Comparison of neural networks and Auto regression performance in prediction of Dividend and Price Index

This paper present two models for short and long term stock price forecasting using Artificial Neural Networks and Vector Auto regression analyses as complementary methods. Presented models also will be able to forecast the stock price of future day. The models forecast the stock price of future day with using of previous stock price information. In presented models, it is possible to use the calendar parameters, like the day of week, special days and etc to forecast the stock price. A major contribution of this work is the resulting time-delayed artificial neural network model that allows stock return predictions and is particularly useful as an investment decision support system for hedge funds and other investors, whose portfolios are at risk of losing market value.
After designing mentioned models, and specifying construction of multi-layer perception neural network, designed network is trained using available data. Presented method for training the perception neural network is, Error back propagation algorithm, which has variable learning rate and momentum factor, and also is fast. Finally, the models have been applied on Tehran-stock Exchange Price Index (TEPIX) and Tehran- stock Exchange-Dividend and Price Index (TEDPIX) and result are compared together and advantages and disadvantages of these models are described. The results show validity of presented models.