Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12131
Title: Prospect theory based portfolio optimisation: An empirical study and analysis using intelligent algorithms
Authors: Date, P
Grishina, N
Lucas, C
Keywords: Portfolio optimisation;Behavioural nance;Prospect theory;Index tracking;Risk modelling
Issue Date: 2016
Publisher: Taylor & Francis (Routledge)
Citation: Quantitative Finance, Forthcoming, (2016)
Abstract: The behaviourally based portfolio selection problem with investor's loss aversion and risk aversion biases in portfolio choice under uncertainty is studied. The main results of this work are developed heuristic approaches for the prospect theory model proposed by Kahneman and Tversky in 1979 as well as an empirical comparative analysis of this model and the index tracking model. The crucial assumption is that behavioural features of the prospect theory model provide better downside protection than traditional approaches to the portfolio selection problem. In this research the large scale computational results for the prospect theory model have been obtained for real fi nancial market data with up to 225 assets. Previously, as far as we aware, only small laboratory tests (2-3 arti ficial assets) have been presented in the literature. In order to investigate empirically the performance of the behaviourally based model, a di erential evolution algorithm and a genetic algorithm which are capable to deal with large universe of assets have been developed. The speci fic breeding and mutation as well as normalisation have been implemented in the algorithms. A tabulated comparative analysis of the algorithms' parameter choice is presented. The prospect theory model with the reference point being the index is compared to the index tracking model. A cardinality constraint has been implemented to the basic index tracking and the prospect theory models. The portfolio diversi cation bene fit has been found. The aggressive behaviour in terms of returns of the prospect theory model with the reference point being the index leads to better performance of this model in the bullish market. However, it performed worse in a bearish market compared to the index tracking model. The tabulated comparative analysis of the performance of two studied models is provided in this paper for in-sample and out-of-sample tests. The performance of the studied models have been tested out-of-sample in di fferent conditions using simulation of the distribution of a growing market and simulation of the t-distribution with fat tails which characterises the dynamics of a decreasing or crisis market.
URI: http://www.tandfonline.com/toc/rquf20/current
http://bura.brunel.ac.uk/handle/2438/12131
ISSN: 1469-7696
Appears in Collections:Dept of Mathematics Research Papers

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