IDEAS home Printed from https://ideas.repec.org/a/eee/ecmode/v88y2020icp47-58.html
   My bibliography  Save this article

A new investor sentiment indicator (ISI) based on artificial intelligence: A powerful return predictor in China

Author

Listed:
  • Ruan, Qingsong
  • Wang, Zilin
  • Zhou, Yaping
  • Lv, Dayong

Abstract

This paper utilizes deep learning approach widely documented in artificial intelligence, and proposes an investor-sentiment indicator (ISI) that is consistent with the purpose of forecasting stock market returns. We find that ISI is positively correlated with future stock market returns at a monthly frequency, but negatively associated with subsequent returns over a longer horizon. Moreover, ISI outperforms other well-recognized predictors both in and out of sample, and can predict cross-sectional stock returns sorted by industry. We also show a positive association between monthly ISI and dividend growth rate, which indicates that investors’ expectations about future cash flows may contribute to the return predictability of ISI.

Suggested Citation

  • Ruan, Qingsong & Wang, Zilin & Zhou, Yaping & Lv, Dayong, 2020. "A new investor sentiment indicator (ISI) based on artificial intelligence: A powerful return predictor in China," Economic Modelling, Elsevier, vol. 88(C), pages 47-58.
    • Handle: RePEc:eee:ecmode:v:88:y:2020:i:c:p:47-58
    DOI: 10.1016/j.econmod.2019.09.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0264999319304201
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econmod.2019.09.009?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. Antoniou, Constantinos & Doukas, John A. & Subrahmanyam, Avanidhar, 2013. "Cognitive Dissonance, Sentiment, and Momentum," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(1), pages 245-275, February.
    2. Ivo Welch & Amit Goyal, 2008. "A Comprehensive Look at The Empirical Performance of Equity Premium Prediction," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1455-1508, July.
    3. Campbell, John Y & Ammer, John, 1993. "What Moves the Stock and Bond Markets? A Variance Decomposition for Long-Term Asset Returns," Journal of Finance, American Finance Association, vol. 48(1), pages 3-37, March.
    4. Dergiades, Theologos, 2012. "Do investors’ sentiment dynamics affect stock returns? Evidence from the US economy," Economics Letters, Elsevier, vol. 116(3), pages 404-407.
    5. West, Kenneth D, 1996. "Asymptotic Inference about Predictive Ability," Econometrica, Econometric Society, vol. 64(5), pages 1067-1084, September.
    6. Dashan Huang & Fuwei Jiang & Jun Tu & Guofu Zhou, 2015. "Investor Sentiment Aligned: A Powerful Predictor of Stock Returns," Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 791-837.
    7. Gregory W. Brown & Michael T. Cliff, 2005. "Investor Sentiment and Asset Valuation," The Journal of Business, University of Chicago Press, vol. 78(2), pages 405-440, March.
    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. Denis Gromb & Dimitri Vayanos, 2010. "Limits of Arbitrage: The State of the Theory," NBER Working Papers 15821, National Bureau of Economic Research, Inc.
    10. Schmeling, Maik, 2009. "Investor sentiment and stock returns: Some international evidence," Journal of Empirical Finance, Elsevier, vol. 16(3), pages 394-408, June.
    11. Lior Menzly & Oguzhan Ozbas, 2010. "Market Segmentation and Cross‐predictability of Returns," Journal of Finance, American Finance Association, vol. 65(4), pages 1555-1580, August.
    12. John H. Cochrane, 2011. "Presidential Address: Discount Rates," Journal of Finance, American Finance Association, vol. 66(4), pages 1047-1108, August.
    13. John Y. Campbell & Christopher Polk & Tuomo Vuolteenaho, 2010. "Growth or Glamour? Fundamentals and Systematic Risk in Stock Returns," Review of Financial Studies, Society for Financial Studies, vol. 23(1), pages 305-344, January.
    14. Kandel, Shmuel & Stambaugh, Robert F, 1996. "On the Predictability of Stock Returns: An Asset-Allocation Perspective," Journal of Finance, American Finance Association, vol. 51(2), pages 385-424, June.
    15. John Y. Campbell, Robert J. Shiller, 1988. "The Dividend-Price Ratio and Expectations of Future Dividends and Discount Factors," Review of Financial Studies, Society for Financial Studies, vol. 1(3), pages 195-228.
