High-dimensional sparse index tracking based on a multi-step convex optimization approach-Reference-Cited by-同舟云学术

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High-dimensional sparse index tracking based on a multi-step convex optimization approach

Published:2023-08-02 Issue:9 Volume:23 Page:1361-1372
ISSN:1469-7688
Container-title:Quantitative Finance
language:en
Short-container-title:Quantitative Finance

Author:

Shi Fangquan¹,Shu Lianjie²,Luo Yiling³,Huo Xiaoming³

Affiliation:

1. School of Finance, Nanjing Audit University, Nanjing, People's Republic of China

2. Faculty of Business Administration, University of Macau, Taipa, People's Republic of China

3. School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA

Funder

National Natural Science Foundation of China

Department of Science and Technology of Guangdong Province

University of Macau Research Committee

Publisher

Informa UK Limited

Subject

General Economics, Econometrics and Finance,Finance

Link

https://www.tandfonline.com/doi/pdf/10.1080/14697688.2023.2236158

Reference41 articles.

1. Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors

2. Sparse Portfolios for High-Dimensional Financial Index Tracking

3. Discussion: One-step sparse estimates in nonconcave penalized likelihood models

4. Statistics for High-Dimensional Data

5. Enhancing Sparsity by Reweighted ℓ 1 Minimization

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. HIT: Solving Partial Index Tracking via Hierarchical Reinforcement Learning;2024 IEEE 40th International Conference on Data Engineering (ICDE);2024-05-13

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