Integrating Explainable AI, Ensemble Technique and Sentiment Analysis for Stock Market Forecasting

Abstract

Financial forecasting that is both accurate and comprehensible, is crucial for stock market risk management. Many of the innovative approaches that have surfaced in recent years make use of deep learning, ensemble methods, and ensemble techniques that combine many approaches on financial data. Although these techniques frequently produce impressive results, they frequently function as "black boxes," making it challenging for people to examine how predictions are made because of a lack of transparency that breeds mistrust. This review examines studies that combine a variety of models. It makes things more readable and trustworthy by utilizing XAI, deep learning, and machine learning. LSTM, BiLSTM, CNN, XGBoost, ARIMA, and Prophet are a few of these. These are used to identify important trends in financial data and to monitor patterns over time. In addition to SHAP, LIME, and Layer-wise Relevance Propagation, there are more XAI technologies that can be useful. It displays the factors that assist models in making predictions. These justifications encourage more individuals to have faith in the outcome. The review discusses major issues, such as making models easy to comprehend, how well these models hold up in new marketplaces, and leveraging people's emotions when forecasting.