1932

Abstract

Investor sentiment indicates how far an asset value deviates from its economic fundamentals. In this article, we review various measures of investor sentiment based on market, survey, and text and media data. There is ample evidence that sentiment can explain returns on stocks that are difficult to value and costly to arbitrage, such as unprofitable stocks, non-dividend-paying stocks, extreme growth stocks, and distressed stocks. However, much remains to be done. We discuss three issues for future research: aggregating measures over various sources and various time horizons, linking investor sentiment to technical analysis, and statistically modeling the evolution of investor sentiment.

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2018-11-01
2024-05-09
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Literature Cited

  1. Aliber R, Kindleberger C 2015. Manias, Panics, and Crashes: A History of Financial Crises New York: Palgrave Macmillan
  2. Ang A, Bekaert G 2007. Return predictability: Is it there?. Rev. Financ. Stud. 20:651–707
    [Google Scholar]
  3. Angeletos G, La'O J 2013. Sentiments. Econometrica 81:739–79
    [Google Scholar]
  4. Antoniou C, Doukas JA, Subrahmanyam A 2016. Investor sentiment, beta, and the cost of equity capital. Manag. Sci. 62:347–67
    [Google Scholar]
  5. Antweiler W, Frank M 2004. Is all that talk just noise? The information content of Internet stock message boards. J. Finance 50:1259–94
    [Google Scholar]
  6. Avramov D 2002. Stock return predictability and model uncertainty. J. Financ. Econ. 64:423–58
    [Google Scholar]
  7. Baker M, Wurgler J 2006. Investor sentiment and the cross-section of stock returns. J. Finance 61:1645–80
    [Google Scholar]
  8. Baker M, Wurgler J 2007. Investor sentiment in the stock market. J. Econ. Perspect. 21:129–52
    [Google Scholar]
  9. Baker M, Wurgler J 2012. Behavioral corporate finance: an updated survey. Handbook of the Economics of Finance 2B G Constantinides, M Harris, R Stulz357–424 Amsterdam: Elsevier
    [Google Scholar]
  10. Baker M, Wurgler J, Yuan Y 2012. Global, local, and contagious investor sentiment. J. Financ. Econ. 104:272–87
    [Google Scholar]
  11. Barone-Adesi G, Mancini L, Shefrin H 2017. Estimating sentiment, risk aversion, and time preference from behavioral pricing kernel theory Res. Pap. 12-21 Swiss Finance Inst. Geneva/Zurich:
  12. Benhabib J, Liu X, Wang P 2016. Sentiments, financial markets, and macroeconomic fluctuations. J. Financ. Econ. 120:420–43
    [Google Scholar]
  13. Black F 1986. Noise. J. Finance 41:529–43
    [Google Scholar]
  14. Blume L, Easley D, O'Hara M 1994. Market statistics and technical analysis: the role of volume. J. Finance 49:153–81
    [Google Scholar]
  15. Bodnaruk A, Loughran T, McDonald B 2015. Using 10-K text to gauge financial constraints. J. Financ. Quant. Anal. 50:623–46
    [Google Scholar]
  16. Brock W, Lakonishok J, LeBaron B 1992. Simple technical trading rules and the stochastic properties of stock returns. J. Finance 47:1731–64
    [Google Scholar]
  17. Brown D, Jennings R 1989. On technical analysis. Rev. Financ. Stud. 2:527–51
    [Google Scholar]
  18. Brown G, Cliff M 2005. Investor sentiment and asset valuation. J. Bus. 78:405–40
    [Google Scholar]
  19. Brunnermeier MK 2001. Asset Pricing Under Asymmetric Information: Bubbles, Crashes, Technical Analysis and Herding Oxford, UK: Oxford Univ. Press
