Linear Trading Position with Sparse Spectrum

TL;DR

This paper introduces a novel linear trading position method utilizing sparse spectrum analysis, optimized via a fixed-point algorithm, demonstrating robust performance across diverse trading scenarios.

Contribution

The paper presents a new spectral-based trading position approach and a fixed-point optimization algorithm with proven linear convergence, enhancing diversification and robustness.

Findings

Achieves robust performance in various market conditions.

Demonstrates linear convergence of the optimization algorithm.

Explores a larger spectral region for improved trading signals.

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'ski\u \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.