Reinforcement learning about asset variability and correlation in repeated portfolio decisions

Lay investors construct portfolios that are often not efficient and fail to take the correlation of assets into account. The present work examines whether providing people with a learning opportunity makes them sensitive to the correlation between assets. In two studies, where participants repeatedly allocated their endowment to three assets with feedback, participants changed their portfolio over time dependent on the asset correlation. To model learning about relevant characteristics of a portfolio, we developed reinforcement learning models that take learning about asset variability and correlation into account. We demonstrated via out-of-sample predictions that these models explain portfolio allocations better than basic reinforcement learning models, a static 1/N diversification strategy, and the mean–variance model using sample means, variances, and correlation. Hence, experiencing returns can help investors take asset correlations into account. The principles of reinforcement learning are a cognitively plausible and descriptively valid framework for understanding repeated portfolio allocations.