2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMLSP-41.5
Paper Title HCGM-NET: A DEEP UNFOLDING NETWORK FOR FINANCIAL INDEX TRACKING
Authors Ruben Pauwels, VRIJE UNIVERSITEIT BRUSSELS, Belgium; Evaggelia Tsiligianni, University of Ioannina, Greece; Nikos Deligiannis, VRIJE UNIVERSITEIT BRUSSELS, Belgium
SessionMLSP-41: Deep Learning Optimization
LocationGather.Town
Session Time:Friday, 11 June, 11:30 - 12:15
Presentation Time:Friday, 11 June, 11:30 - 12:15
Presentation Poster
Topic Machine Learning for Signal Processing: [MLR-DEEP] Deep learning techniques
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract 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.