Abstract

This paper explores advanced trading models that incorporate hidden components to enhance predictive accuracy and risk management in quantitative finance. It addresses the limitations of traditional models by integrating latent variables that capture underlying market dynamics not directly observable. The study presents a framework for estimating and utilizing these hidden factors within trading strategies employing techniques such as state-space models Bayesian inference and machine learning algorithms. Empirical results demonstrate that models with hidden components significantly improve forecasting performance and robustness compared to conventional approaches. The findings suggest that incorporating hidden variables can lead to more effective trading strategies and better risk-adjusted returns.