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The Plymouth Student Scientist

Document Type

Engineering, Computing and Mathematics Article

Abstract

Portfolio optimisation is an important problem in finance; it allows investors to manage their investments effectively. This paper considers finding the efficient frontier associated with the mean-variance portfolio optimisation (unconstrained) problem. We then extend the mean-variance model to include cardinality constraints (resulting in an NPHard problem) that limits the number of assets in a portfolio. We discuss different types of algorithms that one can use for finding the optimal portfolios, implementing a meta-heuristic genetic algorithm technique to solve the unconstrained and cardinality constrained problems. Finally, we improve our solutions by altering the crossover and mutation probabilities in the genetic algorithm method. For finding the efficient frontier associated with both problems, we examine a dataset involving 55 assets from the US stock exchange.

Publication Date

2021-12-24

Publication Title

The Plymouth Student Scientist

Volume

14

Issue

2

First Page

429

Last Page

464

ISSN

1754-2383

Deposit Date

December 2021

Embargo Period

2024-07-08

URI

http://hdl.handle.net/10026.1/18510

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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