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arXiv · 2403.06011

Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals

Melda Alaluf·Giulia Crippa·Sinong Geng·Zijian Jing·Nikhil Krishnan·Sanjeev Kulkarni·Wyatt Navarro·Ronnie Sircar·Jonathan Tang
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Abstract

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.

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Melda Alaluf, Giulia Crippa, Sinong Geng, Zijian Jing, Nikhil Krishnan, Sanjeev Kulkarni, Wyatt Navarro, Ronnie Sircar, Jonathan Tang. 2024-03-09. Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals. https://arxiv.org/abs/2403.06011

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