Publication Details
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Ruben Alan J Pauwels, Evangelia Tsiligianni, Nikos Deligiannis
 

IEEE International Conference on Acoustics, Speech and Signal Processing

Contribution To Book Anthology

Abstract 

Tracking the performance of a financial index by selecting asubset of assets composing the index is a problem that raisesseveral difficulties due to the large size of the stock market.Typically, optimisation algorithms with high complexity areemployed 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. This strategy, known as deep unfolding, leads to a learned model with high accuracy at a low computational cost. To the bestof our knowledge, this is the first deep unfolding design pro-posed for financial data processing. Numerical experimentsdemonstrate the superior performance of our approach.

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