A Parallel Monte Carlo Algorithm for the Life Cycle Asset Allocation Problem

Life cycle asset allocation is a crucial aspect of financial planning, especially for pension funds. Traditional methods often face challenges in computational efficiency and applicability to different market conditions. This study aimed to innovatively transplant an algorithm from reinforcement learning that enhances the efficiency and accuracy of life cycle asset allocation. We synergized tabular methods with Monte Carlo simulations to solve the pension problem. This algorithm was designed to correspond states in reinforcement learning to key variables in the pension model: wealth, labor income, consumption level, and proportion of risky assets. Additionally, we used cleaned and modeled survey data from Chinese consumers to validate the model’s optimal decision-making in the Chinese market. Furthermore, we optimized the algorithm using parallel computing to significantly reduce computation time. The proposed algorithm demonstrated superior efficiency compared to the traditional value iteration method. Serial execution of our algorithm took 29.88 min, while parallel execution reduced this to 1.42 min, compared to the 41.15 min required by the value iteration method. These innovations suggest significant potential for improving pension fund management strategies, particularly in the context of the Chinese market.