Portfolio Management Using Prospect Theory: Comparing Genetic Algorithms and Particle Swarm Optimization

Seyedehzahra Nematollahi () and Giancarlo Manzi

Departmental Working Papers from Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano

Abstract: In this work, we compare the performance of two metaheuristic optimization algorithms, namely the Genetic Algorithms (GA) and the Particle Swarm Optimization (PSO), in finding an optimized investing portfolio. This comparison is based on two performance criteria: the consistency and quality of the solution and the speed of convergence of these two algorithms. These metaheuristic algorithms will be developed further to specify the weights of assets in an optimal portfolio, which is a portfolio with a maximum level of return (or a minimum level of risk) using a portfolio optimization model. We chose the prospect theory portfolio optimization as our background model. The prospect theory model is the main behavioral alternative to the expected utility theory and is still a relatively new subject in the financial literature. A mean‐variance portfolio optimization is also considered as a benchmark to our behavioral model. The performance of these two models has been evaluated in practice using several criteria such as the CPU time and the ratio between the portfolio mean returns

Keywords: Portfolio management; Prospect theory; Optimization; Genetic algorithms; Particle swarm optimization (search for similar items in EconPapers)
JEL-codes: C63 G11 G17 (search for similar items in EconPapers)
Date: 2018-03-11
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://wp.demm.unimi.it/files/wp/2018/DEMM-2018_03wp.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:mil:wpdepa:2018-03

Access Statistics for this paper

More papers in Departmental Working Papers from Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano Via Conservatorio 7, I-20122 Milan - Italy. Contact information at EDIRC.
Bibliographic data for series maintained by DEMM Working Papers ( this e-mail address is bad, please contact ).