Investigating Performance of Bayesian and Levenberg-Marquardt Neural Network in Comparison Classical Models in Stock Price Forecasting

Accurate forecasting of stock prices according to high volatility and inherent risk of stock market is a major concern of investors and financial analysts, hence applying novel approaches to predict the stock priceisan inevitable necessity. Accordingly, the purpose of this research is to compare the performance of forecasting models such as neural network with classical model and introducing appropriate model to forecast tomorrow stock price. The daily market prices data and financial indicator have been used as input variables for designing neural network model and daily closing price data set as input variable for designing ARIMA and also tomorrow's closing price is considered as output variable from 2011to2014. The results show that the Bayesian neural network represents less error sand higher Predictive power than the ARIMA model. The findings indicate the efficiency of Bayesian neural network incapture short-term investment opportunities and also can help investors to choose the appropriate portfolio and to obtain more returns.