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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References
Markowitz, H.: Portfolio selection. J. Financ. 7(1), 77–91 (1952)
Sortino, F.A., Price, L.N.: Performance measurement in a downside risk framework. J. Invest. 3(3), 59–64 (1994)
Sharpe, W.F.: The sharpe ratio. J. Portfolio Mgmt. 21(1), 49–58 (1994)
Sharpe, W.F.: Capital asset prices: a theory of market equilibrium under conditions of risk. J. Financ. 19(3), 425–442 (1964)
Bacon, C.R.: Practical Portfolio Performance Measurement and Attribution, vol. 568. Wiley, Hoboken (2011)
Anagnostopoulos, K.P., Mamanis, G.: A portfolio optimization model with three objectives and discrete variables. Comput. Oper. Res. 37(7), 1285–1297 (2010)
Mansini, R., Speranza, M.G.: Heuristic algorithms for the portfolio selection problem with minimum transaction lots. Eur. J. Oper. Res. 114(2), 219–233 (1999)
Lin, C.C., Liu, Y.T.: Genetic algorithms for portfolio selection problems with minimum transaction lots. Eur. J. Oper. Res. 185(1), 393–404 (2008)
Chang, T.J., Meade, N., Beasley, J.E., Sharaiha, Y.M.: Heuristics for cardinality constrained portfolio optimisation. Comput. Oper. Res. 27(13), 1271–1302 (2000)
Tapia, M.G.C., Coello, C.A.C.: Applications of multi-objective evolutionary algorithms in economics and finance: a survey. In: IEEE Congress on Evolutionary Computation, CEC 2007, pp. 532–539. IEEE (2007)
Coello, C.A.C.: Evolutionary multi-objective optimization and its use in finance. In: Handbook of Research on Nature Inspired Computing for Economy and Management. Idea Group Publishing, Hershey (2006)
Ponsich, A., Jaimes, A.L., Coello, C.A.C.: A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications. IEEE Trans. Evol. Comput. 17(3), 321–344 (2013)
Chiam, S., Tan, K., Al Mamum, A.: Evolutionary multi-objective portfolio optimization in practical context. Int. J. Autom. Comput. 5(1), 67–80 (2008)
Branke, J., Scheckenbach, B., Stein, M., Deb, K., Schmeck, H.: Portfolio optimization with an envelope-based multi-objective evolutionary algorithm. Eur. J. Oper. Res. 199(3), 684–693 (2009)
Anagnostopoulos, K.P., Mamanis, G.: The mean-variance cardinality constrained portfolio optimization problem: an experimental evaluation of five multiobjective evolutionary algorithms. Expert Syst. Appl. 38(11), 14208–14217 (2011)
Hassan, G., Clack, C.D.: Robustness of multiple objective GP stock-picking in unstable financial markets: real-world applications track. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 1513–1520. ACM (2009)
Cobb, H.G.: An investigation into the use of hypermutation as an adaptive operator in genetic algorithms having continuous, time-dependent nonstationary environments. Technical report, Naval Research Lab Washington D.C. (1990)
Yang, S., Tinós, R.: A hybrid immigrants scheme for genetic algorithms in dynamic environments. Int. J. Autom. Comput. 4(3), 243–254 (2007)
Filipiak, P., Lipinski, P.: Dynamic portfolio optimization in ultra-high frequency environment. In: Squillero, G., Sim, K. (eds.) EvoApplications 2017. LNCS, vol. 10199, pp. 34–50. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55849-3_3
Laumanns, M.: SPEA2: Improving the strength Pareto evolutionary algorithm. Eidgenössische Technische Hochschule Zürich (ETH), Institut für Technische Informatik und Kommunikationsnetze (TIK) (2001)
Zhang, X., Tian, Y., Jin, Y.: A knee point-driven evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 19(6), 761–776 (2015)
Koza, J.: Genetic Programming on the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
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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