  20. Brunnermeier MK, Nagel S 2004. Hedge funds and the technology bubble. J. Finance 59:2013–40
    [Google Scholar]
  21. Burghardt G, Duncan R, Liu L 2010. Two benchmarks for momentum trading Res. Rep. Newedge Prime Brok. Paris:
  22. Campbell JY, Thompson SB 2008. Predicting the equity premium out of sample: Can anything beat the historical average?. Rev. Financ. Stud. 21:1509–31
    [Google Scholar]
  23. Chu L, Du Q, Tu J 2017. Purging investor sentiment index from too much fundamental information Work. Pap. Singap. Manag. Univ. Singapore:
  24. Chu L, Li K, He T, Tu J 2017. Market sentiment and paradigm shifts in equity premium forecasting Work. Pap. Singap. Manag. Univ. Singapore:
  25. Cochrane JH 2008. The dog that did not bark: a defense of return predictability. Rev. Financ. Stud. 21:1533–75
    [Google Scholar]
  26. Cochrane JH 2011. Presidential address: Discount rates. J. Finance 66:1047–108
    [Google Scholar]
  27. Colacito R, Ghysels E, Meng J, Siwasarit W 2016. Skewness in expected macro fundamentals and the predictability of equity returns: evidence and theory. Rev. Financ. Stud. 20:2069–109
    [Google Scholar]
  28. Da Z, Engelberg J, Gao P 2015. The sum of all FEARS: investor sentiment and asset prices. Rev. Financ. Stud. 28:1–32
    [Google Scholar]
  29. De Long JB, Shleifer A, Summers LH, Waldmann RJ 1990. Noise trader risk in financial markets. J. Political Econ. 98:703–38
    [Google Scholar]
  30. Detzel A, Liu H, Strauss J, Zhou G, Zhu Y 2018. Bitcoin: learning and profitability via technical analysis Work. Pap. Olin Bus. Sch., Wash. Univ. St. Louis St. Louis, MO:
  31. Edmans A, Goldstein I, Jiang W 2015. Feedback effects, asymmetric trading, and the limits to arbitrage. Am. Econ. Rev. 105:3766–97
    [Google Scholar]
  32. Fama EF, French KR 2015. A five-factor asset pricing model. J. Financ. Econ. 116:1–22
    [Google Scholar]
  33. Fama EF, Schwert GW 1977. Asset returns and inflation. J. Financ. Econ. 5:115–46
    [Google Scholar]
  34. Fisher K, Statman M 2000. Investor sentiment and stock returns. Financ. Anal. J. 56:16–23
    [Google Scholar]
  35. French KR, Schwert GW, Stambaugh RF 1987. Expected stock returns and volatility. J. Financ. Econ. 19:3–29
    [Google Scholar]
  36. Gao L, Han Y, Li S, Zhou G 2018. Market intraday momentum. J. Financ. Econ. 129:394–414
    [Google Scholar]
  37. García D 2013. Sentiment during recessions. J. Finance 68:1267–300
    [Google Scholar]
  38. Gehrig T, Menkhoff L 2006. Extended evidence on the use of technical analysis in foreign exchange. Int. J. Finance Econ. 11:327–38
    [Google Scholar]
  39. Geweke J, Amisano G 2011. Optimal prediction pools. J. Econom. 164:130–41
    [Google Scholar]
  40. Giglio S, Kelly B 2017. Excess volatility: beyond discount rates. Q. J. Econ. 133:71–127
    [Google Scholar]
  41. Graham J, Harvey C 2001. The theory and practice of corporate finance: evidence from the field. J. Financ. Econ. 60:187–243
    [Google Scholar]
  42. Greenwood R, Hanson S 2012. Share issuance and factor timing. J. Finance 67:761–98
    [Google Scholar]
  43. Greenwood R, Hanson S, Jin L 2016. A model of credit market sentiment Work. Pap. 2016-02 Harvard Bus. Sch., Harvard Univ. Boston:
  44. Greenwood R, Shleifer A 2014. Expectations of returns and expected returns. Rev. Financ. Stud. 27:714–46
