This research uses Monte Carlo simulation to increase the accuracy of neural network prediction on a limited number of composite stock price index. The case study is Indonesian composite stock price index (i.e., Jakarta Composite Index (JCI)) from July 1997 to December 2007. Monte Carlo simulation is used to generate additional data from the available data, which is then fed into neural network to forecast future data. Testing results show that the output of hybrid neural network-Monte Carlo simulation system produces significantly lower Mean Absolute Percentage Error (MAPE) than the output of neural network without data from Monte Carlo simulation.

Original languageEnglish
Pages (from-to)1-7
Number of pages7
JournalAsian Journal of Information Technology
Issue number1
Publication statusPublished - 2009


  • Composite stock price index
  • Jakarta composite index
  • Monte Carlo simulation
  • Neural network
  • Prediction


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