Kamstra, Kramer and Levi (KKL) in their comment seem to miss the main point of our paper. Many things are correlated with the seasons so it is difficult to distinguish between them when we try to explain the well-known summer winter pattern in stock returns. Finding an isolated seasonal affective disorder (SAD) effect without proper control variables does not disprove our point but strengthens it. To sidestep all of the issues they raise and take our point to the extreme, we show using plain vanilla regressions that the seasonal stock market pattern they attribute to SAD can also be “explained” by variables like ice cream consumption or airline travel. The new variations of SAD variables (“onset” and “incidence”) KKL propose in their recent work for North America are even more problematic than the original SAD variables. We find that these new SAD proxies are not significant in countries where according to KKL they should be: Canada and the United States.

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.

We document the existence of a strong seasonal effect in stock returns based on

Changes in oil prices predict stock market returns worldwide. We find significant predictability in both developed and emerging markets. These results cannot be explained by time-varying risk premia as oil price changes also significantly predict negative excess returns. Investors seem to underreact to information in the price of oil. A rise in oil prices drastically lowers future stock returns. Consistent with the hypothesis of a delayed reaction by investors, the relation between monthly stock returns and lagged monthly oil price changes strengthens once we introduce lags of several trading days between monthly stock returns and lagged monthly oil price changes.

This study provides empirical support for theoretical models that allow for time-varying rare disaster risk. Using a database of 447 international political crises during the period 1918–2006, we create a crisis index that shows substantial variation over time. Changes in this crisis index, our proxy for changes in perceived disaster probability, have a large impact on both the mean and volatility of world stock market returns. Crisis risk is positively correlated with the earnings–price ratio and the dividend yield. Cross-sectional tests also show that crisis risk is priced: Industries that are more crisis risk sensitive yield higher returns.

We study the relative importance of social factors (including household, workplace, and neighborhood peer effects) and personal characteristics (including age, gender, tax rates, and funds under management) for asset allocation decisions. The most important factors (in order) are household peer effects, personal characteristics and workplace peer effects. Neighborhood peer effects and financial advice play a less important role. We use instrumental variables for both household and workplace peer effects and find results that are consistent with causal peer effects.