Iran’s Stock Market Prediction By Neural Networks and GA

Publish Year: 1387
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:

FJCFIS02_245

تاریخ نمایه سازی: 26 تیر 1392

Abstract:

Stock market prediction is one of the areas that had been very interesting for investors, economists and managers. For this purpose, classical and modernmethods such as AR and ARIMA models, Neural Networks, GA, Fuzzy Logic, etc, have been proposed but among them NNs play an essential role. In thispaper, the ability of three different neural networks, namely MLP, RBF and GRNN, are compared for stock market prediction. Unknown parameters of eachnetwork are optimized for minimum error by GA in training phase. Then trained networks are used for prediction of two and three monthly returns. Inaddition, for the first time in the literatures, the optimum order for each model, i.e. the number of input variables for each NN model is determined using trial and error.

Authors

Mahmood Khatibi

MS. in Control Engineering

Habib Rajabi Mashhadi

Associate Professor