Passive Aggressive Ensemble for Online Portfolio Selection

Kailin Xie, Jianfei Yin, Hengyong Yu, Hong Fu () and Ying Chu ()
Additional contact information
Kailin Xie: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Jianfei Yin: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Hengyong Yu: Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA
Hong Fu: Department of Mathematics and Information Technology, The Education University of Hong Kong, Hong Kong, China
Ying Chu: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China

Mathematics, 2024, vol. 12, issue 7, 1-19

Abstract: Developing effective trend estimators is the main method to solve the online portfolio selection problem. Although the existing portfolio strategies have demonstrated good performance through the development of various trend estimators, it is still challenging to determine in advance which estimator will yield the maximum final cumulative wealth in online portfolio selection tasks. This paper studies an online ensemble approach for online portfolio selection by leveraging the strengths of multiple trend estimators. Specifically, a return-based loss function and a cross-entropy-based loss function are first designed to evaluate the adaptiveness of different trend estimators in a financial environment. On this basis, a passive aggressive ensemble model is proposed to weigh these trend estimators within a unit simplex according to their adaptiveness. Extensive experiments are conducted on benchmark datasets from various real-world stock markets to evaluate their performance. The results show that the proposed strategy achieves state-of-the-art performance, including efficiency and cumulative return.

Keywords: online portfolio selection; online ensemble learning; passive aggressive algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/7/956/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/7/956/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:7:p:956-:d:1362614

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().