Computational Intelligence Techniques Used for Stock Market Prediction: A Systematic Review

With the advancement of various computational techniques and the growing search for assertive predictive models, computational intelligence methods have attracted much attention. They are data-based methodologies and mainly include fuzzy logic, artificial neural networks and evolutionary computation. In the economic environment, more specifically, in the stock market forecast, where there is the challenge of the time series volatility, these methods have stood out. In this context, the objective of this paper is to present a systematic review of the literature on recent research involving forecasting techniques in the stock market, and the computational intelligence were the ones that stood out. To define these techniques, articles were collected from four large databases and a keyword filter was applied, which reduced the initial volume. So we selected the articles from the most published journals and remove duplicated articles. The most articles applied hybrid models and for the selection of featured techniques were choose those most frequent ones. A brief description was also made of the most used methods as well as of the selected articles. The review was done with articles published between the years 2014 and 2018 taken from four databases and, after some selection criteria, 24 articles were selected by relation to the subject studied.