Stock price is basically sensitive, non-stationary and very noisy. Many environmental factors are the important variables in stock price change, especially in emerging markets. To forecast stock price, this study proposed the developed integration artificial neural networks (ANNs) using the Wavelet De-nosing-based Back propogation (WDBP) neural network. The main purpose of the wavelet de-composition is to classify the basic elements from the noise of the signal. The used data in this experiment were the monthly closing prices of Stock Exchange of Thailand (SET) index during January 2001 to April 2014. To show the improved integration of using WDBP method, this paper applied three accurate measures to evaluate the forecasting performance. Following this paper methodology, the investors could be guided in investment providing deviation and direction of stock indexes and maximization profits in the emerging stock market.
