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.
@article{arxiv.2403.06011,
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},
journal= {arXiv preprint arXiv:2403.06011},
year = {2024}
}