Reinforcement learning paycheck optimization for multivariate financial goals

Article type: Research Article

Authors: Alaluf, Melda^a | Crippa, Giulia^b | Geng, Sinong^c | Jing, Zijian^a | Krishnan, Nikhil^b | Kulkarni, Sanjeev^{b; d} | Navarro, Wyatt^a | Sircar, Ronnie^b; | Tang, Jonathan^a

Affiliations: [a] San Francisco, CA, USA | [b] ORFE Department, Princeton University, Princeton, NJ, USA | [c] Computer Science Department, Princeton University, Princeton, NJ, USA | [d] Electrical & Computer Engineering Department, Princeton University, Princeton, NJ, USA

Correspondence: [*] Corresponding author: Ronnie Sircar. E-mail: [email protected]

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.

Keywords: Reinforcement learning, financial planning, wealth management, personal finance

DOI: 10.3233/RDA-220025

Journal: Risk and Decision Analysis, vol. 9, no. 1, pp. 11-18, 2023