IDEAS home Printed from https://ideas.repec.org/a/eee/pacfin/v86y2024ics0927538x24001525.html
   My bibliography  Save this article

Social forecasting: Online social opinion and the cross-section of stock returns

Author

Listed:
  • Yuan, Kaibin
  • Liang, Yuheng
  • Zhu, Mengnan

Abstract

We construct a monthly measure for online social opinions on stocks in the Chinese market by extracting textual data from the internet. By implementing a “social forecasting” strategy, we find a significantly positive alpha of 0.544% per month based on the model of Fama-French's (2015) five-factor plus Carhart's (1997) momentum, and 0.636% per month based on Liu et al.'s (2018) Chinese four-factor model. This anomaly is driven by the mispricing of underexplored information since online social opinions are a strong predictor of firms' earning surprise and are strengthened when there are information frictions, such as low investor attention, high arbitrage costs, high proportions of sentiment-driven trades, and low percentages of institutional investors. Our results are robust to a series of tests and remain unchanged after taking the “factor zoo” into consideration.

Suggested Citation

  • Yuan, Kaibin & Liang, Yuheng & Zhu, Mengnan, 2024. "Social forecasting: Online social opinion and the cross-section of stock returns," Pacific-Basin Finance Journal, Elsevier, vol. 86(C).
  • Handle: RePEc:eee:pacfin:v:86:y:2024:i:c:s0927538x24001525
    DOI: 10.1016/j.pacfin.2024.102401
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927538X24001525
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.pacfin.2024.102401?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. Fama, Eugene F. & French, Kenneth R., 2017. "International tests of a five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 123(3), pages 441-463.
    2. Chen, Hailiang & Hwang, Byoung-Hyoun, 2022. "Listening in on investors’ thoughts and conversations," Journal of Financial Economics, Elsevier, vol. 145(2), pages 426-444.
    3. Baker, Malcolm & Stein, Jeremy C., 2004. "Market liquidity as a sentiment indicator," Journal of Financial Markets, Elsevier, vol. 7(3), pages 271-299, June.
    4. Campbell R. Harvey & Yan Liu, 2020. "False (and Missed) Discoveries in Financial Economics," Journal of Finance, American Finance Association, vol. 75(5), pages 2503-2553, October.
    5. Gu, Ming & Jiang, George J. & Xu, Bu, 2019. "The role of analysts: An examination of the idiosyncratic volatility anomaly in the Chinese stock market," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 237-254.
    6. Huang, Yuqin & Qiu, Huiyan & Wu, Zhiguo, 2016. "Local bias in investor attention: Evidence from China's Internet stock message boards," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 338-354.
    7. Lee, Charles M.C. & Sun, Stephen Teng & Wang, Rongfei & Zhang, Ran, 2019. "Technological links and predictable returns," Journal of Financial Economics, Elsevier, vol. 132(3), pages 76-96.
    8. 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.
    9. Richard W. Sias, 2004. "Institutional Herding," The Review of Financial Studies, Society for Financial Studies, vol. 17(1), pages 165-206.
    10. Thomas Renault, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205113, HAL.
    11. David Hirshleifer & Siew Hong Teoh & Jeff Jiewei Yu, 2011. "Short Arbitrage, Return Asymmetry, and the Accrual Anomaly," The Review of Financial Studies, Society for Financial Studies, vol. 24(7), pages 2429-2461.
    12. Angela K. Davis & Jeremy M. Piger & Lisa M. Sedor, 2012. "Beyond the Numbers: Measuring the Information Content of Earnings Press Release Language," Contemporary Accounting Research, John Wiley & Sons, vol. 29(3), pages 845-868, September.
    13. Bailey, Michael & Gupta, Abhinav & Hillenbrand, Sebastian & Kuchler, Theresa & Richmond, Robert & Stroebel, Johannes, 2021. "International trade and social connectedness," Journal of International Economics, Elsevier, vol. 129(C).
    14. Li, Feng, 2008. "Annual report readability, current earnings, and earnings persistence," Journal of Accounting and Economics, Elsevier, vol. 45(2-3), pages 221-247, August.
    15. 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.
    16. Leung, Henry & Ton, Thai, 2015. "The impact of internet stock message boards on cross-sectional returns of small-capitalization stocks," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 37-55.
    17. David Hirshleifer, 2020. "Presidential Address: Social Transmission Bias in Economics and Finance," Journal of Finance, American Finance Association, vol. 75(4), pages 1779-1831, August.
    18. Fama, Eugene F. & French, Kenneth R., 2015. "A five-factor asset pricing model," Journal of Financial Economics, Elsevier, vol. 116(1), pages 1-22.
