RATIONAL LEARNING FOR RISK-AVERSE INVESTORS BY CONDITIONING ON BEHAVIORAL CHOICES

Abstract

The authors present a rational learner agent, which considers the information coming from a behavioral counterpart during the allocation process. The learner agent adopts a herding behavior by conditioning her choice on the selection of the portfolio’s constituents. They use the concept of performance measure to define agents’ preferences: the higher the measure, the higher the expected utility of a given asset. The rational learner agent updates her information in a Bayesian manner similarly to the Black–Litterman model. Finally, the authors provide an empirical application including all the assets present in the NASDAQ and NYSE stock exchange from September 1977 to December 2014.