Application and performance of data mining techniques in stock market: A review

• Published in: Intelligent systems in accounting, finance & management finance & management, 2022-10, Vol.29 (4), p.219-241
• Main Authors: Kaur, Jasleen, Dharni, Khushdeep
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Neural Network (ANN); Data mining; Data Mining Techniques; Forecasting; Phosphates; Securities markets; Stock exchanges; Stock market forecasting; Support vector machines (SVM)
• ISSN: 1550-1949; 2160-0074
• DOI: 10.1002/isaf.1518

Summary Prediction and the stock market go hand in hand. Due to the inherent limitations of traditional forecasting methods and the pursuit to uncover the hidden patterns in stock market data, stock market prediction using data mining techniques has caught the fancy of academicians, researchers, and investors. Based on a systematic review of more than 143 research studies spanning 25 years, the present paper brings to light the major issues concerning forecasting of stock markets based on data mining techniques, such as usage of data mining techniques in the stock market, input data types, single versus hybrid techniques, instruments and stock markets researched, types of software and algorithms used, measures of forecast accuracy, and performance of various data mining techniques. Emerging patterns related to various dimensions have been critically analyzed by highlighting the existing limitations and suggesting future research paradigms. This analysis can be useful for academicians, researchers and investors looking for futuristic directions in a given research domain.