Linear Trading Position with Sparse Spectrum
This work addresses diversification and robustness issues in trading strategies for financial markets, but it appears incremental as it builds on existing principal portfolio methods.
The paper tackles the problem of principal portfolios in signal-based trading lacking diversification and robustness by proposing a linear trading position with sparse spectrum to explore a larger spectral region of the prediction matrix, achieving good and robust performance in experiments.
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 ı-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.