http://repub.eur.nl/res/aut/3841/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/17525/ 2009-12-10T00:00:00Z Bubbles can persist because investors are better off riding bubbles. We define bubbles in a natural way as significant, prolonged deviations from fundamental values measured by the well-known asset pricing models. Our real-time bubble detection system shows that –using US industry returns– periods of both higher volatility and higher abnormal returns follow noisy positive bubble signals. However, for the typical investor the risk-return trade-off improves. Riding bubbles generates annual abnormal returns of three to nine percent. These conclusions are robust to different assumptions and our system allows for alternative multifactor models as proxies for fundamental value. http://repub.eur.nl/res/pub/13586/ 2008-04-01T00:00:00Z We show that results in the recent strand of the literature, which tries to explain stock returns by weather induced mood shifts of investors, might be data-driven inference. More specifically, we consider two recent studies [Kamstra, Mark J., Kramer, Lisa A., Levi, Maurice D., 2003a. Winter blues: A SAD stock market cycle. American Economic Review 93(1), 324–343; Cao, Melanie, Wei, Jason, 2005. Stock market returns: A note on temperature anomaly. Journal of Banking and Finance 29(6), 1559–1573] that claim that a seasonal anomaly in stock returns is caused by mood changes of investors due to lack of daylight and temperature variations, respectively. While we confirm earlier results in the literature that there is indeed a strong seasonal effect in stock returns in many countries: stock market returns tend to be significantly lower during summer and fall months than during winter and spring months as documented by Bouman and Jacobsen [Bouman, Sven, Jacobsen, Ben, 2002. The Halloween indicator, Sell in May and go away: Another puzzle. American Economic Review, 92(5), 1618–1635], there is little evidence in favor of a SAD or temperature explanation. In fact, we find that a simple winter/summer dummy best describes this seasonality. Our results suggest that without any further evidence the correlation between weather-related variables and stock returns might be spurious and the conclusion that weather affects stock returns through mood changes of investors is premature. http://repub.eur.nl/res/pub/1816/ 2004-12-03T00:00:00Z We show that results in the recent strand of the literature that tries to explain stock returns by weather induced mood shifts of investors might be data-driven inference. More specifically, we consider two recent studies (Kamstra, Kramer and Levi, 2003a and Cao and Wei, 2004) that claim that a seasonal anomaly in stock returns is caused by mood changes of investors due to lack of daylight and temperature variations, respectively. We confirm earlier results in the literature that there is indeed a strong seasonal effect in stock returns in many countries: stock market returns tend to be significantly lower during summer and fall months than during winter and spring months. However, we also show that at best, these two studies offer two of many possible explanations for the observed seasonal effect. As an illustration we link ice cream production and airline travel to the stock market seasonality using similar reasoning. Our results suggest that without any further evidence the correlation between weather variables and stock returns might be spurious and the conclusion that weather affects stock returns through mood changes of investors is premature. http://repub.eur.nl/res/pub/1017/ 2003-11-07T00:00:00Z We find that changes in oil prices strongly predict future stock market returns in many countries in the world. In our thirty year sample of monthly data for developed stock markets, we find statistically significant predictability in 12 out of the 18 countries and in a world market index. For our shorter time series of emerging markets we obtain similar results. We show that these results are economically significant and robust with respect to the sample period, different kind of oil prices we consider and well known effects like the January effect and the Halloween effect.