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Multi-objective optimisation with financial applications

Multi-objective optimisation with financial applications

Bradshaw, Noel-Ann (2015) Multi-objective optimisation with financial applications. MPhil thesis, University of Greenwich.

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Portfolio Optimisation is a multi-objective problem which involves finding the allocation of shares in a portfolio that optimises the likely return for a level of risk which an investor is prepared to tolerate. There have been several multi-objective evolutionary algorithms that have been used to solve the portfolio optimisation problem in recent years.

This thesis recounts the development of a new multi-objective evolutionary algorithm, Adaptive Cell Resolution Evolutionary Algorithm (ACREA), which has been shown to perform well in portfolio optimisation. ACREA uses a novel grid-based dynamic reduction mechanism which allows the solution population to grow and reduce whilst maintaining diversity across the whole solution space. The algorithm is tested on data from the OR Library and uses the difference in area between the analytic solution and found solution to measure the new algorithm’s success.

Before this algorithm was developed other heuristics were written in order to understand the basics of coding evolutionary algorithms in Java. After covering background information on financial applications and optimisation techniques, this thesis goes on to describe a simulated annealing algorithm to find the parameters for GARCH (1,1) and an evolutionary algorithm to determine trading rules using various trading indicators. These algorithms are described briefly in order to give an account of the journey and development.

Item Type: Thesis (MPhil)
Uncontrolled Keywords: Portfolio management; portfolio optimisation; multi-objective evolutionary algorithms; financial optimisation;
Subjects: H Social Sciences > HG Finance
Q Science > QA Mathematics
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 11 Apr 2019 09:52
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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