Importance of Data Standardization Methods on Stock Indices Prediction Accuracy

Abstract

Stock market indices prediction has drawn huge attention due to its impact on economic stability. Accurate stock market indices prediction is highly essential to reduce the risk associated with it so as to decide good investment strategies. To acknowledge exact prediction, different strategies have been attempted, amid which the machine learning techniques have pinched consideration and been refined achieving extraordinary results in applying machine learning approaches. In our study, we have adopted Support Vector Machine (SVM) for stock market forecasting due to its capacity to deal with risk. SVM in forecasting requires some preliminary works on the data and one of them is standardization. In this study, we analyze four normalization techniques and their influence on the forecasting results. The investigation demonstrates high affectability of the regularly utilized strategies to input information standardization calculations and shows the requirement for a wary way to deal with the outcomes achieved by them.