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Published online by Cambridge University Press:  17 December 2015

Chandler Lutz*
Copenhagen Business School
Address correspondence to: Chandler Lutz, Copenhagen Business School, Porcelaenshaven 16A, 2000 Frederiksberg, Copenhagen, Denmark; e-mail:


We use the returns on lottery-like stocks and a dynamic factor model to construct a novel index of investor sentiment. This new measure is highly correlated with other behavioral indicators, but more closely tracks speculative episodes. Our main new finding is that the effects of sentiment are asymmetric: During peak-to-trough periods of investor sentiment (sentiment contractions), high sentiment predicts low future returns for the cross section of speculative stocks and for the market overall, whereas the relationship between sentiment and future returns is positive but relatively weak during trough-to-peak episodes (sentiment expansions). Overall, these results match theories and anecdotal accounts of investor sentiment.

Copyright © Cambridge University Press 2015 

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Abreu, D. and Brunnermeier, M.K. (2003) Bubbles and crashes. Econometrica 71 (1), 173204.CrossRefGoogle Scholar
Baker, M. and Wurgler, J. (2006) Investor sentiment and the cross-section of stock returns. Journal of Finance 61 (4), 16451680.CrossRefGoogle Scholar
Baker, M. and Wurgler, J. (2007) Investor sentiment in the stock market. Journal of Economic Perspectives 21 (2), 129151.CrossRefGoogle Scholar
Balke, N.S., Ma, J., and Wohar, M.E. (2015) A Bayesian analysis of weak identification in stock price decomposiitions. Macroeconomic Dynamics 19 (4).CrossRefGoogle Scholar
Barnett, W.A., Chauvet, M., and Tierney, H.L. (2009) Measurement error in monetary aggregates: A Markov switching factor approach. Macroeconomic Dynamics 13 (S2), 381412.CrossRefGoogle Scholar
Brunnermeier, M.K. (2009) Deciphering the liquidity and credit crunch 2007–2008. Journal of Economic Perspectives 23 (1), 77100.CrossRefGoogle Scholar
Brunnermeier, M.K. and Nagel, S. (2004) Hedge funds and the technology bubble. Journal of Finance 59 (5), 20132040.CrossRefGoogle Scholar
Bry, G. and Boschan, C. (1971) Cyclical Analysis of Time Series: Selected Procedures and Computer Programs. New York: NBER.Google Scholar
Carter, C. and Kohn, R. (1994) On Gibbs sampling for state space models. Biometrika 81 (3), 541553.CrossRefGoogle Scholar
Chauvet, M. (1998) An econometric characterization of business cycle dynamics with factor structure and regime switching. International Economic Review 39 (4), 969996.CrossRefGoogle Scholar
Chauvet, M. and Hamilton, J.D. (2006) Dating business cycle turning points. Contributions to Business Analysis 276, 154.CrossRefGoogle Scholar
Chauvet, M. and Piger, J.M. (2003) Identifying business cycle turning points in real time (Digest Summary). Federal Reserve Bank of St. Louis Review 85 (2), 4761.Google Scholar
Chauvet, M. and Piger, J. (2008) A comparison of the real-time performance of business cycle dating methods. Journal of Business and Economic Statistics 26 (1), 4249.CrossRefGoogle Scholar
Chauvet, M. and Potter, S. (2000) Coincident and leading indicators of the stock market. Journal of Empirical Finance 7 (1), 87111.CrossRefGoogle Scholar
Eickmeier, S. and Hofmann, B. (2013) Monetary policy, housing booms, and financial (im)balances. Macroeconomic Dynamics 17 (4), 830860.CrossRefGoogle Scholar
Fuleky, P. and Bonham, C.S. (2015) Forecasting with mixed-frequency factor models in the presence of common trends. Macroeconomic Dynamics 19, 753775.CrossRefGoogle Scholar
Harvey, A.C., Koopman, S.J., and Shephard, N. (2004) State Space and Unobserved Component Models: Theory and Applications. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Kim, C.-J. and Nelson, C.R. (1998) Business cycle turning points, a new coincident index, and tests of duration dependence based on a dynamic factor model with regime switching. Review of Economics and Statistics 80 (2), 188201.CrossRefGoogle Scholar
Kishor, N.K. and Neanidis, K.C. (2015) What is driving financial dollarization in transition economies? A dynamic factor analysis. Macroeconomic Dynamics 19, 816835.CrossRefGoogle Scholar
Kumar, A. (2009). Who gambles in the stock market? Journal of Finance 64 (4), 18891933.CrossRefGoogle Scholar
Lemmon, M. and Portniaguina, E. (2006) Consumer confidence and asset prices: Some empirical evidence. Review of Financial Studies 19 (4), 1499.CrossRefGoogle Scholar
Lim, K.-P. and Brooks, R.D. (2010) Why do emerging stock markets experience more persistent price deviations from a random walk over time? A country-level analysis. Macroeconomic Dynamics 14 (S1), 341.Google Scholar
Loughran, T. and McDonald, B. (2011) When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. Journal of Finance 66 (1), 3565.CrossRefGoogle Scholar
Ludvigson, S.C. and Ng, S. (2009) Macro factors in bond risk premia. Review of Financial Studies 22 (12), 50275067.CrossRefGoogle Scholar
Pagan, A.R. and Sossounov, K.A. (2003) A simple framework for analysing bull and bear markets. Journal of Applied Econometrics 18 (1), 2346.CrossRefGoogle Scholar
Patterson, D.M. and Sharma, V. (2010) The incidence of informational cascades and the behavior of trade interarrival times during the stock market bubble. Macroeconomic Dynamics 14 (S1), 111136.CrossRefGoogle Scholar
Shiller, R.J. (2006) Irrational Exuberance. Princeton, NJ: Crown Business.Google Scholar
Stambaugh, R.F., Yu, J., and Yuan, Y. (2012) The short of it: Investor sentiment and anomalies. Journal of Financial Economics 104 (2), 288302.CrossRefGoogle Scholar
Stock, J.H. and Watson, M.W. (1991) A probability model of the coincident economic indicators (with James H. Stock). In Lahiri, K. and Moore, G. (eds.), Leading Economic Indicators: New Approaches and Forecasting Records. Cambridge University Press.Google Scholar
Temin, P. and Voth, H.-J. (2004) Riding the South Sea Bubble. American Economic Review 94 (5), 16541668.CrossRefGoogle Scholar
Tetlock, P.C. (2007). Giving content to investor sentiment: The role of media in the stock market. Journal of Finance 62 (3), 11391168.CrossRefGoogle Scholar
Whaley, R.E. (2000) The investor fear gauge. Journal of Portfolio Management 26 (3), 1217.CrossRefGoogle Scholar
Wurgler, J. and Zhuravskaya, E. (2002) Does arbitrage flatten demand curves for stocks? Journal of Business 75 (4), 583608.CrossRefGoogle Scholar