Selecting Best Investment Opportunities from Stock Portfolios Optimized by a Multiobjective Evolutionary Algorithm
AbstractMultiobjective optimization of portfolios aims at finding sets of stocks which are expected to provide a possibly high return while retaining a moderate level of risk. The Pareto front of portfolios generated by the optimization algorithm represents attainable trade-offs between returns obtained by the portfolios and the level of risk involved in the investment. This paper studies the relationship between location of portfolios in the Pareto front and future returns of these portfolios. It is observed that the highest future returns can be obtained for the portfolios with the highest return and risk measures observed in the past but also for those with the lowest return and risk in the Pareto front. Neither constantly selecting portfolios with high return on historical data nor, conversely, those with low historical risk (but also low return) yields high future returns. Based on these observations a method is proposed for adaptively selecting the best portfolios for investment from solutions contained in the Pareto front. The proposed method selects high-return (but also high-risk) portfolios or low-risk (but also low-return) portfolios based on the behaviour of the stock marked index in the time period preceding the moment of investment. The proposed method outperforms both the strategy of always selecting the portfolios with the highest return from the past and the risk-averse strategy of selecting portfolios with low risk measure
|Publication size in sheets||0.5|
|Book||Silva Sara (eds.): Proceedings of the 2015 on Genetic and Evolutionary Computation Conference, 2015, Association for Computing Machinery, ISBN 978-1-4503-3472-3, 1466 p.|
|Keywords in English||stock portfolio optimization, multiobjective evolutionary op- timization, average linkage method|
|Publication indicators||= 3; = 3|
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