Transparent Stock Predictor - Leveraging Explainable AI and Sentiment Analysis for Real-Time Stock Market Prediction: A Novel Framework for Financial Transparency

Abstract

Forecasting the stock market has also been an enduring problem with its sentiment-driven, unstable nature based upon historical price action and unpredictable external influences such as news and social media opinion. Traditional models are in large part based upon quantitative data, bypassing the most important contributor to stock action in contemporaneous popular opinion. The “black box” nature of deep learning models provides another layer of difficulty in achieving trust-building along with stakeholders' adoption, with stakeholders requiring accuracy along with traceable causes for forecasted systems. In response to such problems, this work proposes an integrated solution that combines real- time sentiment analysis with time series prediction. It uses sentiment scores from VADER, which is a light-weight lexicon-based model, in conjunction with historical stock prices. It used a Gated Recurrent Unit (GRU) neural network due to it being efficient with smaller parameter requirements compared to LSTM. It further incorporated LIME (Local Interpretable Model- agnostic Explanations), which made the model more transparent by determining the contributing features to predictions. It uses Python and React in designing the system and Flask as the backend API. It was tested with standard prediction metrics like Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Adjusted R², and Mean Absolute Percentage Error (MAPE). Out of numerous deep learning models that were benchmarked—the LSTM, CNN, RNN, and MLP—the best performance was achieved by the GRU model, with an MAE of 1.3362 and an R² score of 0.9961. These validate that sentiment analysis with deep learning is a highly effective combination, with the addition of explainability via LIME meaning that not only are the model’s predictions accurate, but also easily understandable.