Rational Learning for Risk-Averse Investors by Conditioning on Behavioral Choices

37 Pages Posted: 17 Jun 2015  

Michele Costola

Goethe University Frankfurt - Research Center SAFE

Massimiliano Caporin

University of Padua - Department of Statistical Sciences

Date Written: June 11, 2015

Abstract

We present a rational learner agent, which considers the information coming from a behavioral counterpart during the allocation process. The learner agent adopts a herding behaviour by conditioning her choice on the selection of the portfolio's constituents. The considered framework has therefore two types of agents with two different utility functions: the rational agent with a hyperbolic absolute risk aversion (HARA) utility function and the other one with a general behavioral utility function. We use the concept of performance measure related to utility functions 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, which makes use of a weighting factor in blending the two components. We support our methodological framework with an empirical analysis including all the assets present in the NASDAQ and NYSE stock exchange from September 1977 to December 2014.

The appendices for this paper are available at the following URL: http://ssrn.com/abstract=2687066

Keywords: learner agent, investment decision, behavioral agents, Bayesian updating

JEL Classification: G10, G140, G150, G170

Suggested Citation

Costola, Michele and Caporin, Massimiliano, Rational Learning for Risk-Averse Investors by Conditioning on Behavioral Choices (June 11, 2015). University Ca' Foscari of Venice, Dept. of Economics Research Paper Series No. 16/WP/2015. Available at SSRN: https://ssrn.com/abstract=2617632 or http://dx.doi.org/10.2139/ssrn.2617632

Michele Costola (Contact Author)

Goethe University Frankfurt - Research Center SAFE ( email )

(http://www.safe-frankfurt.de)
Theodor-W.-Adorno-Platz 3
Frankfurt am Main, 60323
Germany

Massimiliano Caporin

University of Padua - Department of Statistical Sciences ( email )

Via Battisti, 241
Padova, 35121
Italy

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