A decade systematic review of fusion techniques in financial market prediction

Financial markets are structured systems that facilitate investment, trading, and economic growth at both global and local levels. Accurate and reliable prediction in these markets is essential for making profitable decisions. However, the inherent complexity of financial markets, influenced by numerous external factors, makes it difficult to perform analysis and forecasting. In this context, fusion techniques have emerged as a strong and well-known approach, integrating data and features from multiple sources to enhance prediction accuracy. This paper presents a systematic review of studies on fusion techniques in financial market prediction, covering research published between 2016 and 2025. The primary objective is to provide a comprehensive understanding of the role of fusion techniques in financial market prediction from both macro and micro perspectives. To achieve this, we categorize fusion techniques based on level of integration, analyzing their benefits, limitations, and applications. Additionally, we discuss the necessity of fusion approaches, open challenges, and potential future advancements in this domain. Our review emphasizes the growing adoption of multimodal text data fusion using large language models (LLMs) as a promising trend to enhance prediction reliability. Also, we identify key research directions and emerging trends that are expected to shape the future development of fusion-based financial market prediction methods.