Proceedings

Linear Trading Position with Sparse Spectrum

Download BibTeX Cite

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 5554-5562.

https://doi.org/10.24963/ijcai.2025/618

Abstract

The principal portfolio approach is an emerging method in signal-based trading. However, these principal portfolios may not be diversified to explore the key features of the prediction matrix or robust to different situations. To address this problem, we propose a novel linear trading position with sparse spectrum that can explore a larger spectral region of the prediction matrix. We also develop a Krasnosel'skii-Mann fixed-point algorithm to optimize this trading position, which possesses the descent property and achieves a linear convergence rate in the objective value. This is a new theoretical result for this type of algorithms. Extensive experiments show that the proposed method achieves good and robust performance in various situations.

Zhao-Rong Lai, and Haisheng Yang. 2025. Linear Trading Position with Sparse Spectrum. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 5554–5562. https://doi.org/10.24963/ijcai.2025/618
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards