Abstract
Optimizing a stock portfolio from a given financial dataset is always a very attractive task, as various factors should be considered. Hence, many methods based on evolutionary algorithms have been developed in the past decades to deal with the portfolio optimization problem. To provide a more flexible stock portfolio, we propose an algorithm to optimize a group stock portfolio by using a grouping genetic algorithm. In accordance with the optimized group stock portfolio, many stock portfolios can be generated and provided to investors. Each chromosome in the genetic algorithm is composed of a grouping part, a stock part and a stock portfolio part. The grouping and stock parts are used to indicate how to divide stocks into groups. The stock portfolio part is used to represent how many stocks should be selected from groups to form a portfolio and what units should be purchased. Four fitness functions are designed to evaluate each individual. Each of them is composed of the group balance, the unit balance, the stock price balance and the portfolio satisfaction. Genetic operations, including crossover, mutation and inversion, are then executed to obtain new offspring to find the best solution. Furthermore, the proposed approach with a trading mechanism is designed to get a more useful group stock portfolio. Experiments on 31 stocks in accordance with four scenarios were conducted to show the merits and effectiveness of the proposed approach.
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References
Ait-Sahalia Y, Hansen LP (2009) Handbook of financial econometrics: tools and techniques. Elsevier, Amsterdam
Barak S, Abessi M, Modarres M (2013) Fuzzy turnover rate chance constraints portfolio model. Eur J Oper Res 228(1):141–147
Bevilacqua V, Pacelli V, Saladino S (2012) A novel multi objective genetic algorithm for the portfolio optimization. In: Advanced intelligent computing, pp 186–193
Brown EC, Sumichrast RT (2005) Evaluating performance advantages of grouping genetic algorithms. Eng Appl Artif Intell 18(1):1–12
Bermúdez J, Segura J, Vercher E (2012) A multi-objective genetic algorithm for cardinality constrained fuzzy portfolio selection. Fuzzy Sets Syst 188:16–26
Birattari M, Yuan Z, Balaprakash P, Stützle T (2010) F-race and iterated F-race: an overview. In: Bartz-Beielstein T, Chiarandini M, Paquete L, Preuss M (eds) Experimental methods for the analysis of optimization algorithms. Springer, Berlin
Chen CH, Hsieh CY (2016) Mining actionable stock portfolio by genetic algorithms. Journal of Information Science and Engineering 32(6):1657–1678
Chou YH, Kuo SY, Chen CY, Chao HC (2014) A rule-based dynamic decision-making stock trading system based on quantum-inspired tabu search algorithm. IEEE Access 2:883–896
Chen C H, Lin CB, Chen CC (2015) Mining group stock portfolio by using grouping genetic algorithms. In: The IEEE congress on evolutionary computation, pp 738–743
Chen W, Li D, Liu YJ (2018) A novel hybrid ICA-FA algorithm for multi-period uncertain portfolio optimization model based on multiple criteria. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2018.2829463
Chen J, Hou J, Wu S, Chang-Chien Y (2009) Constructing investment strategy portfolios by combination genetic algorithms. Expert Syst Appl 36(2):3824–3828
Chang J, Yang SC, Chang KJ (2009) Portfolio optimization problems in different risk measures using genetic algorithm. Expert Syst Appl 36:10529–10537
Deb K, Sundar J (2006) Reference point based multi-objective optimization using evolutionary algorithms. In: The annual conference on genetic and evolutionary computation, pp 635–42
Deb K, Jain H (2014) An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Trans Evol Comput 18(4):577–601
Pham Dinh T, Pham VN, Le Thi HA (2014) DC Programming and DCA for portfolio optimization with linear and fixed transaction costs. In: The Asian conference on intelligent information and database systems, pp 392–402
Elhachloufi ZGM, Hamza F (2012) Stocks portfolio optimization using classification and genetic algorithms. Applied Mathematical Sciences 6:4673–4684
Falkenauer E (1994) A new representation and operators for genetic algorithms applied to grouping problems. Evol Comput 2:123–144
Falkenauer E (1996) A hybrid grouping genetic algorithm for bin packing. J Heuristics 2:5–30
Fabozzi FJ, Kolm PN, Pachamanova DA, Focardi SM (2007) Robust portfolio optimization and management. Wiley, New York
Gupta P, Mehlawat MK, Mittal G (2012) Asset portfolio optimization using support vector machines and real-coded genetic algorithm. J Global Optim 53:297–315
Gupta P, Mehlawat MK, Saxena A (2012) Hybrid optimization models of portfolio selection involving financial and ethical considerations. Knowl-Based Syst 37:318–337
DE Goldberg (1989) Genetic algorithms in search, optimization, and machine learning. Addison Wesley, Reading
Gaspero LD, Tollo GD, Roli A, Schaerf A (2011) Hybrid metaheuristics for constrained portfolio selection problems. Quantitative Finance 11(10):1473–1487
Huxley TH (2007) The high dividend yield return advantage: an examination of empirical data associating investment in high dividend yield securities with attractive returns over long measurement periods. Tweedy, Browne Company LLC, New York
Hoklie LRZ (2010) Resolving multi objective stock portfolio optimization problem using genetic algorithm. In: International conference on computer and automation engineering, pp 40–44
