Quantitative Analysis: Trading Models Incorporating Concealed Components
Author(s): Dr. Antim PrasadAbstract
This study examines sophisticated trading models that include concealed elements to improve the accuracy of predictions and the management of risks in quantitative finance. This approach overcomes the constraints of conventional models by using latent variables that describe the hidden market dynamics that are not readily visible. The paper introduces a systematic approach for evaluating and using these undisclosed elements in trading strategies utilising advanced methods such as state-space models Bayesian inference and machine learning algorithms. Empirical evidence shows that models with hidden components greatly enhance predicting accuracy and resilience compared to standard methods. The results indicate that the inclusion of concealed variables may result in more efficient trading tactics and improved returns that account for risk.