---
title: "Linear Trading Position with Sparse Spectrum"
authors:
  - "Zhao-Rong Lai"
  - "Haisheng Yang"
doi: "10.24963/ijcai.2025/618"
url: "https://ijcai.org/proceedings/2025/618"
pdf: "https://ijcai.org/proceedings/2025/0618.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "5554-5562"
track: "Main Track"
keywords:
  - "Constraint Satisfaction and Optimization: CSO: Solvers and tools"
  - "Machine Learning: ML: Optimization"
  - "Multidisciplinary Topics and Applications: MTA: Finance"
---

# Linear Trading Position with Sparse Spectrum

Zhao-Rong Lai, Haisheng Yang

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 5554-5562. DOI: [10.24963/ijcai.2025/618](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.