# Linear Trading Position with Sparse Spectrum

arXiv: https://arxiv.org/abs/2508.18596 · HTML: https://mlsanity.com/paper/2508.18596/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 26 Aug 2025.

## Builds on (most cited first, 6 of 6)

- [Canonical Tensor Decomposition for Knowledge Base Completion](https://mlsanity.com/paper/1806.07297/) (2018)
- [Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient Situations](https://mlsanity.com/paper/2003.12237/) (2020)
- [Handling Missing Data with Graph Representation Learning](https://mlsanity.com/paper/2010.16418/) (2020)
- [A Globally Optimal Portfolio for m-Sparse Sharpe Ratio Maximization](https://mlsanity.com/paper/2410.21100/) (2024)
- [A De-singularity Subgradient Approach for the Extended Weber Location Problem](https://mlsanity.com/paper/2405.06965/) (2024)
- [Autonomous Sparse Mean-CVaR Portfolio Optimization](https://mlsanity.com/paper/2405.08047/) (2024)

## Abstract (arXiv)

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'skiu i-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.