Explainable AI for Improved Financial Decision Support in Trading

In order to improve financial decision support in trading situations, this study presents a strong framework that uses Explainable Artificial Intelligence (XAI) methodologies. The suggested model incorporates essential components, such as a Contrastive Divergence Neural Network for classification, Least Absolute Shrinkage and Selection Operator(LASSO) Regression for feature selection, and min-max normalization for dataset preparation. By using min-max normalization throughout the dataset preparation phase, we hope to guarantee data homogeneity and scalability. Important for reducing biases, standardizing input characteristics, and improving model convergence in later stages, this phase is crucial. One approach to feature selection is to use LASSO Regression, which is well-known for its capacity to isolate and keep the most important characteristics while eliminating the less useful ones. To maximize predicted accuracy and interpretability, the following model is trained on a refined set of variables. An effective unsupervised learning approach, the Contrastive Divergence Neural Network is used by the central classification component. The algorithm is able to spot nuanced trends and correlations that conventional methods miss because of this neural network's exceptional pattern-capturing capabilities in financial data.