This paper investigates the relationship between seven mood-proxy variables and a global equity dataset using a variety of group tests. The mood-proxy variables are constructed from weather data (precipitation, temperature, wind, geomagnetic storms) and biorhythm data (seasonal affective disorder, daylight savings time changes, lunar phases). This study contributes a greater understanding of the relationship between mood and equity pricing through testing the strength of the relationship between groups of mood-proxy variables and both returns and variance. Using a large and globally diverse equity dataset, robust econometric testing approaches, and testing deseasonalised and regular weather variables, we conclude that seasonal affective disorder and low temperatures show the greatest relationship with equity pricing.
