Machine learning techniques and data for stock market forecasting: A literature review
作者：
Highlights：
• A systematic review of 138 related journal articles published during 2000–2019.
• North American market covered most, especially the S&P500 index, followed by Asia.
• Technical Indicators (e.g., return, simple moving average, RSI) common predictors.
• Neural networks and support vector machines are frequently used algorithms.
• Growing use of deep learning methods and textual data in recent research articles.
摘要
•A systematic review of 138 related journal articles published during 2000–2019.•North American market covered most, especially the S&P500 index, followed by Asia.•Technical Indicators (e.g., return, simple moving average, RSI) common predictors.•Neural networks and support vector machines are frequently used algorithms.•Growing use of deep learning methods and textual data in recent research articles.
论文关键词：Classification,Data mining,Financial market,Predictive performance,Regression,Stock market prediction
论文评审过程：Received 28 April 2021, Revised 9 December 2021, Accepted 5 February 2022, Available online 19 February 2022, Version of Record 24 February 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116659
