A Population-Based Incremental Learning Method for Constrained Portfolio Optimisation

This paper investigates a hybrid algorithm which utilizes exact and heuristic methods to optimise asset selection and capital allocation in portfolio optimisation. The proposed method is composed of a customised population based incremental learning procedure and a mathematical programming applicati...

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Vydáno v:2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing s. 212 - 219
Hlavní autoři: Yan Jin, Rong Qu, Atkin, Jason
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.09.2014
Témata:
Heuristic algorithms
Mathematical model
Optimization
Portfolios
Sociology
Statistics
Vectors
ISBN:9781479984473, 1479984477
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Shrnutí:This paper investigates a hybrid algorithm which utilizes exact and heuristic methods to optimise asset selection and capital allocation in portfolio optimisation. The proposed method is composed of a customised population based incremental learning procedure and a mathematical programming application. It is based on the standard Markowitz model with additional practical constraints such as cardinality on the number of assets and quantity of the allocated capital. Computational experiments have been conducted and analysis has demonstrated the performance and effectiveness of the proposed approach.
ISBN:9781479984473
1479984477
DOI:10.1109/SYNASC.2014.36