Trend representation based log-density regularization system for portfolio optimization. (April 2018)

• Record Type: Journal Article
• Authors: Yang, Pei-Yi; Lai, Zhao-Rong; Wu, Xiaotian; Fang, Liangda
• Is Part Of: Pattern recognition. Volume 76(2018:Apr.)
• Page Start: 14
• Page End: 24
• Publication Date: 2018-04
• Subjects: Trend representation -- Log-density regularization -- Ridge regression -- Portfolio optimization; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.10.024
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A novel trend representation is proposed for portfolio optimization. The system directly exploits time variable to represent price trend patterns. The system is robust to a small error of the price prediction. Experimental results show that the proposed system is effective and efficient. Abstract: Portfolio optimization (PO) has been catching more and more attention in the artificial intelligence and the machine learning communities. In this paper, we propose a novel Trend Representation based Log-density Regularization (TRLR) system for portfolio optimization. Its novelty falls into two aspects. First, it introduces a log-density regularization to the increasing factor of portfolio, which is seldom addressed by previous PO systems. It reflects a relationship between the portfolio and the price relative at an equilibrium point. Second, TRLR exploits a novel trend representation by taking the time variable as regressor in a weighted ridge regression, hence TRLR captures price trend patterns effectively. Extensive experiments conducted on 5 benchmark datasets from real-world financial markets demonstrate that TRLR achieves significantly better performance than other state-of-the-art strategies and runs fast, which shows its effectiveness and efficiency for large-scale applications.