Trend representation based log-density regularization system for portfolio optimization. (April 2018) - Yang, Pei-Yi; Lai, Zhao-Rong; Wu, Xiaotian; Fang, Liangda
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.