Stock trading is one of the businesses that has been done worldwide. In order to gain the maximum profit, accurate analysis is needed, so a trader can decide to buy and sell stock at the perfect time and price. Conventionally, two analyses are employed, namely fundamental and technical. Technical analysis is obtained based on historical data that is processed mathematically. Along with technology development, stock price analysis and prediction can be performed with the help of computational algorithms, such as machine learning. In this research, Artificial Neural Network simulations to produce accurate stock price predictions were carried out. Experiments are performed by using various input parameters, such as moving average filters, in order to produce the best accuracy. Simulations are completed with stock index datasets that represent three continents, i.e. NYA (America, USA), GDAXI (Europe, Germany), and JKSE (Asia, Indonesia). This work proposes a new method, which is the utilization of input parameters combinations of C, O, L, H, MA-5 of C, MA-5 of O, and the average of O C prices. Furthermore, this proposed scheme is also compared to previous work done by Khorram et al, where this new work shows more accurate results.