| 题名 | Adaptive online mean-variance portfolio selection with transaction costs |
| 作者 | |
| 通讯作者 | Ching, Wai-Ki |
| 发表日期 | 2023-12-19
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| DOI | |
| 发表期刊 | |
| ISSN | 1469-7688
|
| EISSN | 1469-7696
|
| 卷号 | 24期号:1 |
| 摘要 | Online portfolio selection is attracting increasing attention in both artificial intelligence and finance communities due to its efficiency and practicability in deriving optimal investment strategies in real investment activities where the market information is constantly renewed every second. The key issues in online portfolio selection include predicting the future returns of risky assets accurately given historical data and providing optimal investment strategies for investors in a short time. In the existing online portfolio selection studies, the historical return data of one risky asset is used to estimate its future return. In this paper, we incorporate the peer impact into the return prediction where the predicted return of one risky asset not only depends on its past return data but also the other risky assets in the financial market, which gives a more accurate prediction. An adaptive moving average method with peer impact (AOLPI) is proposed, in which the decaying factors can be adjusted automatically in the investment process. In addition, the adaptive mean-variance (AMV) model is firstly applied in online portfolio selection where the variance is employed to measure the investment risk and the covariance matrix can be linearly updated in the investment process. The adaptive online moving average mean-variance (AOLPIMV) algorithm is designed to provide flexible investment strategies for investors with different risk preferences. Finally, numerical experiments are presented to validate the effectiveness and advantages of AOLPIMV. |
| 关键词 | |
| 相关链接 | [来源记录] |
| 收录类别 | |
| 语种 | 英语
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| 学校署名 | 其他
|
| 资助项目 | National Natural Science Foundation of China["72201033","72002201"]
; Hong Kong Research Grants Council[17309522]
; Seed Funding Research Grant of HKU-TCL Joint Research Centre for Artificial Intelligence, The University of Hong Kong, Introduction Project of China Postdoctoral International Exchange Program[YJ20220045]
; China Postdoctoral Science Foundation[2022M720426]
; Beijing Institute of Technology Research Fund Program for Young Scholars, Guangdong Basic and Applied Basic Research Foundation[2023A1515030197]
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| WOS研究方向 | Business & Economics
; Mathematics
; Mathematical Methods In Social Sciences
|
| WOS类目 | Business, Finance
; Economics
; Mathematics, Interdisciplinary Applications
; Social Sciences, Mathematical Methods
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| WOS记录号 | WOS:001129290000001
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| 出版者 | |
| ESI学科分类 | ECONOMICS BUSINESS
|
| 来源库 | Web of Science
|
| 引用统计 | |
| 成果类型 | 期刊论文 |
| 条目标识符 | http://kc.sustech.edu.cn/handle/2SGJ60CL/789427 |
| 专题 | 理学院_数学系 |
| 作者单位 | 1.Beijing Inst Technol, Sch Management & Econ, Beijing, Peoples R China 2.Southern Univ Sci & Technol, Dept Math, Shenzhen, Peoples R China 3.Univ Hong Kong, Dept Math, Adv Modeling & Appl Comp Lab, Pokfulam Rd, Hong Kong, Peoples R China 4.Hughes Hall,Wollaston Rd, Cambridge, England |
| 推荐引用方式 GB/T 7714 |
Guo, Sini,Gu, Jia-Wen,Ching, Wai-Ki,et al. Adaptive online mean-variance portfolio selection with transaction costs[J]. QUANTITATIVE FINANCE,2023,24(1).
|
| APA |
Guo, Sini,Gu, Jia-Wen,Ching, Wai-Ki,&Lyu, Benmeng.(2023).Adaptive online mean-variance portfolio selection with transaction costs.QUANTITATIVE FINANCE,24(1).
|
| MLA |
Guo, Sini,et al."Adaptive online mean-variance portfolio selection with transaction costs".QUANTITATIVE FINANCE 24.1(2023).
|
| 条目包含的文件 | 条目无相关文件。 | |||||
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