关键词:

在线投资组合, 泛证券投资组合, 自适应矩估计, 梯度下降

Abstract:

For online portfolio selection problem, making full use of historical data can effectively reduce the impact of market noise on investment strategies, but it usually results in reduced their computational efficiency. Correspondingly, the increasing development of high-frequency trading and the explosive growth of data volume increasingly require investment strategies to have efficient computational ability. To this end, this paper proposes an online portfolio gradient descent strategy based on adaptive moment estimation, which makes use of historical data in an incremental manner with the help of adaptive moment estimation. The theoretical analysis shows that this strategy is universal, i.e., it has the same asymptotic average logarithmic growth rate as the offline best constant rebalanced portfolio. Meanwhile, this strategy still maintains linear time complexity while making full use of historical data. The empirical analysis shows that this strategy has a good performance in terms of the return and computational time metrics, and can sustain reasonable transaction costs. Therefore, it has good practical application prospects.

Key words:

online portfolio, universal portfolio, adaptive moment estimation, gradient descent