Prediction of stock market indices has pinched considerable debate due to its brunt on economic development. Prediction of appropriate stock market indices is important in order to curtail the risk related with it in order to decide on effective investment schemes. Thus, selection of a proper forecasting model is highly appreciated. The objective of this paper is to efficiently normalize data in order to obtain accurate forecasting of stock movement and compare the results. A new technique called the hybrid normalization methodology for the efficient forecasting of stock movement has been implemented. This study discusses three normalization approaches along with our proposed normalization technique and their effect on the forecasting performance. In our work, we implemented Support Vector Machine (SVM), Artificial neural Network (ANN) and K-Nearest Neighbor (KNN) for stock trend forecasting as of their risk management capabilities. This article deals primarily with the normalization of input data for the estimation of stock movement. Simulation was performed on six stock indices (BSESN, NIFTY50, NASDAQ, HANG SENG, NIKKEI225 and SSE composite index) from different parts of the world market. The comparative study indicates that the hybrid normalization process is comparable to other normalization techniques. The results of the hybrid normalization process with ANN are found to be prominent as compared to other classifiers. The approximate accuracy obtained by using the hybrid normalization technique were 71%, 60% and 71% in the combination of KNN, SVM and ANN respectively.