Human vs. Machine: Disposition Effect among Algorithmic and Human Day Traders

Abstract

This paper studies whether and why algorithmic traders exhibit one of the most broadlydocumented behavioral puzzles – the disposition effect. We use trade data from the NASDAQ Copenhagen Stock Exchange merged with the weather data. We find that on average, the disposition effect for human traders is substantial and increases significantly on colder days, while for similarly-trading algorithms, it is insignificant and insensitive to the weather. This provides causal evidence of the link between human psychology and the disposition effect and suggests that algorithms can reduce psychology-related human errors. Considering the ongoing AI adoption, this may have broad implications.