 Application and performance of data mining techniques in stock market: A reviewJasleen Kaur and Khushdeep Dharni Intelligent Systems in Accounting, Finance and Management, 2022, vol. 29, issue 4, 219-241 Abstract: 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. Date: 2022 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:isacfm:v:29:y:2022:i:4:p:219-241 Ordering information: This journal article can be ordered from Access Statistics for this article More articles in Intelligent Systems in Accounting, Finance and Management from John Wiley & Sons, Ltd. |
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