A modified mean-variance-conditional value at risk model of multi-objective portfolio optimization with an application in finance

Abstract

This research focuses on the development of a portfolio optimization model based on the classic optimization method and a meta-heuristic algorithm. The main goal of a portfolio optimization model is to achieve maximum return with minimum investment risk by allocating capital based on a set of existing assets. Recently, mean-variance models have been improved to mean-variance-CVaR (MVC) model as a multi-objective portfolio optimization (MPO) problem which is difficult to be solved directly and optimally. In this work, a modified MVC model of portfolio optimization is constructed using the weighted sum method (WSM). In this method, each objective function of MVC model is given a weight. The optimization problem is then minimized as a weighted sum of the objective functions. The implementation of WSM enables the MVC model to be transformed from a multi-objective function to one with a single objective function. The modified MVC model is then solved using ant colony optimization (ACO) algorithm. This algorithm solves the MVC model by the number of ant colonies and the number of pheromone, a chemical creating trails for others to follow. The modified MVC model can be used in managing diverse investment portfolio, including stocks on the stock market and currency exchange. The applicability and effectiveness of the proposed method are demonstrated by solving a benchmark problem and a practical investment problem as examples. The data of practical examples are collected from the foreign currency exchange of Bank Negara Malaysia for the years 2012 and 2013. In conclusion, this thesis presented a hybrid optimization algorithm which utilizes a classical approach, WSM and a meta-heuristic approach, ACO to solve an MVC model of portfolio optimization.