Review of Behavioral Economics > Vol 4 > Issue 2

Portfolio Optimization With Investor Utility Preference of Higher-Order Moments: A Behavioral Approach

Stelios Bekiros, European University Institute, Italy, and IPAG Business School, France, , Nikolaos Loukeris, University of Macedonia, Greece, , Iordanis Eleftheriadis, University of Macedonia, Greece,
Suggested Citation
Stelios Bekiros, Nikolaos Loukeris and Iordanis Eleftheriadis (2017), "Portfolio Optimization With Investor Utility Preference of Higher-Order Moments: A Behavioral Approach", Review of Behavioral Economics: Vol. 4: No. 2, pp 83-106.

Publication Date: 13 Sep 2017
© 2017 S. Bekiros, N. Loukeris, and I. Eleftheriadis
Financial markets,  Behavioral decision making,  Information systems economics,  Financial econometrics,  Computational problems
JEL Codes: C32C58G10G17
Utility preferenceSupport Vector MachinesGenetic Evolution


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In this article:
1. Introduction 
2. Behavioral Modeling and Higher Moments 
3. Methodology 
4. Empirical Results 
5. Conclusions 


We incorporate advanced higher moments of individual or institutional investors in a new approach dealing with the portfolio selection problem, formulated under a multi-criteria optimization framework. The “integrated portfolio intelligence” model extracts hidden patterns out of company fundamental indices and filters out effects such as trader noise or fraud utilizing advanced big data machine learning modeling. One of the main advantages of this novel system aside from providing with computer-efficient algorithmic optimality and predictive out performance is that it detects and extracts hidden trader behavioral patterns and firm investment “styles” from the data sets of large-scale institutional portfolios, which ultimately leads to the aversion and protection of extensive market manipulation and speculation.