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

50 Pages Posted: 23 Jun 2010 Last revised: 24 Jan 2016

See all articles by Elise Payzan-LeNestour

Elise Payzan-LeNestour

University of New South Wales; Financial Research Network (FIRN)

Date Written: May 4, 2012

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

Suggested Citation

Payzan-LeNestour, Elise, Learning to Choose the Right Investment in an Unstable World: Experimental Evidence Based on the Bandit Problem (May 4, 2012). Swiss Finance Institute Research Paper No. 10-28. Available at SSRN: https://ssrn.com/abstract=1628657 or http://dx.doi.org/10.2139/ssrn.1628657

Elise Payzan-LeNestour (Contact Author)

University of New South Wales ( email )

Australian School of Business
Sydney, NSW 2052
Australia

HOME PAGE: http://www.elisepayzan.com/

Financial Research Network (FIRN)

C/- University of Queensland Business School
St Lucia, 4071 Brisbane
Queensland
Australia

HOME PAGE: http://www.firn.org.au

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