Learning to Choose the Right Investment in an Unstable World: Experimental Evidence Based on the Bandit Problem

Abstract

When asset returns are unstable, investment performance directly depends on learning about their patterns optimally. Without optimal learning, strong investment performance is not possible. Yet, optimal learning is often considered too complex for investors to achieve. In order to test this experimentally, we simulate the return profiles of unstable assets with a multi-armed bandit in which the expected returns of the arms jump over the experiment. We find substantial evidence of optimal learning, despite the high difficulty of the task. This finding suggests that investors can in fact learn optimally in, at least, a subset of unstable financial contexts.

Keywords: Bandit problem, Learning, Experiments, Neurofinance, Instability, Uncertainty

JEL Classification: C91, D83, D87