Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929270490660864 |
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| author | Alaluf, Melda Crippa, Giulia Geng, Sinong Jing, Zijian Krishnan, Nikhil Kulkarni, Sanjeev Navarro, Wyatt Sircar, Ronnie Tang, Jonathan |
| author_facet | Alaluf, Melda Crippa, Giulia Geng, Sinong Jing, Zijian Krishnan, Nikhil Kulkarni, Sanjeev Navarro, Wyatt Sircar, Ronnie Tang, Jonathan |
| contents | We study paycheck optimization, which examines how to allocate income in order to achieve several competing financial goals. For paycheck optimization, a quantitative methodology is missing, due to a lack of a suitable problem formulation. To deal with this issue, we formulate the problem as a utility maximization problem. The proposed formulation is able to (i) unify different financial goals; (ii) incorporate user preferences regarding the goals; (iii) handle stochastic interest rates. The proposed formulation also facilitates an end-to-end reinforcement learning solution, which is implemented on a variety of problem settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06011 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals Alaluf, Melda Crippa, Giulia Geng, Sinong Jing, Zijian Krishnan, Nikhil Kulkarni, Sanjeev Navarro, Wyatt Sircar, Ronnie Tang, Jonathan Machine Learning Optimization and Control We study paycheck optimization, which examines how to allocate income in order to achieve several competing financial goals. For paycheck optimization, a quantitative methodology is missing, due to a lack of a suitable problem formulation. To deal with this issue, we formulate the problem as a utility maximization problem. The proposed formulation is able to (i) unify different financial goals; (ii) incorporate user preferences regarding the goals; (iii) handle stochastic interest rates. The proposed formulation also facilitates an end-to-end reinforcement learning solution, which is implemented on a variety of problem settings. |
| title | Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals |
| topic | Machine Learning Optimization and Control |
| url | https://arxiv.org/abs/2403.06011 |