Stock Price Forecast with Multi Layer Perceptron Artificial Neural Networks
Keywords:
Stock price forecast, Multi layer perceptron, Back propagation, Bibliometric analysis, Systematic reviewAbstract
This study maps and analyzes the academic literature regarding the stock price forecast with multi layer perceptron artificial neural networks, through bibliometric analysis and systematic review. The adoption of these methods requires the use of RStudio, VOSViewer and Rank Words software. In the bibliometric analysis, its main laws are verified [1,2,3]. As a result of the bibliometric analysis, the most frequent keywords are forecast, model(s), neural network and market, and most authors are associated with institutions located in China. Concerning the systematic review results, research on the different training methods, as well as the different data pre- and post-processing models are urgent, as they may reduce risks and maximize investors' returns. As for the directions for new research, further studies are suggested on the different models and architectures of multi-layer perceptron artificial neural networks, associations with other statistical and intelligent models, and research focusing on specific market segments, such as industry, energy and civil construction.
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