A Novel PSO for Multi-stage Portfolio Planning

In this paper, we present a decision-making process that uses our proposed quasi-oppositional comprehensive learning particle swarm optimizers (QCLPSO) to solve multi-period portfolio problem. Multi-stage stochastic financial optimization takes order with portfolio in ever-changing financial markets by periodically rebalancing the asset portfolio to achieve return maximization and/or risk minimization. It brings together all major financial-related decision in a single consistent structure and integrates investment strategies, liability decisions and savings strategies in an all-around fashion. The objective function is classical return-variance function. The performance of our algorithm is demonstrated by optimizing the allocation of cash and various stocks in SSE 180 Index. Experiments are conducted to compare performance of the portfolio optimized by different objective functions with PSO and genetic algorithm (GA) in the terms of efficient frontiers.