Hassanzadeh F, Collan M, Modarres M (2012) A practical approach to R & D portfolio selection using the fuzzy pay-off method. IEEE Trans Fuzzy Syst 20:615–622
Hung K, Cheung Y, Xu L (2003) An extended ASLD trading system to enhance portfolio management. IEEE Trans Neural Networks 14(2):413–425
Hutter F, Hoos HH, Leyton-Brown K, Stützle T (2009) ParamILS: an automatic algorithm configuration framework. Journal of Artificial Research 36:267–306
Farrell JL Jr (1974) Analyzing covariation of returns to determine homogeneous stock groupings. The Journal of Business 47(2):186–207
Farrell JL Jr (1975) Homogeneous stock groupings: implications for portfolio management. Financial Analysts Journal 31(3):50–56
Fu TC, Chung CP, Chung FL (2013) Adopting genetic algorithms for technical analysis and portfolio management. Comput Math Appl 66(10):1743–1757
Kumar R, Bhattacharya S (2012) Cooperative search using agents for cardinality constrained portfolio selection problem. IEEE Trans Syst Man Cybern Part C Appl Rev 42:1510–1518
Kim Y, Enke D (2016) Developing a rule change trading system for the futures market using rough set analysis. Expert Syst Appl 59:165–173
Kuo SY, Kuo C, Chou YH (2013) Dynamic stock trading system based on quantum-inspired tabu search algorithm. In: The IEEE congress on evolutionary computation, pp 1029–1036
Kim MJ, Lee Y, Kim JH, Kim WC (2016) Sparse tangent portfolio selection via semi-definite relaxation. Operations Research Letters 44(4):540–543
Kellerer H, Mansini R, Grazia SM (2000) Selecting portfolios with fixed costs and minimum transaction lots. Ann Oper Res 99:287–304
Kao C, Steuer RE (2016) Value of information in portfolio selection, with a Taiwan stock market application illustration. Eur J Oper Res 253(2):418–427
Lin PC (2012) Portfolio optimization and risk measurement based on non-dominated sorting genetic algorithm. Journal of Industrial and Management Optimization 8:549–564
Lin CC, Liu YT (2008) Genetic algorithms for portfolio selection problems with minimum transaction lots. Eur J Oper Res 185:393–404
Liu YJ, Zhang WG (2013) Fuzzy portfolio optimization model under real constraints. Insur Math Econ 53(3):704–711
Li J, Xu J (2013) Multi-objective portfolio selection model with fuzzy random returns and a compromise approach-based genetic algorithm. Inf Sci 220:507–521
Markowitz H (1952) Portfolio selection. Journal of Finance 7(1):77–91
Markowitz HM (2009) Harry Markowitz: selected works. World Scientific Publishing Company, Singapore
Mutingi M, Mbohwa C (2016) Grouping genetic algorithms: advances and applications. Springer, Berlin
Mansini R, Speranza MG (2005) An exact approach for portfolio selection with transaction costs and rounds. IIE Trans 37:919–929
Najafi AA, Pourahmadi Z (2016) An efficient heuristic method for dynamic portfolio selection problem under transaction costs and uncertain conditions. Physica A 448:154–162
Pankratz G (2005) A grouping genetic algorithm for the pickup and delivery problem with time windows. Operations Research Spectrum 27:21–41
Patalia TP, Kulkarni G (2011) Design of genetic algorithm for knapsack problem to perform stock portfolio selection using financial indicators. In: International conference on computational intelligence and communication networks, pp 289–292
Quiroz-Castellanos M, Cruz-Reyes L, Torres-Jimenez J, Claudia Gómez S, Huacuja HJF, Alvim ACF (2015) A grouping genetic algorithm with controlled gene transmission for the bin packing problem. Comput Oper Res 55:52–64
Roy AD (1952) Safety first and the holding of assets. Econometrica 20:431–449
Rekiek B, Delchambre A, Saleh HA (2006) Handicapped person transportation: an application of the grouping genetic algorithm. Engineering Application of Artificial Intelligence 19:511–520
Tollo GD, Lardeux F, Maturana J, Saubion F (2015) An experimental study of adaptive control for evolutionary algorithms. Appl Soft Comput 35:359–372
Tollo GD, Roli A (2007) Metaheuristics for the portfolio selection problem. International Journal of Operations Research 5(1):13–35
Wah E, Mei Y, Wah BW (2011) Portfolio optimization through data conditioning and aggregation. In: IEEE international conference on tools with artificial intelligence, pp 253–260
You CF, Lin SH, Hsiao HF (2010) Dividend yield investment strategies in the Taiwan stock market. Invest Manag Financ Innov 7(2):189–199
Yao H, Li Z, Li D (2016) Multi-period mean-variance portfolio selection with stochastic interest rate and uncontrollable liability. Eur J Oper Res 252(3):837–851
Zitzler E, Laumanns M, Thiele L (2001) SPEA2: Improving the strength Pareto evolutionary algorithm for multiobjective optimization. In: Evolutionary methods for design, optimization and control with application to industrial problems, pp 95–100
Acknowledgements
This research was supported by the Ministry of Science and Technology of the Republic of China under Grants MOST 104-2221-E-032-040 and MOST 106-2221-E-032-049-MY2.
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Chen, CH., Lu, CY. & Lin, CB. An intelligence approach for group stock portfolio optimization with a trading mechanism. Knowl Inf Syst 62, 287–316 (2020). https://doi.org/10.1007/s10115-019-01353-2
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DOI: https://doi.org/10.1007/s10115-019-01353-2