    [Google Scholar]
  45. Griffin J, Harris J, Shu T, Topaloglu S 2011. Who drove and burst the tech bubble?. J. Finance 66:1251–90
    [Google Scholar]
  46. Grundy B, Kim Y 2002. Stock market volatility in a heterogeneous information economy. J. Financ. Quant. Anal. 37:1–27
    [Google Scholar]
  47. Han Y, Yang K, Zhou G 2013. A new anomaly: the cross-sectional profitability of technical analysis. J. Financ. Quant. Anal. 48:1433–61
    [Google Scholar]
  48. Han Y, Zhou G, Zhu Y 2016. A trend factor: any economic gains from using information over investment horizons?. J. Financ. Econ. 122:352–75
    [Google Scholar]
  49. Hansen LP, Jagannathan R 1991. Implications of security market data for models of dynamic economies. J. Polit. Econ. 99:225–62
    [Google Scholar]
  50. Harvey CR, Liu Y, Zhu H 2016. …And the cross-section of expected returns. Rev. Financ. Stud. 29:5–68
    [Google Scholar]
  51. He A, Huang D, Zhou G 2018. Pricing error reversals: a diagnostic test of asset pricing models Work. Pap. Olin Bus. Sch., Wash. Univ. St. Louis St. Louis, MO:
  52. Henkel SJ, Martin JS, Nadari F 2011. Time-varying short-horizon predictability. J. Financ. Econ. 99:560–80
    [Google Scholar]
  53. Hirshleifer D 2001. Investor psychology and asset pricing. J. Finance 56:1533–97
    [Google Scholar]
  54. Hirshleifer D, Li J, Yu J 2015. Asset pricing in production economies with extrapolative expectations. J. Monet. Econ. 76:87–106
    [Google Scholar]
  55. Hou K, Xue C, Zhang L 2015. Digesting anomalies: an investment approach. Rev. Financ. Stud. 28:650–705
    [Google Scholar]
  56. Huang D, Jiang F, Tu J, Zhou G 2015. Investor sentiment aligned: a powerful predictor of stock returns. Rev. Financ. Stud. 28:791–837
    [Google Scholar]
  57. Huang D, Lehkonen H, Pukthuanthong K, Zhou G 2018. Investor sentiment across asset markets Work. Pap. Olin Bus. Sch., Wash. Univ. St. Louis St. Louis, MO:
  58. Huang D, Zhou G 2017. Upper bounds on return predictability. J. Financ. Quant. Anal. 52:401–25
    [Google Scholar]
  59. Huang H, Lee T 2010. To combine forecasts or to combine information?. Econom. Rev. 29:534–70
    [Google Scholar]
  60. Jiang F, Lee J, Martin X, Zhou G 2017. Manager sentiment and stock returns. J. Financ. Econ. In press
  61. Jiang L, Wu K, Zhou G 2018. Asymmetry in stock comovements: an entropy approach. J. Financ. Quant. Anal. 53:1479–507
    [Google Scholar]
  62. Jiang L, Wu K, Zhou G, Zhu Y 2017. Stock return asymmetry: beyond skewness Work. Pap. Olin Bus. Sch., Wash. Univ. St. Louis St. Louis, MO:
  63. Kan R, Zhou G 2006. A new variance bound on the stochastic discount factor. J. Bus. 79:941–61
    [Google Scholar]
  64. Kandel S, Stambaugh RF 1996. On the predictability of stock returns: an asset allocation perspective. J. Finance 51:385–424
    [Google Scholar]
  65. Kelly B, Pruitt S 2013. Market expectations in the cross-section of present values. J. Finance 68:1721–56
    [Google Scholar]
  66. Kelly B, Pruitt S 2015. The three-pass regression filter: a new approach to forecasting using many predictors. J. Econom. 186:294–316
    [Google Scholar]
  67. Keynes J 1936. The General Theory of Employment, Interest and Money London: Macmillan
  68. Lamont O, Stein J 2006. Investor sentiment and corporate finance: micro and macro. Am. Econ. Rev. 16:147–51
