Personal asset allocation using particle swarm optimization approach

Given the current benefits structure of the Taiwan Employee Retirement Income Security Act (TERISA), two pension plans (defined contribution and defined benefit) are examined to discuss the asset allocation of personal accounts, which may include N targets investment strategies. The study proposes Particle Swarm Optimization (PSO) approach to be compared with Genetic Algorithms for carrying out asset allocation and the findings indicate that PSO approach outperforms GAs approach in both solution quality and computation time. The adequacy of the pension plans is examined by an actuarial model based on the hypothesis that all the variables are set as random, including simulated salary growth rate, inflation rate, interest rate, and investment return rate.