Ozhamaratli, Fatih;
Barucca, Paolo;
(2023)
Heterogeneous Retirement Savings Strategy Selection with
Reinforcement Learning.
Entropy
, 25
(7)
, Article 977. 10.3390/e25070977.
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Abstract
Saving and investment behaviour is crucial for all individuals to guarantee their welfare during work-life and retirement. We introduce a deep reinforcement learning model in which agents learn optimal portfolio allocation and saving strategies suitable for their heterogeneous profiles. The environment is calibrated with occupation- and age-dependent income dynamics. The research focuses on heterogeneous income trajectories dependent on agents’ profiles and incorporates the parameterisation of agents’ behaviours. The model provides a new flexible methodology to estimate lifetime consumption and investment choices for individuals with heterogeneous profiles.
| Type: | Article |
|---|---|
| Title: | Heterogeneous Retirement Savings Strategy Selection with Reinforcement Learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/e25070977 |
| Publisher version: | https://doi.org/10.3390/e25070977 |
| Language: | English |
| Additional information: | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | agent based modelling; retirement finances; deep reinforcement learning; financial computing; portfolio choice; profile heterogeneity |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10172763 |
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