    [Google Scholar]
  69. Lee C, Shleifer A, Thaler R 1991. Investor sentiment and the closed-end fund puzzle. J. Finance 46:75–109
    [Google Scholar]
  70. Lefèvre E 1923. Reminiscences of a Stock Operator New York: George H. Doran
  71. Lewellen J 2004. Predicting returns with financial ratios. J. Financ. Econ. 74:209–35
    [Google Scholar]
  72. Lie E, Meng B, Qian Y, Zhou G 2017. Corporate activities and the market risk premium Work. Pap. Olin Bus. Sch., Wash. Univ. St. Louis St. Louis, MO:
  73. Lin H, Wu C, Zhou G 2017. Trend momentum in corporate bonds Work. Pap. Sch. Econ. Finance, Vic. Univ. Wellingt. Wellington, NZ:
  74. Lin H, Wu C, Zhou G 2018. Forecasting corporate bond returns: an iterated combination approach. Manag. Sci. 64:3971–470
    [Google Scholar]
  75. Lo AW, Hasanhodzic J 2009. The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis New York: Bloomberg Press
  76. Lo AW, Hasanhodzic J 2010. The Evolution of Technical Analysis: Financial Prediction from Babylonian Tablets to Bloomberg Terminals Hoboken, NJ: Wiley
  77. Lo AW, Mamaysky H, Wang J 2000. Foundations of technical analysis: computational algorithms, statistical inference, and empirical implementation. J. Finance 55:1705–65
    [Google Scholar]
  78. Loewenstein M, Willard G 2006. The limits of investor behavior. J. Finance 61:231–58
    [Google Scholar]
  79. Loughran T, McDonald B 2011. When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. J. Finance 66:35–65
    [Google Scholar]
  80. Loughran T, McDonald B 2016. Textual analysis in accounting and finance: a survey. J. Account. Res. 54:1187–230
    [Google Scholar]
  81. Manela A, Moreira A 2017. News implied volatility and disaster concerns. J. Financ. Econ. 123:137–62
    [Google Scholar]
  82. Murphy J 1986. Technical Analysis of Futures Markets New York: N.Y. Inst. Finance
  83. Narang RK 2013. Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading Hoboken, NJ: Wiley
  84. Neely CJ, Rapach DE, Tu J, Zhou G 2014. Forecasting the equity risk premium: the role of technical indicators. Manag. Sci. 60:1772–91
    [Google Scholar]
  85. Pástor L˘, Stambaugh RF 2000. Comparing asset pricing models: an investment perspective. J. Financ. Econ. 56:335–81
    [Google Scholar]
  86. Pástor L˘, Stambaugh RF, Taylor 2017. Do funds make more when they trade more?. J. Finance 72:1483–528
    [Google Scholar]
  87. Pettenuzzo D, Timmermann A, Valkanov R 2014. Forecasting stock returns under economic constraints. J. Financ. Econ. 114:517–53
    [Google Scholar]
  88. Phillips P, Shi S, Yu J 2015. Testing for multiple bubbles: historical episodes of exuberance and collapse in the S&P 500. Int. Econ. Rev. 56:1043–78
    [Google Scholar]
  89. Phillips P, Wu Y, Yu J 2011. Explosive behavior in the 1990s NASDAQ: When did exuberance escalate asset values?. Int. Econ. Rev. 52:201–26
    [Google Scholar]
  90. Rapach DE, Ringgenberg M, Zhou G 2016. Short interest and aggregate stock returns. J. Financ. Econ. 121:46–65
    [Google Scholar]
  91. Rapach DE, Strauss JK, Zhou G 2010. Out-of-sample equity premium prediction: combination forecast and links to the real economy. Rev. Financ. Stud. 23:821–62
    [Google Scholar]
  92. Rapach DE, Zhou G 2013. Forecasting stock returns. Handbook of Economic Forecasting 2A G Elliott, A Timmermann329–83 Amsterdam: Elsevier
    [Google Scholar]
