An Efficient Decision Support System for Stock Market Prediction using Technical Indicators as Features by Machine Learning Approaches

In this Research, we have built Machine Learning Models to predict the next day price for the Nifty Index, Reliance and TCS Stocks. We will use R Project Library, quantmod, TTR for data acquisition and technical analysis. We have used the R Project Machine Learning Library caret along with the daily data for the last five years (June 2017 - June 2022) for the NIFTY Index and the Reliance and TCS Stocks. Our data will consist of Open, High, Low, Last (Close) and Volume. Our objective is to predict the price change for the next day using 6 technical indicators as features which include RSI, Williams%R, etc. For building Machine Learning Models, we have used the R Library caret. The primary methods which are used are KNN, Classification Regression Trees, Naive Bayes, SVM with Radial Basis Function and DNN. For k-nearest neighbors, we are able to achieve an accuracy of 72.78%, for DNN 72.533%, for CART 72.36%, for NB 70.08%, and for SVM 72.69% for the prediction accuracy of the next day.