    19. Kewei Hou & Chen Xue & Lu Zhang, 2020. "Replicating Anomalies," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2019-2133.
    20. Campbell R. Harvey & Yan Liu, 2020. "False (and Missed) Discoveries in Financial Economics," Papers 2006.04269, arXiv.org.
    21. Lee, Charles M. C. & Qu, Yuanyu & Shen, Tao, 2017. "Reverse Mergers, Shell Value, and Regulation Risk in Chinese Equity Markets," Research Papers repec:ecl:stabus:3604, Stanford University, Graduate School of Business.
    22. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    23. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    24. Fama, Eugene F & MacBeth, James D, 1973. "Risk, Return, and Equilibrium: Empirical Tests," Journal of Political Economy, University of Chicago Press, vol. 81(3), pages 607-636, May-June.
    25. Da, Zhi & Huang, Xing & Jin, Lawrence J., 2021. "Extrapolative beliefs in the cross-section: What can we learn from the crowds?," Journal of Financial Economics, Elsevier, vol. 140(1), pages 175-196.
    26. Hvide, Hans K. & Östberg, Per, 2015. "Social interaction at work," Journal of Financial Economics, Elsevier, vol. 117(3), pages 628-652.
    27. Lee, Charles M. C. & So, Eric C., 2015. "Alphanomics: The Informational Underpinnings of Market Efficiency," Foundations and Trends(R) in Accounting, now publishers, vol. 9(2-3), pages 59-258, December.
    28. Juhani T Linnainmaa & Michael R Roberts, 2018. "The History of the Cross-Section of Stock Returns," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2606-2649.
    29. Bushee, Brian & Cedergren, Matthew & Michels, Jeremy, 2020. "Does the media help or hurt retail investors during the IPO quiet period?," Journal of Accounting and Economics, Elsevier, vol. 69(1).
    30. Cai, Fang & Han, Song & Li, Dan & Li, Yi, 2019. "Institutional herding and its price impact: Evidence from the corporate bond market," Journal of Financial Economics, Elsevier, vol. 131(1), pages 139-167.
    31. John H. Cochrane, 2011. "Presidential Address: Discount Rates," Journal of Finance, American Finance Association, vol. 66(4), pages 1047-1108, August.
    32. Hirshleifer, David & Hsu, Po-Hsuan & Li, Dongmei, 2013. "Innovative efficiency and stock returns," Journal of Financial Economics, Elsevier, vol. 107(3), pages 632-654.
    33. Frömmel, Michael & Han, Xing & Li, Youwei & Vigne, Samuel A., 2022. "Low liquidity beta anomaly in China," Emerging Markets Review, Elsevier, vol. 50(C).
    34. Carhart, Mark M, 1997. "On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
    35. Siganos, Antonios & Vagenas-Nanos, Evangelos & Verwijmeren, Patrick, 2014. "Facebook's daily sentiment and international stock markets," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 730-743.
    36. Luo, Ronghua & Zhao, Senyang & Zhou, Jing, 2023. "Information network, public disclosure and asset prices," Pacific-Basin Finance Journal, Elsevier, vol. 77(C).
    37. Manela, Asaf & Moreira, Alan, 2017. "News implied volatility and disaster concerns," Journal of Financial Economics, Elsevier, vol. 123(1), pages 137-162.
    38. Liu, Tengdong & Zheng, Dazhi & Zheng, Suyan & Lu, Yang, 2023. "Herding in Chinese stock markets: Evidence from the dual-investor-group," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    39. Zhi Da & Xing Huang, 2020. "Harnessing the Wisdom of Crowds," Management Science, INFORMS, vol. 66(5), pages 1847-1867, May.
    40. Han, Chunmao & Zhang, Wei, 2024. "Trading volume, anomaly returns and noise trader risk in China," Pacific-Basin Finance Journal, Elsevier, vol. 84(C).
    41. Daniel Bradley & Jan Hanousek & Russell Jame & Zicheng Xiao, 2024. "Place Your Bets? The Value of Investment Research on Reddit’s Wallstreetbets," The Review of Financial Studies, Society for Financial Studies, vol. 37(5), pages 1409-1459.
    42. Zheng Tracy Ke & Bryan T. Kelly & Dacheng Xiu, 2019. "Predicting Returns With Text Data," NBER Working Papers 26186, National Bureau of Economic Research, Inc.
    43. 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.
    44. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    45. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    46. Sun, Feifan & Yin, Chen & Zhou, Sili & Zhu, Zijing, 2022. "IPO underpricing and mutual fund allocation: New evidence from registration system," International Review of Financial Analysis, Elsevier, vol. 84(C).
    47. Shiller, 021Robert J. & Pound, John, 1989. "Survey evidence on diffusion of interest and information among investors," Journal of Economic Behavior & Organization, Elsevier, vol. 12(1), pages 47-66, August.
    48. 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.
    Full references (including those not matched with items on IDEAS)

