September 11 and Stock Return Expectations of Individual Investors
This study uses data that offers the unique opportunity to analyze how an unprecedented crisis such as the September 11 tragedy influences expected returns and volatility forecasts of individual investors. Via e-mail, we asked a randomly selected group of individual investors with accounts at a German online broker to answer an internet questionnaire at the beginning of August 2001. A second e-mail to the investors who had not yet answered, scheduled five weeks later, was postponed due to the terror attacks until September 20, which was exactly the day with the lowest share prices in Germany in the year 2001. Based on the answers to questions concerning stock market predictions, we find that return forecasts of the investors in our sample are significantly higher after September 11, suggesting a belief in mean reversion. Our results show that investors interpret the large drop in share prices during the ten day period after September 11 mainly as temporary rather than permanent. After the terror attacks, volatility forecasts are higher than before September 11. In two out of four cases, historical volatilities are overestimated. Therefore, investors are not generally overconfident in the way that they underestimate the variance of stock returns. Differences of opinion with regard to return forecasts are lower after the terror attacks whereas differences of opinion concerning volatility forecasts are mainly unaffected. Copyright Springer 2005
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Volume (Year): 9 (2005)
Issue (Month): 2 (06)
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References listed on IDEAS
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- S. T. M. Straetmans & W. F. C. Verschoor & C. C. P. Wolff, 2008. "Extreme US stock market fluctuations in the wake of 9|11," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(1), pages 17-42.
- Spanos,Aris, 1986. "Statistical Foundations of Econometric Modelling," Cambridge Books, Cambridge University Press, number 9780521269124, October.
- Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
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