Abstract:
Neural networks (NNs), as artificial intelligence methods, have become very important in making stock market predictions. Much research on the applications of NNs for solving business problems have proven their advantages over statistical and other methods that do not include Al, although there is no optimal methodology for a certain problem. The system has been trained with the Standard & Poor (S&P) 500 composite indexes of past twelfth years. It can be concluded from analysis that NNs are most implemented in forecasting stock prices, returns, and stock modeling, and the most frequent methodology is the Backpropagation algorithm. Inspite of many benefits, there are limitations that should be investigated.
Stocks are commonly predicted on the basis of daily data, although some researchers use weekly and monthly data.
Additionally, future research should focus on the examinations
of other types of networks that were rarely applied, such as Hopfiled's, Kohonen's, etc. This data prediction can be used in
weather forecasting also. End user for this data prediction, either the stockbroker or else who wants to predict the future record, based on the past data, but the key to all applications though, is how we present and enhance data, and working through parameter selection by trial and error
