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Portfolio optimization using genetic algorithm.

Authors:

Vikkitharan Gnanasambandapillai

UNSW Sydney

65 p. Portfolio optimization is a multi-objective, non-linear optimization problem for maximum return with minimum risk. It normally has a huge number of input variables (assets) and numerous local optima; Therefore mathematical derivatives based optimization is very difficult or impossible to be applied. Genetic algorithms (GA) have been used for this kind of problem, as it is good to deal with optimization involving a large number of inputs and non-linear multi-model objectives. But performance of GA optimization degrades when the number of inputs of the problem increases. MSC(COMPUTER CONTROL and AUTOMATION)

A Novel Data Driven Machine Learning Algorithm For Fuzzy Estimates of Optimal Portfolio Weights and Risk Tolerance Coefficient

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Jul 2021

ResearchGate has not been able to resolve any references for this publication.

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