    16. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    17. Abarbanell, Jeffery S., 1991. "Do analysts' earnings forecasts incorporate information in prior stock price changes?," Journal of Accounting and Economics, Elsevier, vol. 14(2), pages 147-165, June.
    18. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    19. 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.
    20. 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.
    21. Fazal Husain & Tariq Mahmood, 2001. "The Stock Market and the Economy in Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 40(2), pages 107-114.
    22. JULES H. Van BINSBERGEN & RALPH S. J. KOIJEN, 2010. "Predictive Regressions: A Present‐Value Approach," Journal of Finance, American Finance Association, vol. 65(4), pages 1439-1471, August.
    23. Ferreira, Miguel A. & Santa-Clara, Pedro, 2011. "Forecasting stock market returns: The sum of the parts is more than the whole," Journal of Financial Economics, Elsevier, vol. 100(3), pages 514-537, June.
    24. Baker, Malcolm & Wurgler, Jeffrey & Yuan, Yu, 2012. "Global, local, and contagious investor sentiment," Journal of Financial Economics, Elsevier, vol. 104(2), pages 272-287.
    25. Garrett, Ian & Priestley, Richard, 2012. "Dividend Growth, Cash Flow, and Discount Rate News," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 47(5), pages 1003-1028, October.
    26. JS Armstrong, 2004. "The Seer-Sucker Theory: The Value of Experts in Forecasting," General Economics and Teaching 0412009, University Library of Munich, Germany.
    27. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    28. Dongweí Su, 2003. "Risk, Return and Regulation in Chinese Stock Markets," World Scientific Book Chapters, in: Chinese Stock Markets A Research Handbook, chapter 3, pages 75-122, World Scientific Publishing Co. Pte. Ltd..
    29. Lettau, Martin & Ludvigson, Sydney C., 2005. "Expected returns and expected dividend growth," Journal of Financial Economics, Elsevier, vol. 76(3), pages 583-626, June.
    30. John H. Cochrane, 2008. "The Dog That Did Not Bark: A Defense of Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1533-1575, July.
    31. Mele, Antonio, 2007. "Asymmetric stock market volatility and the cyclical behavior of expected returns," Journal of Financial Economics, Elsevier, vol. 86(2), pages 446-478, November.
    32. Kelly, Bryan & Pruitt, Seth, 2015. "The three-pass regression filter: A new approach to forecasting using many predictors," Journal of Econometrics, Elsevier, vol. 186(2), pages 294-316.
    33. Shleifer, Andrei & Vishny, Robert W, 1997. "The Limits of Arbitrage," Journal of Finance, American Finance Association, vol. 52(1), pages 35-55, March.
    34. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    35. Kraus, Alan & Stoll, Hans R., 1972. "Parallel Trading by Institutional Investors," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(5), pages 2107-2138, December.
    36. Trueman, Brett, 1994. "Analyst Forecasts and Herding Behavior," Review of Financial Studies, Society for Financial Studies, vol. 7(1), pages 97-124.
    37. Shlomo Benartzi, 2001. "Excessive Extrapolation and the Allocation of 401(k) Accounts to Company Stock," Journal of Finance, American Finance Association, vol. 56(5), pages 1747-1764, October.
    38. Bryan Kelly & Seth Pruitt, 2013. "Market Expectations in the Cross-Section of Present Values," Journal of Finance, American Finance Association, vol. 68(5), pages 1721-1756, October.
    39. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    40. Lior Menzly & Tano Santos & Pietro Veronesi, 2004. "Understanding Predictability," Journal of Political Economy, University of Chicago Press, vol. 112(1), pages 1-47, February.
    41. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
    42. Lee, Wayne Y. & Jiang, Christine X. & Indro, Daniel C., 2002. "Stock market volatility, excess returns, and the role of investor sentiment," Journal of Banking & Finance, Elsevier, vol. 26(12), pages 2277-2299.
    43. Moshirian, Fariborz & Ng, David & Wu, Eliza, 2009. "The value of stock analysts' recommendations: Evidence from emerging markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 74-83, March.
    44. Mark Gertler & Simon Gilchrist, 1993. "The role of credit market imperfections in the monetary transmission mechanism: arguments and evidence," Finance and Economics Discussion Series 93-5, Board of Governors of the Federal Reserve System (U.S.).
    45. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
