Forecasting Stock prices of banks using artificial neural networks (GMDH) and response surface methodology
Publish Year: 1398
Type: Conference paper
Language: English
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FAMA02_386
Index date: 12 February 2020
Forecasting Stock prices of banks using artificial neural networks (GMDH) and response surface methodology abstract
According to the importance of shares and investment forecasts for those in the capital market and insufficient analysis of financial information regardless of other variables and parameters as well as the economic data, behavioral and social sciences od time series which are effective in banks stock price, investors are encouraged toward additional tools and available soft wares. In this study modeling and forecasting stock prices of banks using artificial neural networks (GMDH) and response surface methodology (RSM) is accomplished. Obtained results showed that performance of the models in processing simultaneous effects of turnover, value of transactions and number of transactions on bank stock price is excellent.
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Forecasting Stock prices of banks using artificial neural networks (GMDH) and response surface methodology authors
Erfan Mohammadi
Master of Accounting, Shahid Beheshti University