Factor Model

Factor models are statistical tools used to reduce the dimensionality of complex datasets by identifying underlying latent factors that explain the observed variables. Current research focuses on enhancing factor models using machine learning techniques, such as neural networks (including recurrent and convolutional architectures), variational autoencoders, and Kolmogorov-Arnold networks, to improve their ability to handle non-linear relationships, temporal dependencies, and high-dimensional data. These advancements are impacting diverse fields, including finance (asset pricing, risk management), economics (GDP nowcasting), and social sciences (social network analysis, behavior modeling), by providing more accurate predictions and deeper insights into complex systems. The integration of probabilistic methods further enhances the reliability and interpretability of these models, addressing limitations of purely deterministic approaches.