Literature review: Machine learning techniques applied to financial market prediction

• Published in: Expert systems with applications Vol. 124; pp. 226 - 251
• Main Authors: Henrique, Bruno Miranda, Sobreiro, Vinicius Amorim, Kimura, Herbert
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.06.2019; Elsevier BV
• Subjects: Artificial intelligence; Bibliographies; Financial time series prediction; Literature review; Literature reviews; Machine learning; Main path analysis; Market value; Mathematical models; Neural networks; Pattern recognition; Predictions; Pricing; Securities markets; Support vector machines; Texts; Literature review; Main path analysis; Financial time series prediction; Machine learning
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2019.01.012

•The search for models to predict the prices is still a highly researched topic.•The prices are financial time series that are difficult to predict.•The machine learning area applied to the prediction of financial market prices. The search for models to predict the prices of financial markets is still a highly researched topic, despite major related challenges. The prices of financial assets are non-linear, dynamic, and chaotic; thus, they are financial time series that are difficult to predict. Among the latest techniques, machine learning models are some of the most researched, given their capabilities for recognizing complex patterns in various applications. With the high productivity in the machine learning area applied to the prediction of financial market prices, objective methods are required for a consistent analysis of the most relevant bibliography on the subject. This article proposes the use of bibliographic survey techniques that highlight the most important texts for an area of research. Specifically, these techniques are applied to the literature about machine learning for predicting financial market values, resulting in a bibliographical review of the most important studies about this topic. Fifty-seven texts were reviewed, and a classification was proposed for markets, assets, methods, and variables. Among the main results, of particular note is the greater number of studies that use data from the North American market. The most commonly used models for prediction involve support vector machines (SVMs) and neural networks. It was concluded that the research theme is still relevant and that the use of data from developing markets is a research opportunity.