Learning About Unstable, Publicly Unobservable Payoffs
Review of Financial Studies, Forthcoming
66 Pages Posted: 14 May 2012 Last revised: 22 Oct 2014
Date Written: June 10, 2014
Neoclassical finance assumes that investors are Bayesian. In many realistic situations, Bayesian learning is challenging. Here, we consider investment opportunities that change randomly, while payoffs are observable only when invested. In a stylized version of the task, we wondered whether performance would be affected if one were to follow reinforcement learning principles instead. The answer is a definite yes. When asked to perform our task, participants overwhelmingly learned in a Bayesian way. They stopped being Bayesians though when not nudged into paying attention to contingency shifts. This raises an issue for financial markets: who has the incentive to nudge investors?
Keywords: Instability, Payoff observability, Complexity, Learning, Multi-armed bandit problem, Neurofinance
JEL Classification: C91, D83, D87, G02, G11
Suggested Citation: Suggested Citation