Improving Portfolio Efficiency: A Genetic Algorithm Approach
- Xiaolou Yang
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In this paper, I present a decision-making process that incorporates a Genetic Algorithm (GA) into a state dependent dynamic portfolio optimization system. A GA is a probabilistic search approach and thus can serve as a stochastic problem solving technique. A Genetic Algorithm solves the model by forward-looking and backward-induction, which incorporates both historical information and future uncertainty when estimating the asset returns. It significantly improves the accuracy of expected return estimation and thus improves the overall portfolio efficiency over the classical mean-variance method. In addition a GA could handle a large variety of future uncertainties, which overcome the computational difficulties in the traditional Bayesian approach.
- Bauer, Richard J. Jr. (1994). Genetic Algorithms and Investment Strategies. John Wiley and Sons, Inc.
- Berger, A.J. (1995). Solving global optimization problems in long-term financial planning. Statistics and Operations Research Technical Report, Princeton University.
- Best, M.J. and Grauer, R.R. (1991). On the sensitivity of mean-variance-efficient portfolios to changes in asset means: Some analytical and computational results. Review of Financial Studies, 4, 315–342. CrossRef
- Chopra, V.K. and Ziemba, W.T. (1993). The effect of errors in means, variances, and covariances on optimal portfolio choice. Journal of Portfolio Management, 19, 6–11. CrossRef
- De Santis, G. and Gerard, B. (1997). Internationa asset pricing and portfolio diversification with time-varying risk. Journal of Finance, 52, 1881–1912. CrossRef
- Fichter, Daniel P. (2000). Application of Genetic Algorithms in portfolio optimization for the oil and gas industry. Proc. of the SPE Annual Technical Conference, Texas.
- Goldberg, D.E. (1989). Genetic Algorithms in search, optimization and machine learning. Addison-Wesley.
- Grefenstette, J.J. and Baker, J.J. (1989). How Genetic Algorithms work: A critical look at implicit parallelism. Proc. of the Third International Conference on Genetic Algorithms, pp. 20–27.
- Grefenstette, J.J. (1991). Conditions for implicit parallelism. Foundations of Genetic Algorithms, 252–261.
- Grinblatt, M. and Titman, S. (1989). Financial markets and corporate strategy. McGrawill Companies, Inc.
- Holland, J.H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control and artificial intelligence, University of Michigan Press.
- Jorion, P. (1985). International portfolio diversification with estimation risk. Journal of Business, 58, 259–278. CrossRef
- Jorion, P. (1986). Bayes-stein estimation for portfolio analysis. Journal of Financial and Quantitative Analysis, 21, 279–292. CrossRef
- Klein, R.W. and Bawa, V.S. (1976). The effect of estimation risk on optimal portfolio choice. Journal of Financial Economics, 3, 215–231. CrossRef
- Knight, F. (1921). Risk, uncertainty and profit, Houghton Mifflin, Boston.
- LeRoy, S.F. and Werner (2001). Principles of financial economics, Cambridge University Press.
- Marlowitx, H.M. (1952). Portfolio selection. Journal of Finance, 7, 77–91. CrossRef
- Markowitz, H. (1987). Mean-variance analysis in portfolio choice and capital markets, Cambridge Press.
- Markowitz, H. (1991). Portfolio selection: Efficient diversification of investments, New York Press, 1959.
- Michalewicz, Z. (1996). Genetic Algorithms + data structures=evolution programs. Springer-Verlag.
- Michaud, R.O. (1989). The Markowitz optimization enigma: Is optimized optimal. Financial Analysts Journal, 45, 31–42. CrossRef
- Michaud, R.O. (1998). Efficient Asset Management, Harvard Business School Press, Boston.
- Mulvey, J.M. (1997). Strategic financial risk management and operations research. European Journal of Operational Research, 1–16.
- Pastor, L. (2000). Portfolio selection and asset pricing models. Journal of Finance, 55, 179–123. CrossRef
- Pastor, L. and Stambaugh, R.F. (2000). Comparing asset pricing models: An investment perspective. Journal of Financial Economics, 56, 335–381. CrossRef
- Schaerf, A. (2002). Local search techniques for constrained portfolio selection problems. Computational Economics, 20, 177–190. CrossRef
- Stephens, C., Waelbroeck, H. and Aguirre, R. (1999). Schemata as building blocks: Does size matter?. Foundations of Genetic Algorithms, 117–133.
- Whitley, D. (1992). Deception, dominance and implicit parallelism in genetic search. Annals of Mathematics and Artificial Intelligence, 5, 49–78. CrossRef
- Improving Portfolio Efficiency: A Genetic Algorithm Approach
Volume 28, Issue 1 , pp 1-14
- Cover Date
- Print ISSN
- Online ISSN
- Springer US
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- genetic algorithm
- estimation risk
- mean-variance analysis
- Bayesian approach
- Industry Sectors
- Xiaolou Yang (1)
- Author Affiliations
- 1. The School of Business, Humboldt State University, Humboldt, USA