A Study of Stock Market Volatility using an AI Diagnosis Model based on Holographic Multi-Relational Convolutional Graph Neural Network with Circulatory System-Based Optimization

Stock Market Volatility (SMV) study with an AI diagnosis model examines stock market movements with artificial intelligence to detect changes, forecast trends, and evaluate risks in order to make wiser financial decisions. Investors, analysts, and policymakers alike are confronted with stock market volatility because it is dynamic and difficult to predict. Most traditional models do not have the ability to represent dynamic interactions between financial instruments. To counter these challenges, this study suggested a Holographic Multi-Relational Convolutional Graph Neural Network with Circulatory System-Based Optimization (HMRCGN2Nets+CSBO) architecture. Inputs of data are taken from the Stock Market Volatility dataset. These data are preprocessed initially using A Reversible Automatic Selection Normalization (ARASN) method. Features are extracted using Efficient Inception Transformer (EIT). Future Prediction of the SMV with an AI Diagnosis Model is subjected to the Holographic Multi-Relational Convolutional Graph Neural Network (HMRCGN2Nets), again optimized using the Circulatory System-Based Optimization (CSBO). Stock Market Volatility dataset is used to determine how effective the proposed model HMRCGN2Nets+CSBO is, with a whopping accuracy of 99.9% and a 99.8% recall. The proposed method is implemented on the Python platform. The result of the suggested HMRCGN2Nets+CSBO model proved to be outstanding in forecasting stock market volatility. It successfully improved accuracy and recall over standard techniques and maximized decision-making and risk management techniques in financial markets through sophisticated data processing and predictive methods.