Decision Fusion for Stock Market Prediction: A Systematic Review

Abstract

Stock market prediction based on machine or deep learning is an essential topic in the financial community. Typically, models with different structures or initializations provide different forecasts of the same response variable. In such cases, better prediction is often achieved by combining forecasts from multiple models rather than using a single model in isolation. This combination of forecasts from the base learners is known as decision fusion. Furthermore, although decision fusion is typical and essential for making the best possible use of multiple forecasts, few studies have systematically summarized the studies that apply this technique. Therefore, there is an urgent need for a literature review reflecting the application of decision fusion in this field. To this end, this study systematically reviewed research related to decision fusion for stock market prediction, focusing on the characteristics of base learners and decision fusion methods. Specifically, the research trend on this topic, which has shifted over the past two decades, is discussed. This review also presents future directions in applying decision fusion to stock market prediction, such as the fusion of forecasts with different data types, using new algorithms as base learners, and integrating sentiment analysis with decision fusion techniques.