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Sentiment Dynamics and Stock Returns: The Case of the German Stock Market

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  • Thomas Lux

Abstract

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 tri-variate 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 VAR models shows significant exploitable linear structure for the richer VAR(5) model. Out-of-sample trading experiments underscore the potential for excess profits from a VAR-based strategy compared to the buy-and-hold benchmark

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Bibliographic Info

Paper provided by Kiel Institute for the World Economy in its series Kiel Working Papers with number 1470.

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Length: 28 pages
Date of creation: Dec 2008
Date of revision:
Handle: RePEc:kie:kieliw:1470

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Keywords: investor sentiment; opinion dynamics; return predictability;

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  1. Neal, Robert & Wheatley, Simon M., 1998. "Do Measures of Investor Sentiment Predict Returns?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 33(04), pages 523-547, December.
  2. Todd E. Clark & Kenneth D. West, 2005. "Approximately normal tests for equal predictive accuracy in nested models," Research Working Paper RWP 05-05, Federal Reserve Bank of Kansas City.
  3. Verma, Rahul & Verma, Priti, 2007. "Noise trading and stock market volatility," Journal of Multinational Financial Management, Elsevier, vol. 17(3), pages 231-243, July.
  4. Rahul Verma & Hasan Baklaci & Gokce Soydemir, 2008. "The impact of rational and irrational sentiments of individual and institutional investors on DJIA and S&P500 index returns," Applied Financial Economics, Taylor & Francis Journals, vol. 18(16), pages 1303-1317.
  5. Schmeling, Maik, 2007. "Institutional and individual sentiment: Smart money and noise trader risk?," International Journal of Forecasting, Elsevier, vol. 23(1), pages 127-145.
  6. Kevin D. Hoover & Stephen J. Perez, . "Data Mining Reconsidered: Encompassing And The General-To-Specific Approach To Specification Search," Department of Economics 97-27, California Davis - Department of Economics.
  7. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-38, August.
  8. Kling, Gerhard & Gao, Lei, 2008. "Chinese institutional investors' sentiment," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(4), pages 374-387, October.
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Cited by:
  1. Elisabete F. Sim�es Vieira, 2012. "Investor sentiment and market reaction: evidence on 2010 FIFA World Cup," International Journal of Economics and Accounting, Inderscience Enterprises Ltd, vol. 3(1), pages 51-76.
  2. Thomas Lux, 2009. "Mass Psychology in Action: Identification of Social Interaction Effects in the German Stock Market," Kiel Working Papers 1514, Kiel Institute for the World Economy.

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