Combining expert weights for online portfolio selection based on the gradient descent algorithm

摘要

In this paper, we propose a new online portfolio selection strategy based on a weighted learning technique and an online gradient descent algorithm. Our strategy, named combination weights based on online gradient descent (CW-OGD), achieves improved robustness by integrating different expert strategies and overcomes the difficult problem of complex computational time. First, an expert system including many basic expert strategies, in which we choose the strategy that invests in a single stock as the basic expert strategy, is established. Second, we exploit the loss function to evaluate the performance of different basic expert strategies and use the OGD algorithm to update the weight vector for the experts based on their losses. In addition, we theoretically prove that the proposed strategy has a regret bound. Finally, extensive experiments conducted on four stock combinations and seven benchmark datasets show that our strategy can outperform some state-of-the-art strategies in terms of the return, risk and computational time metrics. Furthermore, our strategy can achieve higher returns even at certain transaction cost rates, which illustrates its effectiveness in the actual stock market.