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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 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.
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.
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