Combined peak price tracking strategies for online portfolio selection based on the meta-algorithm

Abstract

Machine learning algorithms have been widely used to establish online portfolio selection strategies. Meta-algorithm, one of the machine learning algorithms, has the advantage of combining different base expert algorithm, which can greatly reduce the risk of choosing wrong experts, especially in the case of the base expert algorithm being very sensitive to the expert selection. In this paper, we investigate the online portfolio selection problem using Online Gradient Update (OGU) and Online Newton Update (ONU) meta-algorithms to combine expert advice for price prediction, in order to avoid the prediction bias caused by using a single expert that uses a fixed parameter. In addition, we theoretically analyze the price prediction algorithms’ regret bounds, which can guarantee that the prediction algorithms perform as well as the optimal combined prediction algorithm in hindsight. More importantly, we establish an online portfolio optimization model based on the price estimator, and obtain the portfolio update formula via Lagrange method. Finally, extensive numerical experiments are conducted to test the performance of the proposed strategies in several real-world stock markets. The empirical results show that our strategies are competitive and efficient in the diverse financial environment.