Literature review: Machine learning techniques applied to financial market prediction
| Autor: | Herbert Kimura, Vinicius Amorim Sobreiro, Bruno Miranda Henrique |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Artificial neural network business.industry Computer science Financial market General Engineering Subject (documents) 02 engineering and technology Machine learning computer.software_genre Computer Science Applications Support vector machine Survey methodology 020901 industrial engineering & automation Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Emerging markets computer Theme (narrative) |
| Zdroj: | Expert Systems with Applications. 124:226-251 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2019.01.012 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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