e Ahmadi - Department of Industrial Engineering, Yazd University, Yazd, Iran
m.h Abooie - Department of Industrial Engineering, Yazd University, Yazd, Iran
m Jasemi - Department of Industrial Engineering, Khajeh Nasir Toosi University of Technology, Tehran, Iran
y Zare Mehrjardi - Department of Industrial Engineering, Yazd University, Yazd, Iran

In this paper, the nonlinear autoregressive model with exogenous variables as a new neural network is used for timing of the stock markets on the basis of the technical analysis of Japanese Candlestick. In this model, the nonlinear autoregressive model with exogenous variables is an analyzer. For a more reliable comparison, here (like the literature) two approaches of Raw-based and Signal-based are devised to generate the input data of the model. The correct predictions percentages for periods of 1- 6days with the total number of buy and sell signals are considered. The result proves that to some extent the approaches have similar performances while apparently, they are superior to a feed-forward static neural network. The created network is evaluated by the measure of Mean of Squared Error and the proposed model accuracy is calculated to be extremely high

Finance,Stock Market Forecasting,Technical Analysis,NARX Recurrent Neural Network,Levenberg–marquardt Algorithm