Retail Investor Prediction in India's Stock Market Using Complex-Value Spatio-Temporal Graph Convolutional Neural Network for Demographic Analysis and Risk Assessment

Retail investor participation in India's stock market has been increasing, driven by improved access to digital platforms and rising financial literacy. However, this surge leads to higher volatility and exposes investors to significant risks if they lack proper knowledge or experience in market dynamics. To overcome this problem, in this manuscript Retail Investor Prediction in India's Stock Market for Demographic Analysis and Risk Assessment (RIP-ISM-CVSTGCNN) is proposed. The model utilizes financial time-series data from the Tata Consultancy Services (TCS) stock dataset, integrated with investor demographic and behavioral attributes to enable comprehensive analysis. The data are pre-processed utilizing Implicit Unscented Particle Filter (IUPF), which is employed to clean the data. Then the pre-processed data are fed into a Complex-Value Spatio-Temporal Graph Convolutional Neural Network (CVSTGCNN) to forecast demographic factors along with a quantitative risk score that reflects the investor’s financial risk level. The proposed model is evaluated using multiple performance metrics, including precision, accuracy, sensitivity, specificity, F1-score, and AUC. Results show that the RIP-ISM-CVSTGCNN method achieves an accuracy of 97.3%, precision of 96.3%, sensitivity of 96.3%, specificity of 96.3%, F1-score of 96.6%, and an AUC of 0.97. Comparative analysis with existing methods, including Multi-Granularity Spatio-Temporal Correlation Networks for Stock Trend Prediction (MGSTCN-STP-GNN), Black Swan event-based hybrid model for Indian stock markets’ trends prediction (ISMT-1DCNN), and Series decomposition Transformer with period- correlation for stock market index prediction (SDTPC-SMI-RNN), demonstrates that the proposed model significantly outperforms all baseline approaches across all evaluation metrics. These outcomes confirm that the proposed framework provides a robust and effective solution for integrated demographic prediction and risk assessment, enabling improved financial decision-making in stock market analysis.