Integrating Heterogeneous Data for Stock Market Prediction: A Systematic Literature Review

This systematic literature review examines recent developments in stock market prediction using heterogeneous data sources that combine technical indicators, fundamental attributes, and sentiment-driven signals. Despite the growing adoption of machine learning in financial forecasting, existing research remains fragmented across data modalities, fusion strategies, and evaluation protocols, limiting comparability and practical applicability. Studies published between 2018 and 2024 were retrieved from five major scholarly databases and screened based on predefined eligibility criteria, resulting in 44 peer-reviewed articles included in the final analysis. The review synthesizes the quantitative and qualitative data modalities employed, the machine learning and deep learning methodologies adopted, the evaluation metrics used to assess predictive performance, and the principal challenges associated with multi-source stock market prediction. Findings reveal a clear shift toward deep learning architectures, hybrid fusion techniques, and the integration of external information such as news, corporate disclosures, and social media sentiment. Despite this progress, the literature exhibits inconsistent evaluation practices, limited attention to temporal data leakage, and insufficient coverage of non-English and emerging markets. This review consolidates current knowledge, presents a structured taxonomy of heterogeneous data sources and fusion strategies, and identifies open research challenges to guide future work in multimodal stock market prediction.