Machine learning techniques and data for stock market forecasting: A literature review

In this literature review, we investigate machine learning techniques that are applied for stock market prediction. A focus area in this literature review is the stock markets investigated in the literature as well as the types of variables used as input in the machine learning techniques used for predicting these markets. We examined 138 journal articles published between 2000 and 2019. The main contributions of this review are: (1) an extensive examination of the data, in particular, the markets and stock indices covered in the predictions, as well as the 2173 unique variables used for stock market predictions, including technical indicators, macro-economic variables, and fundamental indicators, and (2) an in-depth review of the machine learning techniques and their variants deployed for the predictions. In addition, we provide a bibliometric analysis of these journal articles, highlighting the most influential works and articles. •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.