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, macroeconomic 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.

Automated summary

This literature review explores the application of machine learning techniques in stock market prediction. It focuses on the stock markets investigated in the literature and the types of variables used as input in machine learning techniques for predicting these markets. The review includes an examination of 138 journal articles published between 2000 and 2019 and provides extensive insights into the data and machine learning techniques used for stock market prediction.