A composite trend representation-based tracking system with historical portfolio data for portfolio optimization

Article type: Research Article

Authors: Guo, Rui

Affiliations: College of Information Science and Technology, Jinan University, Guangzhou, Guangdong 510000, China | E-mail: [email protected]

Correspondence: [*] Corresponding author: College of Information Science and Technology, Jinan University, Guangzhou, Guangdong 510000, China. E-mail: [email protected].

Abstract: In this paper, we propose a novel tracking system based on composite trend representation and historical portfolio data (CTRHP) for portfolio optimization (PO). In the part of obtaining prediction of price, we introduce the important data of historical portfolio, which is rarely utilized before, to improve the accuracy of measuring investment performance. In addition, we propose a set of correlation coefficient-based similarity measurement functions (CSMFs) to automatically assign different weights to different trend representations, which enables each trend representation to have an impact on future price predictions and set the strength according to their investment performance. In the part of portfolio optimization, a new optimization objective based on generalized increasing factors is proposed to optimize the investment portfolio, and a fast solution algorithm is presented. Extensive experiments on six standard datasets from real financial markets across different assets and different time horizons show that our CTRHP achieves significantly better performance compared with previous state-of-the-art PO systems in investing returns and risk control. Moreover, it has the advantages of being able to tolerate certain transaction fees and running fast, which shows that it is suitable for real financial environments.

Keywords: Correlation coefficient, historical portfolio, portfolio optimization (PO), composite trend representation

DOI: 10.3233/JCM-226638

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 2, pp. 1021-1042, 2023