Optimization Algorithms for Pension Asset Allocation Under Market Volatility
By Akshay Sharma & Satish Kabade
Pension fund organizations function as essential financial entities that protect employee retirement funds to provide complete and punctual pension distributions. Pension fund effectiveness through asset distribution determines how well a fund meets its future obligations. Traditional methods of portfolio optimization align with the Modern Portfolio Theory through the Markowitz model to help determine asset allocation by defining the relationship between return and risk. These static modeling approaches fail to fulfill their purpose in real market environments because financial markets today experience increasing rates of volatility and unpredictable economic conditions and rapid changes in economic conditions. In view of current market challenges, alternative solutions to manage dynamic portfolios effectively are needed, which AI, in conjunction with computational optimization techniques, offers. Deep learning (DL), reinforcement learning (RL), genetical algorithms (GA), particle swarm optimization (PSO) are the key development of pension fund asset allocation and their implementation under a volatile market. The research presents a critical comparison between the two approaches based on the fund stability and return optimization and investment risk management using AI based algorithms.
Source SSRN