  93. Ross SA 2005. Neoclassical Finance Princeton, NJ: Princeton Univ. Press
  94. Schwager JD 1989. Market Wizards: Interviews with the Top Traders New York: N.Y. Inst. Finance
  95. Shefrin H 2008. A Behavioral Approach to Asset Pricing New York: Elsevier 2nd ed.
  96. Shen J, Yu J, Zhao S 2017. Investor sentiment and economic forces. J. Monet. Econ. 86:1–21
    [Google Scholar]
  97. Shiller RJ 1981. Do stock prices move too much to be justified by subsequent changes in dividends?. Am. Econ. Rev. 71:421–36
    [Google Scholar]
  98. Shiller RJ 1984. Stock prices and social dynamics. Brookings Pap. Econ. Act. 2:457–510
    [Google Scholar]
  99. Shleifer A, Vishny RW 1997. The limits of arbitrage. J. Finance 52:35–55
    [Google Scholar]
  100. Sibley S, Wang Y, Xing Y, Zhang Y 2016. The information content of the sentiment index. J. Bank. Finance 62:164–79
    [Google Scholar]
  101. Spiegel M 2008. Forecasting the equity premium: where we stand today. Rev. Financ. Stud. 21:1453–54
    [Google Scholar]
  102. Stambaugh RF, Yu J, Yuan Y 2012. The short of it: investor sentiment and anomalies. J. Financ. Econ. 104:288–302
    [Google Scholar]
  103. Stock J, Watson M 2002. Macroeconomic forecasting using diffusion indexes. J. Bus. Econ. Stat. 20:147–62
    [Google Scholar]
  104. Stock J, Watson M 2010. Dynamic factor models. Oxford Handbook of Economic Forecasting M Clements, D Henry35–60 Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  105. Sun L, Najand M, Shen J 2016. Stock return predictability and investor sentiment: a high-frequency perspective. J. Bank. Finance 73:147–64
    [Google Scholar]
  106. Taylor MP, Allen H 1992. The use of technical analysis in the foreign exchange market. J. Int. Money Finance 11:304–14
    [Google Scholar]
  107. Tetlock PC 2007. Giving content to investor sentiment: the role of media in the stock market. J. Finance 62:1139–68
    [Google Scholar]
  108. Thaler R 1993. Advances in Behavioral Finance New York: Russell Sage Found.
  109. Timmermann A 2006. Forecast combinations. Handbook of Economic Forecasting 1 G Elliott, CWJ Granger, A Timmermann135–96 Amsterdam: Elsevier
    [Google Scholar]
  110. Timmermann A 2018. Forecasting methods in finance. Annu. Rev. Financ. Econ. 10:449–79
    [Google Scholar]
  111. Wang J 1993. A model of intertemporal asset prices under asymmetric information. Rev. Econ. Stud 60:249–82
    [Google Scholar]
  112. Welch I, Goyal A 2008. A comprehensive look at the empirical performance of equity premium prediction. Rev. Financ. Stud. 21:1455–508
    [Google Scholar]
  113. Wold H 1966. Estimation of principal components and related models by iterative least squares. Multivariate Analysis PR Krishnaiah391–420 New York: Academic Press
    [Google Scholar]
  114. Wold H 1975. Path models with latent variables: the NIPALS approach. Quantitative Sociology: International Perspectives on Mathematical and Statistical Modeling H Blalock, A Aganbegian, F Borodkin, R Boudon, V Cappecchi305–57 New York: Seminar Press
    [Google Scholar]
  115. Yu J 2013. A sentiment-based explanation of the forward premium puzzle. J. Monet. Econ. 60:474–91
    [Google Scholar]
  116. Yu J, Yuan Y 2011. Investor sentiment and the mean-variance relation. J. Financ. Econ. 100:367–81
    [Google Scholar]
  117. Zhou G 1999. Security factors as linear combinations of economic variables. J. Financ. Markets 2:403–32
    [Google Scholar]
  118. Zhou G 2010. How much stock return predictability can we expect from an asset pricing model?. Econ. Lett. 108:184–86
    [Google Scholar]
  119. Zhu Y, Zhou G 2009. Technical analysis: an asset allocation perspective on the use of moving averages. J. Financ. Econ. 92:519–44
    [Google Scholar]
  120. Zweig M 1973. An investor expectations stock price predictive model using closed-end fund premiums. J. Finance 28:67–87
    [Google Scholar]
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