Every year, usually in the month May, the European financial press refers to a—presumably—old
and inherited market saying:“Sell in May and go away.” 1 According to this saying, the …

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 …

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-…

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–…

We investigate two alternative explanations why men may hold more stocks than women do.
Apart from the traditional explanation of a gender difference in risk aversion, gender …

Social media platforms hold vast amounts of biographical data about our lives. They repackage
our past content as ‘memories’ and deliver them back to us. But how does that change …

In a true out-of-sample test based on fresh data we find no evidence that several well-known
technical trading strategies predict stock markets over the period of 1987 to 2011. Our test …

We investigate volatility clustering using a modeling approach based on the temporal
aggregation results for generalized autoregressive conditional heteroscedasticity (GARCH) …

Over 300 years of UK stock returns reveal that well-known monthly seasonals are sample
specific. For instance, the January effect only emerges around 1830. Most months have had …

US stock market sectors and industries perform better during winter than summer from 1926
to 2006. In more than two‐thirds of sectors and industries, the difference in summer and …