Surveying Explainable Deep Learning in Stock Forecasting: Trends and Open Challenges

Because nancial, economic, political, and social elements are ever-changing, predicting future share market movements is a dicult endeavor. Conventional statistical approaches often suer to handle the complex and dynamic patterns and temporal dynamics typical of nancial data. Lately, Deep learning is gaining more and more attention because it's capable of uncover meaningful insights from vast and diverse datasets. This review work oers an in-depth examination of deep-learning applications in stock market forecasting, exploring both unimodal and multimodal strategies. We classify these models by their data types, architectural frameworks, integration techniques, and learning goals. Moreover, we address existing challenges, discuss widelyused datasets and evaluation metrics, and nd the main ideas that need more attention in future research on making nancial prediction systems more reliable, understandable, and suitable for real-time use.