ساخت پرتفوی های استراتژی سرمایه گذاری توسط الگوریتم ژنتیک ترکیبی

The classical portfolio problem is a problem of distributing capital to a set of securities. By generalizing the set of securities to a set of investment strategies (or security-rule pairs), this study proposes an investment strategy portfolio problem, which becomes a problem of distributing capital to a set of investment strategies. Since the investment strategy portfolio problem can be formulated as a combination optimization problem, a new combination genetic algorithm is proposed for solving the new investment strategy portfolio problem. Experimental results show that the idea of investment strategy portfolios is feasible and the combination genetic algorithm is effective on the investment strategy portfolio problem.

The classical portfolio problem is a problem of distributing capital to a set of securities (Gondzio and Grothey, 2007, Ince and Trafalis, 2006, Markowitz and Arnott, 1952 and Wu and Chang, 2007). By generalizing the set of securities to a set of investment strategies (or security-rule pairs), this study proposes an investment strategy portfolio problem, which becomes a problem of distributing capital to a set of investment strategies. The classical portfolio problem can be viewed a special case of the new investment strategy portfolio problem with buy-and-hold as the only trading rule. Both the investment strategy portfolio problem and the classical portfolio problem can be formulated as combination optimization problems. Therefore, a new combination genetic algorithm (CGA) is proposed for solving the combination optimization problem in general, and the new investment strategy portfolio problem in particular. Statistical test result indicates that the performance of our CGA is significantly better than that of uniform allocation. Experimental results show that the idea of investment strategy portfolios is feasible and the combination genetic algorithm is effective on the investment strategy portfolio problem. The rest of this paper is organized as follows. Section 2 reviews the classical portfolio problem and genetic algorithms. Section 3 describes the investment strategy portfolio problem and our solution method, the combination genetic algorithm. Section 4 presents the results of our experiments. Section 5 gives the conclusions and future directions.

This study proposes a new investment strategy portfolio problem generalized from the classical portfolio problem. In addition, a new combination genetic algorithm is proposed for solving this new investment strategy portfolio problem. Experimental results have demonstrated the feasibility of the investment strategy portfolio idea and the effectiveness of our combination genetic algorithm on the investment strategy portfolio problem. Future work includes the incorporation of short trading rules in addition to current long trading rules. Generating variable weights instead of just constant weights is another interesting direction.