Dictionary Learning-Based Denoising for Portfolio Selection

In the finance industry, real-world data are affected by noise, which comes from several external sources. This makes it challenging to select optimal portfolios for profitable investment strategies. Therefore, noise removal (or denoising) has become important for investors to create accurate investment models that guarantee better returns. In this paper, we propose a novel dictionary learning-based denoising approach for financial time series. The transform matrix in dictionary learning is built by training the noisy data with a K-singular value decomposition (K-SVD) algorithm. We evaluated the effectiveness of the proposed method using the 30 Fama French portfolio (FF30) as sample data. Furthermore, the out-of-sample performance of the denoising approach is tested under a minimum-variance framework. Empirical results prove that the proposed dictionary learning-based denoising method outperforms the other benchmarks in terms of portfolio selection.