Explainable deep learning model for stock price forecasting using textual analysis

Abstract

Stock price forecasting is a challenging task because financial time series are primarily nonlinear, noisy, and disordered systems that are complicated to forecast. Deep learning models show promise in this domain along with natural language processing, to extract relevant features from text data and map them to numerical representations. This study aims to forecast stock prices using text analysis and deep learning approaches and explain the models using explainable AI. We construct a World Halal Tourism Composite Sentiment Index (WHTCSI) using text analysis to forecast halal tourism stock price. The results suggest that Convolutional Neural Networks (CNN) outperform all other models. The results are robust when considering country-level data. In addition, model explanations show that the index contributes 35.55% to the forecasting model, indicating irrational investment activity and herding behavior in the halal tourism industry. The study’s findings have significant implications for investors, analysts, and portfolio managers in making investment decisions.