    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. Cookson, J. Anthony & Lu, Runjing & Mullins, William & Niessner, Marina, 2024. "The social signal," Journal of Financial Economics, Elsevier, vol. 158(C).
    2. Daniele Ballinari & Simon Behrendt, 2021. "How to gauge investor behavior? A comparison of online investor sentiment measures," Digital Finance, Springer, vol. 3(2), pages 169-204, June.
    3. Shi, Huai-Long & Zhou, Wei-Xing, 2022. "Factor volatility spillover and its implications on factor premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    4. Obaid, Khaled & Pukthuanthong, Kuntara, 2022. "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, vol. 144(1), pages 273-297.
    5. Xin Chen & Wei He & Libin Tao & Jianfeng Yu, 2023. "Attention and Underreaction-Related Anomalies," Management Science, INFORMS, vol. 69(1), pages 636-659, January.
    6. Hanauer, Matthias X. & Jansen, Maarten & Swinkels, Laurens & Zhou, Weili, 2024. "Factor models for Chinese A-shares," International Review of Financial Analysis, Elsevier, vol. 91(C).
    7. Cakici, Nusret & Fieberg, Christian & Metko, Daniel & Zaremba, Adam, 2023. "Machine learning goes global: Cross-sectional return predictability in international stock markets," Journal of Economic Dynamics and Control, Elsevier, vol. 155(C).
    8. Eierle, Brigitte & Klamer, Sebastian & Muck, Matthias, 2022. "Does it really pay off for investors to consider information from social media?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    9. Hollstein, Fabian, 2022. "The world of anomalies: Smaller than we think?," Journal of International Money and Finance, Elsevier, vol. 129(C).
    10. 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.
    11. Lee, Charles M.C. & Sun, Stephen Teng & Wang, Rongfei & Zhang, Ran, 2019. "Technological links and predictable returns," Journal of Financial Economics, Elsevier, vol. 132(3), pages 76-96.
    12. Huang, Tao & Zhang, Xueyong, 2022. "Industry-level media tone and the cross-section of stock returns," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 59-77.
    13. Roland Füss & Massimo Guidolin & Christian Koeppel, 2019. "Sentiment Risk Premia In The Cross-Section of Global Equity," Working Papers on Finance 1913, University of St. Gallen, School of Finance, revised May 2020.
    14. Szymon Lis, 2024. "Investor Sentiment in Asset Pricing Models: A Review of Empirical Evidence," Papers 2411.13180, arXiv.org.
    15. Roland Fuess & Massimo Guidolin & Christian Koeppel, 2019. "Sentiment Risk Premia in the Cross-Section of Global Equity and Currency Returns," BAFFI CAREFIN Working Papers 19116, BAFFI CAREFIN, Centre for Applied Research on International Markets Banking Finance and Regulation, Universita' Bocconi, Milano, Italy.
    16. Liu, Sha & Han, Jingguang, 2020. "Media tone and expected stock returns," International Review of Financial Analysis, Elsevier, vol. 70(C).
    17. Cakici, Nusret & Zaremba, Adam, 2023. "Recency bias and the cross-section of international stock returns," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 84(C).
    18. Keunbae Ahn, 2021. "Predictable Fluctuations in the Cross-Section and Time-Series of Asset Prices," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2021, January-A.
    19. Zhu, Hongbing & Yang, Lihua & Xu, Changxin, 2023. "Tracking investor gambling intensity," International Review of Financial Analysis, Elsevier, vol. 86(C).
    20. Agarwal, Shweta & Kumar, Shailendra & Goel, Utkarsh, 2019. "Stock market response to information diffusion through internet sources: A literature review," International Journal of Information Management, Elsevier, vol. 45(C), pages 118-131.

    More about this item

    Keywords

    Factor model; Anomalies; Online social opinions; Underexplored information;
    All these keywords.

    JEL classification:

    • 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
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making

    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:pacfin:v:86:y:2024:i:c:s0927538x24001525. 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/pacfin .

    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.