Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions

Investment in a financial market is aimed at getting higher benefits; this complex market is influenced by a large number of events wherein the prediction of future market dynamics is challenging. The investors' etiquettes towards stock market may demand the need of studying various associated factors and extract the useful information for reliable forecasting. Fusion can be considered as an approach to integrate data or characteristics, in general, and enhance the prediction based on the combinational approach that can aid each other. We conduct a systematic approach to present a survey for the years 2011-2020 by considering articles that have used fusion techniques for various stock market applications and broadly categorize them into information fusion, feature fusion, and model fusion. The major applications of stock market include stock price and trend prediction, risk analysis and return forecasting, index prediction, as well as portfolio management. We also provide an infographic overview of fusion in stock market prediction and extend our survey for other finely addressed financial prediction problems. Based on our surveyed articles, we provide potential future directions and concluding remarks on the significance of applying fusion in stock market.

Automated summary

Investing in financial markets aims to gain higher benefits, but predicting market dynamics is challenging due to the complex nature of the market. Fusion techniques can integrate data and characteristics to enhance prediction accuracy in stock market applications. Major applications include stock price prediction, risk analysis, index prediction, and portfolio management.