Melda Alaluf, Giulia Crippa, Sinong Geng, Zijian Jing, Nikhil Krishnan, Sanjeev Kulkarni, Wyatt Navarro, R. Sircar, Jonathan Tang
{"title":"多变量财务目标的强化学习工资优化","authors":"Melda Alaluf, Giulia Crippa, Sinong Geng, Zijian Jing, Nikhil Krishnan, Sanjeev Kulkarni, Wyatt Navarro, R. Sircar, Jonathan Tang","doi":"10.3233/rda-220025","DOIUrl":null,"url":null,"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.","PeriodicalId":38805,"journal":{"name":"Risk and Decision Analysis","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Reinforcement learning paycheck optimization for multivariate financial goals\",\"authors\":\"Melda Alaluf, Giulia Crippa, Sinong Geng, Zijian Jing, Nikhil Krishnan, Sanjeev Kulkarni, Wyatt Navarro, R. Sircar, Jonathan Tang\",\"doi\":\"10.3233/rda-220025\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":38805,\"journal\":{\"name\":\"Risk and Decision Analysis\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-05-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Risk and Decision Analysis\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/rda-220025\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Economics, Econometrics and Finance\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Risk and Decision Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/rda-220025","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Economics, Econometrics and Finance","Score":null,"Total":0}
Reinforcement learning paycheck optimization for multivariate financial goals
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.