Design of experiments on neural network's parameters optimization for time series forecasting in stock markets

Mu Yen Chen, Min Hsuan Fan, Young Long Chen, Hui Mei Wei

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


Artificial neural network (ANN) model has been used for years to conduct research in stock price prediction for three reasons. First, it has a higher prediction accuracy rate in empirical research. Second, it is not subject to the assumption of having samples from a normal distribution. Third, it can deal with non-linear problems. Nevertheless, the accuracy of prediction relies on the parameter settings of neural network as well as the complexities of problems and the neural network architecture; the results of the analysis could be even more significant with the selection of optimal parameters and network architecture. Currently, as a way of setting parameters, most researchers employed the trial and error method. However, this method is very time-consuming and labor-intensive and may not result in the optimal parameters. Therefore, this research took advantage of a back propagation neural network (BPNN) for the purpose of parameter optimization through constructing a model of stock price prediction, applying design of experiment (DOE) to systematize experiment scheduling, and methods of main effects analysis and interaction analysis. The research used two datasets of financial ratios from 50 blue chip companies in Taiwanese stock market and 40 listed American banks in New York stock exchange as experimental samples. Research results showed that the correlation forecasting, root mean squared error (RMSE), and computing time, which can effectively increase the accuracy of stock price prediction, are better than traditional statistical methods and conventional neural network model.

Original languageEnglish
Pages (from-to)369-393
Number of pages25
JournalNeural Network World
Issue number4
Publication statusPublished - 2013

All Science Journal Classification (ASJC) codes

  • Software
  • General Neuroscience
  • Hardware and Architecture
  • Artificial Intelligence


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