Linear Trading Position with Sparse Spectrum
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 5554-5562.
https://doi.org/10.24963/ijcai.2025/618
Keywords
- Constraint Satisfaction and Optimization: CSO: Solvers and tools
- Machine Learning: ML: Optimization
- Multidisciplinary Topics and Applications: MTA: Finance
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.
@inproceedings{ijcai2025p618,
title = {Linear Trading Position with Sparse Spectrum},
author = {Lai, Zhao-Rong and Yang, Haisheng},
booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
Artificial Intelligence, {IJCAI-25}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {James Kwok},
pages = {5554--5562},
year = {2025},
month = {8},
note = {Main Track},
doi = {10.24963/ijcai.2025/618},
url = {https://doi.org/10.24963/ijcai.2025/618},
}
TY - CPAPER TI - Linear Trading Position with Sparse Spectrum AU - Lai, Zhao-Rong AU - Yang, Haisheng T2 - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2025 SP - 5554 EP - 5562 DO - 10.24963/ijcai.2025/618 UR - https://doi.org/10.24963/ijcai.2025/618 ER -Copied
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