Multi-class stock market forecasting with deep learning models: an explainable artificial intelligence - Patel, Chhaya; Raiyani, Ashwin (Institute of Advanced Engineering and Science, 2025)
In this research, we investigated the influence of different deep learning techniques on time series stock market data, especially for all Nifty50 companies in the Indian stock market. Our proposed method of stock market prediction focused on multi-class classification with explainable artificial intelligence (XAI). Our proposed model incorporates convolutional neural network (CNN) for operational feature extraction and long short-term memory (LSTM) to capture time-based dependencies. Predicted value is classified with multiclass classes-very bullish, bullish, neutral, bearish, very bearish signals for all Nifty50 stocks. The model integrates essential technical indicators to find patterns from basic price data. XAI techniques are also used to find feature contributions to model prediction. It improves the clarity of the model’s administrative procedure by figuring out how technical indicators influence stock estimates. The outcomes highlight the model’s ability to generate actionable trading signals, reinforced by performance progress metrics, contributing to more well-informed and planned venture decisions. The proposed model reveals greater performance, reaching an average accuracy of 96%, beating LightGBM at 89%, random forest at 85%, and support vector machine at 60%.