| Autor: |
Alaluf, Melda, Crippa, Giulia, Geng, Sinong, Jing, Zijian, Krishnan, Nikhil, Kulkarni, Sanjeev, Navarro, Wyatt, Sircar, Ronnie, Tang, Jonathan |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Risk and Decision Analysis, Volume 9, 2023 |
| Druh dokumentu: |
Working Paper |
| Popis: |
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. |
| Databáze: |
arXiv |
| Externí odkaz: |
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