Browsing by Author "Erwin, Kyle Harper"

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    Set-based particle swarm optimization for portfolio optimization
    (Stellenbosch : Stellenbosch University, 2021-12) Erwin, Kyle Harper; Engelbrecht, Andries; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences. Division Computer Science.
    ENGLISH ABSTRACT: Portfolio optimization is a complex problem, not only in the depth of the topics it covers but also in breadth. It is the process of determining which assets to include in a portfolio while simultaneously maximizing profit and minimizing risk. Portfolio optimization is rich with interesting research not only by researchers in finance, but also in computer science. The overlap of these fields has lead to an increase in the use of meta-heuristics to make intelligent investment decisions. This thesis conducts a thorough investigation into the current state of evolutionary and swarm intelligence algorithms for portfolio optimization. The investigation showed that these algorithms suffer from stability issues for larger portfolio optimization problems. A new approach using set-based particle swarm optimization (SBPSO) is proposed to reduce the dimensionality, and therefore complexity, of portfolio optimization problems. The results show that SBPSO is capable of obtaining good-quality solutions while being relatively fast. New set-based diversity measures are developed in order to better understand the exploration and exploitation behaviour of SBPSO, and set-based algorithms in general. It is shown that SBPSO fails to converge to a single solution and uses an inadequate process to determine the contribution of each asset to the portfolio. Based on these findings, improvements are made to the proposed SBPSO approach that yield significant gains in performance. The first multi-objective adaptation of SBPSO is also developed and is shown to scale to larger portfolio problems better than the multi-guided particle swarm optimization (MGPSO) algorithm, with lower levels of risk.