    46. Lauren Cohen & Andrea Frazzini, 2008. "Economic Links and Predictable Returns," Journal of Finance, American Finance Association, vol. 63(4), pages 1977-2011, August.
    47. 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.
    48. 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.
    49. Mei-Hua Liao & Chien-Chih Lin & Yinrou Wang, 2011. "The Effects of Removing Price Limits: Evidence from Taiwan IPO Stocks," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 47(0), pages 40-52, November.
    50. Christiano, Lawrence & Motto, Roberto & Rostagno, Massimo & Ilut, Cosmin, 2008. "Monetary policy and stock market boom-bust cycles," Working Paper Series 955, European Central Bank.
    51. Kim, Soon-Ho & Kim, Dongcheol, 2014. "Investor sentiment from internet message postings and the predictability of stock returns," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 708-729.
    52. Stavros Thomadakis & Dimitrios Gounopoulos & Christos Nounis & Andreas Merikas, 2016. "Collateral Regulation and IPO†Specific Liberalisation: the Case of Price Limits in the Athens Stock Exchange," European Financial Management, European Financial Management Association, vol. 22(2), pages 276-312, March.
    53. Long Chen & Xinlei Zhao, 2009. "Return Decomposition," Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5213-5249, December.
    54. Denis Gromb & Dimitri Vayanos, 2010. "Limits of Arbitrage," Annual Review of Financial Economics, Annual Reviews, vol. 2(1), pages 251-275, December.
    55. 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.
    56. Pontiff, Jeffrey, 1995. "Closed-end fund premia and returns Implications for financial market equilibrium," Journal of Financial Economics, Elsevier, vol. 37(3), pages 341-370, March.
    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. Dai, Zhifeng & Zhu, Junxin & Zhang, Xinhua, 2022. "Time-frequency connectedness and cross-quantile dependence between crude oil, Chinese commodity market, stock market and investor sentiment," Energy Economics, Elsevier, vol. 114(C).
    2. Song, Ziyu & Gong, Xiaomin & Zhang, Cheng & Yu, Changrui, 2023. "Investor sentiment based on scaled PCA method: A powerful predictor of realized volatility in the Chinese stock market," International Review of Economics & Finance, Elsevier, vol. 83(C), pages 528-545.
    3. Wen, Fenghua & Zou, Qian & Wang, Xiong, 2021. "The contrarian strategy of institutional investors in Chinese stock market," Finance Research Letters, Elsevier, vol. 41(C).
    4. Jiang, Zhe & Zhang, Lin & Zhang, Lingling & Wen, Bo, 2022. "Investor sentiment and machine learning: Predicting the price of China's crude oil futures market," Energy, Elsevier, vol. 247(C).
    5. Gaies, Brahim & Nakhli, Mohamed Sahbi & Ayadi, Rim & Sahut, Jean-Michel, 2022. "Exploring the causal links between investor sentiment and financial instability: A dynamic macro-financial analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 204(C), pages 290-303.
    6. Larissa Batrancea, 2021. "Empirical Evidence Regarding the Impact of Economic Growth and Inflation on Economic Sentiment and Household Consumption," JRFM, MDPI, vol. 14(7), pages 1-16, July.
    7. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
    8. Karam KIM & Doojin RYU, 2020. "Predictive ability of investor sentiment for the stock market," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 33-46, December.
    9. Yuan Li & Yu Zhang, 2021. "Investor Sentiment, Idiosyncratic Risk, and Stock Price Premium: Evidence From Chinese Cross-Listed Companies," SAGE Open, , vol. 11(2), pages 21582440211, June.
    10. Song, Ziyu & Yu, Changrui, 2022. "Investor sentiment indices based on k-step PLS algorithm: A group of powerful predictors of stock market returns," International Review of Financial Analysis, Elsevier, vol. 83(C).
    11. Li, Yan & Li, Weiping, 2021. "Firm-specific investor sentiment for the Chinese stock market," Economic Modelling, Elsevier, vol. 97(C), pages 231-246.
    12. Chen, Chuanglian & Wang, Shudan & Yao, Shujie & Lin, Yuting, 2023. "Does digital transformation increase the labor income share? From a perspective of resources reallocation," Economic Modelling, Elsevier, vol. 128(C).
    13. Rameeza Andleeb & Arshad Hassan, 2023. "Impact of Investor Sentiment on Contemporaneous and Future Equity Returns in Emerging Markets," SAGE Open, , vol. 13(3), pages 21582440231, August.
