Deep Reinforcement Learning for Optimal Investment and Saving Strategy Selection in Heterogeneous Profiles: Intelligent Agents working towards retirement

Fatih Ozhamaratli and Paolo Barucca
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Fatih Ozhamaratli: University College London
Paolo Barucca: University College London

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Abstract: 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.

Date: 2022-06
New Economics Papers: this item is included in nep-age and nep-big
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