Hcgm-Net: A Deep Unfolding Network For Financial Index Tracking

Tracking the performance of a financial index by selecting a subset of assets composing the index is a problem that raises several difficulties due to the large size of the stock market. Typically, optimisation algorithms with high complexity are employed to address such problems. In this paper, we focus on sparse index tracking and employ a Frank-Wolfe-based algorithm which we translate into a deep neural network, a strategy known as deep unfolding. Numerical experiments show that the learned model outperforms the iterative algorithm, leading to high accuracy at a low computational cost. To the best of our knowledge, this is the first deep unfolding design proposed for financial data processing.