Empirical Economics

, Volume 41, Issue 3, pp 663–679 | Cite as

Sentiment dynamics and stock returns: the case of the German stock market

  • Thomas LuxEmail author


We use weekly survey data on short-term and medium-term sentiment of German investors in order to study the causal relationship between investors’ mood and subsequent stock price changes. In contrast to extant literature for other countries, a trivariate vector autoregression for short-run sentiment, medium-run sentiment, and stock index returns allows to reject exogeneity of returns. Depending on the chosen VAR specification, returns are found to either follow a feedback process caused by medium-run sentiment, or returns form a simultaneous systems together with the two sentiment measures. An out-of-sample forecasting experiment on the base of estimated subset VAR models shows significant exploitable linear structure. However, trading experiments do not yield convincing evidence of significant economic gains from the VAR forecasts, and it appears that predictability of returns from sentiment decreases during the recent market gyrations.


Investor sentiment Opinion dynamics Return predictability 

JEL Classification

G12 G14 C22 


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  1. Baker M, Wurgler J (2006) Investor sentiment and the cross-section of stock returns. J Financ 61: 1645–1680CrossRefGoogle Scholar
  2. Baker M, Wurgler J, Yuan Y (2009) Global, local and contagious investor sentiment. Working paper, Harvard Business SchoolGoogle Scholar
  3. Black F (1986) Noise. J Financ 41: 529–543CrossRefGoogle Scholar
  4. Brown G, Cliff M (2004) Investors sentiment and the near-term stock market. J Empir Financ 11: 1–27CrossRefGoogle Scholar
  5. Brown G, Cliff M (2005) Investor sentiment and asset valuation. J Bus 78: 405–440CrossRefGoogle Scholar
  6. Brüggemann R (2004) Model reduction methods for vector autoregressive processes. Springer, BerlinCrossRefGoogle Scholar
  7. Clark T, West K (2007) Approximately normal tests for equal predictive accuracy in nested models. J Econom 138: 291–311CrossRefGoogle Scholar
  8. DeLong J, Shleifer A, Summers L, Waldman R (1990) Noise trader risk in financial markets. J Political Econ 98: 703–738CrossRefGoogle Scholar
  9. Hengelbrock J, Theissen E, Westheide C (2009) Market response to investor sentiment. Working paper, University BonnGoogle Scholar
  10. Kling G, Gao L (2008) Chinese institutional investors’ sentiment. Int Financ Mark Inst Money 18: 374–387CrossRefGoogle Scholar
  11. Lemmon M, Portniaguina E (2006) Consumer confidence and asset prices: some empirical evidence. Rev Financ Stud 19: 1499–1529CrossRefGoogle Scholar
  12. Liu T-R, Geslow M, Irwin S (1994) The performance of alternative VAR models in forecasting exchange rates. Int J Forecast 10: 419–433CrossRefGoogle Scholar
  13. Lütkepohl H (2005) New introduction to multiple time series. Springer, BerlinGoogle Scholar
  14. Penm J, Terrell R (1984) Multivariate subset autoregressive modelling with zero constraints for detecting ‘overall causality’. J Econom 24: 311–330CrossRefGoogle Scholar
  15. Schmeling M (2007) Institutional and individual sentiment: smart money and noise trader risk?.  Int J Forecast 23: 127–145CrossRefGoogle Scholar
  16. Schmeling M (2009) Investor sentiment and stock returns: some international evidence. J Empir Financ 16: 394–408CrossRefGoogle Scholar
  17. Shleifer A, Vishny R (1997) The limits of arbitrage. J Financ 52: 35–55CrossRefGoogle Scholar
  18. Verma R, Baklaci H, Soydemir G (2008) The impact of rational and irrational sentiments of individual and institutional investors on DIJA and S&P500 index returns. Appl Financ Econ 18: 1303–1317CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Department of EconomicsUniversity of KielKielGermany
  2. 2.Kiel Institute for the World EconomyKielGermany

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