Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals

Melda Alaluf, Giulia Crippa, Sinong Geng, Zijian Jing, Nikhil Krishnan +4 more
3/9/2024
cs.LGmath.OC

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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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Cite this paper

@article{alaluf2024reinforcement,
  title  = {Reinforcement Learning Paycheck Optimization for Multivariate Financial Goals},
  author = {Melda Alaluf and Giulia Crippa and Sinong Geng and Zijian Jing and Nikhil Krishnan and Sanjeev Kulkarni and Wyatt Navarro and Ronnie Sircar and Jonathan Tang},
  year   = {2024},
  eprint = {2403.06011},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2403.06011v1}
}

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