Machine learning techniques and data for stock market forecasting: A literature review
Literature review on machine learning techniques for stock market prediction, analyzing data, methods, and performance metrics.
What it examines
This survey investigates machine learning techniques for stock market prediction, focusing on data sources, variables, and methods used. It examines 138 journal articles from 2000 to 2019, providing a comprehensive review of the data and machine learning techniques applied in stock market forecasting.
What it concludes
The study concludes that deep learning techniques are increasingly popular in stock market predictions. Potential applications include improved financial forecasting models. Future research should focus on integrating more diverse data sources and refining machine learning techniques for better accuracy.
Evidence objects
The study concludes that deep learning techniques are increasingly popular in stock market predictions. Potential applications include improved financial forecasting models. Future research should focus on integrating more diverse data sources and refining machine learning techniques for better accuracy.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
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.
Source row: 1268 · abstract type: unknown