Abstract
This paper concerns the problem of portfolio optimization in dynamic environments using multi-objective evolutionary algorithms. Financial markets are characterized by volatility and uncertainty making portfolio optimization a challenging task. A novel multi-objective genetic programming algorithm is proposed, which is a memory enhanced version of the standard SPEAII algorithm. The proposed algorithm employs an explicit memory to store a number of non-dominated solutions. These solutions are reused in the later stages for adapting to the changing environments. A stock ranking based trading simulation is used for fitness evaluation and a probabilistic metric is employed to choose a solution from the Pareto Front. The experiments are performed on the constituent stocks of the S&P BSE FMCG Index. The results, evaluated using RMSE, MEA and cumulative returns, are very promising.
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Acknowledgement
The authors would like to thank Prof. (Mrs.) Lakshmi Kurup, D. J. Sanghvi College of Engineering, for her assistance on the machine learning front and Dr. Ram Mangrulkar, D. J. Sanghvi College of Engineering, for his review and comments that greatly improved the manuscript.
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Shah, P., Shah, S. (2017). Portfolio Optimization in Dynamic Environments Using MemSPEAII. In: Ghosh, A., Pal, R., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2017. Lecture Notes in Computer Science(), vol 10682. Springer, Cham. https://doi.org/10.1007/978-3-319-71928-3_40
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DOI: https://doi.org/10.1007/978-3-319-71928-3_40
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