14. Yang, Chunpeng & Zhou, Liyun, 2015. "Investor trading behavior, investor sentiment and asset prices," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 42-62.
    15. Ling Cen & Hai Lu & Liyan Yang, 2013. "Investor Sentiment, Disagreement, and the Breadth--Return Relationship," Management Science, INFORMS, vol. 59(5), pages 1076-1091, May.
    16. Jinfang Li, 2021. "The term structure effects of individual stock investor sentiment on excess returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1695-1705, April.
    17. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    18. Chunpeng Yang & Rengui Zhang, 2014. "Does mixed-frequency investor sentiment impact stock returns? Based on the empirical study of MIDAS regression model," Applied Economics, Taylor & Francis Journals, vol. 46(9), pages 966-972, March.
    19. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    20. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    21. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    22. Brown, Gregory W. & Cliff, Michael T., 2004. "Investor sentiment and the near-term stock market," Journal of Empirical Finance, Elsevier, vol. 11(1), pages 1-27, January.
    23. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    24. Alexander Kurov, 2008. "Investor Sentiment, Trading Behavior and Informational Efficiency in Index Futures Markets," The Financial Review, Eastern Finance Association, vol. 43(1), pages 107-127, February.
    25. Jinfang Li & Chunpeng Yang, 2017. "The cross-section and time-series effects of individual stock sentiment on stock prices," Applied Economics, Taylor & Francis Journals, vol. 49(47), pages 4806-4815, October.
    26. Jinfang Li, 2015. "The asymmetric effects of investor sentiment and monetary policy on stock prices," Applied Economics, Taylor & Francis Journals, vol. 47(24), pages 2514-2522, May.
    27. Moskowitz, Tobias J. & Ooi, Yao Hua & Pedersen, Lasse Heje, 2012. "Time series momentum," Journal of Financial Economics, Elsevier, vol. 104(2), pages 228-250.
    28. Alok Kumar & Charles M.C. Lee, 2006. "Retail Investor Sentiment and Return Comovements," Journal of Finance, American Finance Association, vol. 61(5), pages 2451-2486, October.
    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. Li, Jinfang, 2022. "The sentiment pricing dynamics with short-term and long-term learning," The North American Journal of Economics and Finance, Elsevier, vol. 63(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. Gao, Bin & Liu, Xihua, 2020. "Intraday sentiment and market returns," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 48-62.
    2. Gao, Bin & Yang, Chunpeng, 2017. "Forecasting stock index futures returns with mixed-frequency sentiment," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 69-83.
    3. Jinfang Li, 2021. "The term structure effects of individual stock investor sentiment on excess returns," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 1695-1705, April.
    4. Seok, Sang Ik & Cho, Hoon & Ryu, Doojin, 2019. "Firm-specific investor sentiment and daily stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    5. Seok, Sangik & Cho, Hoon & Ryu, Doojin, 2024. "Dual effects of investor sentiment and uncertainty in financial markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 300-315.
    6. Yang, Chunpeng & Zhou, Liyun, 2015. "Investor trading behavior, investor sentiment and asset prices," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 42-62.
    7. Yang, Chunpeng & Hu, Xiaoyi, 2021. "Individual stock sentiment beta and stock returns," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    8. Li, Jinfang, 2019. "Sentiment trading, informed trading and dynamic asset pricing," The North American Journal of Economics and Finance, Elsevier, vol. 47(C), pages 210-222.
    9. Li, Jinfang, 2020. "The momentum and reversal effects of investor sentiment on stock prices," The North American Journal of Economics and Finance, Elsevier, vol. 54(C).
    10. Seok, Sang Ik & Cho, Hoon & Ryu, Doojin, 2021. "Stock Market’s responses to intraday investor sentiment," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    11. Gao, Bin & Xie, Jun & Jia, Yun, 2019. "A futures pricing model with long-term and short-term traders," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 9-28.
    12. Chen, Haozhi & Zhang, Yue, 2023. "Research on the effect of firm-specific investor sentiment on the idiosyncratic volatility anomaly: Evidence from the Chinese market," Pacific-Basin Finance Journal, Elsevier, vol. 81(C).
    13. Li, Jinfang, 2022. "The sentiment pricing dynamics with short-term and long-term learning," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    14. Li, Jinfang, 2017. "Investor sentiment, heterogeneous agents and asset pricing model," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 504-512.
    15. Han, Xing & Li, Youwei, 2017. "Can investor sentiment be a momentum time-series predictor? Evidence from China," Journal of Empirical Finance, Elsevier, vol. 42(C), pages 212-239.
    16. Corredor, Pilar & Ferrer, Elena & Santamaria, Rafael, 2013. "Investor sentiment effect in stock markets: Stock characteristics or country-specific factors?," International Review of Economics & Finance, Elsevier, vol. 27(C), pages 572-591.
    17. Yang, Chunpeng & Zhou, Liyun, 2016. "Individual stock crowded trades, individual stock investor sentiment and excess returns," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 39-53.
    18. Ahmed Bouteska & Taimur Sharif & Mohammad Zoynul Abedin, 2024. "Does investor sentiment create value for asset pricing? An empirical investigation of the KOSPI‐listed firms," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3487-3509, July.
    19. Zhou, Liyun & Huang, Jialiang, 2020. "Contagion of future-level sentiment in Chinese Agricultural Futures Markets," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    20. 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.

    More about this item

    Keywords

    Individual stock investor sentiment; Mixed-frequency; Influence power; Forecasting;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    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:eee:ecofin:v:58:y:2021:i:c:s1062940821001388. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620163 .

    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.