Genetic Algorithms and Investment Strategies
Genetic Algorithms and Investment Strategies
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Dependency mining in large sets of stock market trading rules
Enhanced methods in computer security, biometric and artificial intelligence systems
Genetic Algorithms in Electromagnetics
Genetic Algorithms in Electromagnetics
Discovering stock market trading rules using multi-layer perceptrons
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Infeasibility driven evolutionary algorithm with ARIMA-based prediction mechanism
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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This paper addresses a problem of finding portfolios that perform better than investment funds while showing similar behaviour. The quality of investment portfolio can be measured using various criteria such as the return and some kind of risk measurement. Investors seek to maximize return while minimizing risk. In order to achieve this goal various instruments are considered. One of the possibilities is to entrust the assets to an investment fund. Investment funds build their own portfolios of stocks, bonds, commodities, currencies, etc. In this paper we consider the problem of finding a portfolio which outperforms a given investment fund with respect to both the return and the risk and which also behaves in a similar way to the given fund. The rationale behind such an approach is that investment strategies of mutual funds are prepared by experts and are therefore expected to be reasonably good in terms of both the return and the risk. To achieve the presented goal we use a multiobjective evolutionary algorithm with a dedicated "division mutation" operator and a local search procedure. Presented method seems capable of building portfolios with desired qualities.