    14. Kim, Karam & Ryu, Doojin & Yang, Heejin, 2021. "Information uncertainty, investor sentiment, and analyst reports," International Review of Financial Analysis, Elsevier, vol. 77(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. Jiang, Fuwei & Lee, Joshua & Martin, Xiumin & Zhou, Guofu, 2019. "Manager sentiment and stock returns," Journal of Financial Economics, Elsevier, vol. 132(1), pages 126-149.
    2. Guofu Zhou, 2018. "Measuring Investor Sentiment," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 239-259, November.
    3. Reis, Pedro Manuel Nogueira & Pinho, Carlos, 2020. "A new European investor sentiment index (EURsent) and its return and volatility predictability," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    4. Chen, Jian & Tang, Guohao & Yao, Jiaquan & Zhou, Guofu, 2023. "Employee sentiment and stock returns," Journal of Economic Dynamics and Control, Elsevier, vol. 149(C).
    5. Rapach, David E. & Ringgenberg, Matthew C. & Zhou, Guofu, 2016. "Short interest and aggregate stock returns," Journal of Financial Economics, Elsevier, vol. 121(1), pages 46-65.
    6. Wang, Yudong & Pan, Zhiyuan & Liu, Li & Wu, Chongfeng, 2019. "Oil price increases and the predictability of equity premium," Journal of Banking & Finance, Elsevier, vol. 102(C), pages 43-58.
    7. Lin, Qi & Lin, Xi, 2021. "Cash conversion cycle and aggregate stock returns," Journal of Financial Markets, Elsevier, vol. 52(C).
    8. Ma, Feng & Wang, Ruoxin & Lu, Xinjie & Wahab, M.I.M., 2021. "A comprehensive look at stock return predictability by oil prices using economic constraint approaches," International Review of Financial Analysis, Elsevier, vol. 78(C).
    9. 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.
    10. Wenjie Ding & Khelifa Mazouz & Qingwei Wang, 2019. "Investor sentiment and the cross-section of stock returns: new theory and evidence," Review of Quantitative Finance and Accounting, Springer, vol. 53(2), pages 493-525, August.
    11. Tiwari, Aviral Kumar & Abakah, Emmanuel Joel Aikins & Bonsu, Christiana Osei & Karikari, Nana Kwasi & Hammoudeh, Shawkat, 2022. "The effects of public sentiments and feelings on stock market behavior: Evidence from Australia," Journal of Economic Behavior & Organization, Elsevier, vol. 193(C), pages 443-472.
    12. Papapostolou, Nikos C. & Pouliasis, Panos K. & Nomikos, Nikos K. & Kyriakou, Ioannis, 2016. "Shipping investor sentiment and international stock return predictability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 81-94.
    13. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    14. Song, Ziyu & Yu, Changrui, 2022. "Investor sentiment indices based on k-step PLS algorithm: A group of powerful predictors of stock market returns," International Review of Financial Analysis, Elsevier, vol. 83(C).
    15. Jian Chen & Jiaquan Yao & Qunzi Zhang & Xiaoneng Zhu, 2023. "Global Disaster Risk Matters," Management Science, INFORMS, vol. 69(1), pages 576-597, January.
    16. Dong, Dayong & Yue, Sishi & Cao, Jiawei, 2020. "Site visit information content and return predictability: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    17. Xi Dong & Yan Li & David E. Rapach & Guofu Zhou, 2022. "Anomalies and the Expected Market Return," Journal of Finance, American Finance Association, vol. 77(1), pages 639-681, February.
    18. Kim Kaivanto & Peng Zhang, 2019. "Investor Sentiment as a Predictor of Market Returns," Working Papers 268005798, Lancaster University Management School, Economics Department.
    19. Gonçalo Faria & Fabio Verona, 2016. "Forecasting the equity risk premium with frequency-decomposed predictors," Working Papers de Economia (Economics Working Papers) 06, Católica Porto Business School, Universidade Católica Portuguesa.
    20. Bätje, Fabian & Menkhoff, Lukas, 2016. "Predicting the equity premium via its components," VfS Annual Conference 2016 (Augsburg): Demographic Change 145789, Verein für Socialpolitik / German Economic Association.

    More about this item

    Keywords

    Investor sentiment; Artificial intelligence; Return predictability; Asset allocation; Cash flow;
    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
    • G19 - Financial Economics - - General Financial Markets - - - Other

    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:ecmode:v:88:y:2020:i:c:p:47-58. 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/30411 .

    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.