{"title":"利用现金流进行稳健的在线投资组合优化","authors":"","doi":"10.1016/j.omega.2024.103169","DOIUrl":null,"url":null,"abstract":"<div><p>One fundamental issue in finance is portfolio selection, which seeks the best strategy for assigning capital among a group of assets. There has been growing interest in online portfolio selection where the investment strategy is frequently readjusted in a short time as new financial market data arrives constantly. Numerous effective algorithms have been extensively examined both in terms of theoretical analysis and empirical evaluation. Previous online portfolio selection algorithms that incorporate transaction costs are limited by the fact that they often approximate the transaction remainder factor instead of calculating it precisely. This could lead to suboptimal investment performance. To address this issue, we present an innovative method that considers transaction costs and resolves the accurate transaction remainder factor and the optimal portfolio allocation simultaneously for each period. In addition, we take into account the open-end fund, which permits constant cash inflows, and develop a framework for online portfolio selection. We also incorporate the uncertainty set to minimize the impact of the prediction error during the prediction process. Utilizing the framework presented in this innovative model, we develop a novel algorithm for online portfolio selection that incorporates transaction costs and continuous cash inflows with the objective of maximizing cumulative wealth. Numerical experiments show that the proposed algorithms are able to handle transaction costs and constant cash inflows effectively.</p></div>","PeriodicalId":19529,"journal":{"name":"Omega-international Journal of Management Science","volume":null,"pages":null},"PeriodicalIF":6.7000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robust online portfolio optimization with cash flows\",\"authors\":\"\",\"doi\":\"10.1016/j.omega.2024.103169\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>One fundamental issue in finance is portfolio selection, which seeks the best strategy for assigning capital among a group of assets. There has been growing interest in online portfolio selection where the investment strategy is frequently readjusted in a short time as new financial market data arrives constantly. Numerous effective algorithms have been extensively examined both in terms of theoretical analysis and empirical evaluation. Previous online portfolio selection algorithms that incorporate transaction costs are limited by the fact that they often approximate the transaction remainder factor instead of calculating it precisely. This could lead to suboptimal investment performance. To address this issue, we present an innovative method that considers transaction costs and resolves the accurate transaction remainder factor and the optimal portfolio allocation simultaneously for each period. In addition, we take into account the open-end fund, which permits constant cash inflows, and develop a framework for online portfolio selection. We also incorporate the uncertainty set to minimize the impact of the prediction error during the prediction process. Utilizing the framework presented in this innovative model, we develop a novel algorithm for online portfolio selection that incorporates transaction costs and continuous cash inflows with the objective of maximizing cumulative wealth. Numerical experiments show that the proposed algorithms are able to handle transaction costs and constant cash inflows effectively.</p></div>\",\"PeriodicalId\":19529,\"journal\":{\"name\":\"Omega-international Journal of Management Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.7000,\"publicationDate\":\"2024-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Omega-international Journal of Management Science\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0305048324001348\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MANAGEMENT\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Omega-international Journal of Management Science","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0305048324001348","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
Robust online portfolio optimization with cash flows
One fundamental issue in finance is portfolio selection, which seeks the best strategy for assigning capital among a group of assets. There has been growing interest in online portfolio selection where the investment strategy is frequently readjusted in a short time as new financial market data arrives constantly. Numerous effective algorithms have been extensively examined both in terms of theoretical analysis and empirical evaluation. Previous online portfolio selection algorithms that incorporate transaction costs are limited by the fact that they often approximate the transaction remainder factor instead of calculating it precisely. This could lead to suboptimal investment performance. To address this issue, we present an innovative method that considers transaction costs and resolves the accurate transaction remainder factor and the optimal portfolio allocation simultaneously for each period. In addition, we take into account the open-end fund, which permits constant cash inflows, and develop a framework for online portfolio selection. We also incorporate the uncertainty set to minimize the impact of the prediction error during the prediction process. Utilizing the framework presented in this innovative model, we develop a novel algorithm for online portfolio selection that incorporates transaction costs and continuous cash inflows with the objective of maximizing cumulative wealth. Numerical experiments show that the proposed algorithms are able to handle transaction costs and constant cash inflows effectively.
期刊介绍:
Omega reports on developments in management, including the latest research results and applications. Original contributions and review articles describe the state of the art in specific fields or functions of management, while there are shorter critical assessments of particular management techniques. Other features of the journal are the "Memoranda" section for short communications and "Feedback", a correspondence column. Omega is both stimulating reading and an important source for practising managers, specialists in management services, operational research workers and management scientists, management consultants, academics, students and research personnel throughout the world. The material published is of high quality and relevance, written in a manner which makes it accessible to all of this wide-ranging readership. Preference will be given to papers with implications to the practice of management. Submissions of purely theoretical papers are discouraged. The review of material for publication in the journal reflects this aim.