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Heterogeneous Retirement Savings Strategy Selection with Reinforcement Learning.

Fatih Ozhamaratli¹, Paolo Barucca¹

¹Department of Computer Science, University College London, London WC1E 6BT, UK.

Entropy (Basel, Switzerland)

|July 29, 2023

Summary

This summary is machine-generated.

This study uses deep reinforcement learning to help individuals optimize saving and investment strategies based on unique income profiles. The model aids in planning for financial welfare during work-life and retirement.

Keywords:

agent based modelling deep reinforcement learning financial computing portfolio choice profile heterogeneity retirement finances

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Area of Science:

Behavioral Economics
Computational Finance
Artificial Intelligence

Background:

Individual financial planning is essential for long-term economic security.
Heterogeneous income trajectories significantly impact saving and investment decisions.
Existing models often lack the flexibility to capture individual behavioral nuances.

Purpose of the Study:

To develop a deep reinforcement learning model for optimal personal finance strategies.
To account for diverse income dynamics and agent behaviors.
To provide a flexible methodology for estimating lifetime consumption and investment choices.

Main Methods:

Implementation of a deep reinforcement learning agent.
Calibration of the environment with occupation- and age-dependent income dynamics.
Parameterization of agent behaviors to reflect heterogeneous profiles.

Main Results:

Agents learn optimal portfolio allocation and saving strategies.
The model successfully incorporates heterogeneous income trajectories.
Demonstrates a flexible approach to individual financial decision-making.

Conclusions:

Deep reinforcement learning offers a powerful tool for personalized financial planning.
Accounting for individual income dynamics and behaviors is key to welfare.
The proposed methodology enhances the estimation of lifetime financial choices.