Deep Reinforcement Learning for Optimal Investment and Saving Strategy Selection in Heterogeneous Profiles: Intelligent Agents working towards retirement
This work addresses the problem of financial planning for individuals with heterogeneous profiles in retirement savings, offering a domain-specific incremental improvement by applying existing deep reinforcement learning methods to new data in pension modeling.
The paper tackles the challenge of determining optimal saving and investment strategies for individuals with diverse income profiles in defined contribution pension plans, using deep reinforcement learning to train agents that learn suitable strategies, resulting in a flexible methodology for estimating lifetime consumption and investment choices under varying scenarios.
The transition from defined benefit to defined contribution pension plans shifts the responsibility for saving toward retirement from governments and institutions to the individuals. Determining optimal saving and investment strategy for individuals is paramount for stable financial stance and for avoiding poverty during work-life and retirement, and it is a particularly challenging task in a world where form of employment and income trajectory experienced by different occupation groups are highly diversified. We introduce a model in which agents learn optimal portfolio allocation and saving strategies that are suitable for their heterogeneous profiles. We use deep reinforcement learning to train agents. The environment is calibrated with occupation and age dependent income evolution dynamics. The research focuses on heterogeneous income trajectories dependent on agent profiles and incorporates the behavioural parameterisation of agents. The model provides a flexible methodology to estimate lifetime consumption and investment choices for heterogeneous profiles under varying scenarios.