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Applying a general hybrid intelligent system for ultra-high-frequency stock market forecasting

de Mattos Neto, Paulo S.G. ; Ferreira, Tiago A.E.

2016 International Joint Conference on Neural Networks (IJCNN), 2016, p.2104-2109

IEEE

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  • Título:
    Applying a general hybrid intelligent system for ultra-high-frequency stock market forecasting
  • Autor: de Mattos Neto, Paulo S.G. ; Ferreira, Tiago A.E.
  • Assuntos: Data models ; Forecasting ; Mathematical model ; Predictive models ; Stock markets ; Time series analysis ; Training
  • É parte de: 2016 International Joint Conference on Neural Networks (IJCNN), 2016, p.2104-2109
  • Descrição: The stock market is the most important institution for global investments all around the world. Among the possibles analysis, the study and forecasting of ultra-high-frequency time series is an interesting and great challenge to econometric modeling and statistical analysis due its complex behaviour. This work proposes a hybrid intelligent system to forecast ultra-high-frequency stock prices. The intelligent system is composed of a genetic algorithm (GA) that seeks the best parameters (the number of nodes in the input and hidden layers, and the training algorithm) of an artificial neural network (ANN) of type multiLayer perceptron (MLP). The proposed method called Time-delay Added Evolutionary Forecasting (TAEF) is a data-driven approach, that performs a pos-processing, where the objective is to reduce the difference between the forecasting and the actual series. The experimental study is performed using ultra-high frequency time series (Amazon, APPLE, Google and Intel stock prices) and shows that the proposed approach overcomes classical techniques of the computational intelligence and statistics in light of six relevant evaluation measures.
  • Editor: IEEE
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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