🤖 AI Summary
High-dimensional features with strong correlations often lead to overparameterization, degrading predictive performance, interpretability, and numerical stability of supervised learning—particularly in Chinese financial news–driven stock return forecasting. To address this, we propose a factor-augmented feature engineering method grounded in factor modeling and principal component analysis (PCA): it separately decomposes the design matrix and its nonlinear transformations to jointly extract shared latent factors and idiosyncratic residuals, which are then combined into augmented features. This approach bridges data augmentation and model architecture modification, offering structural simplicity and computational efficiency. Extensive experiments across diverse real-world datasets—including Chinese financial news text—demonstrate substantial improvements in prediction accuracy and robustness across multiple supervised algorithms, especially under small-sample and high-noise regimes. Our work fills a critical methodological gap by introducing factor-driven feature engineering for NLP-based financial forecasting.
📝 Abstract
High-dimensional measurements are often correlated which motivates their approximation by factor models. This holds also true when features are engineered via low-dimensional interactions or kernel tricks. This often results in over parametrization and requires a fast dimensionality reduction. We propose a simple technique to enhance the performance of supervised learning algorithms by augmenting features with factors extracted from design matrices and their transformations. This is implemented by using the factors and idiosyncratic residuals which significantly weaken the correlations between input variables and hence increase the interpretability of learning algorithms and numerical stability. Extensive experiments on various algorithms and real-world data in diverse fields are carried out, among which we put special emphasis on the stock return prediction problem with Chinese financial news data due to the increasing interest in NLP problems in financial studies. We verify the capability of the proposed feature augmentation approach to boost overall prediction performance with the same algorithm. The approach bridges a gap in research that has been overlooked in previous studies, which focus either on collecting additional data or constructing more powerful algorithms, whereas our method lies in between these two directions using a simple PCA augmentation.