FORECASTING THE DATA OF NIFTY MIDCAP-50 BASED ON ARIMA MODEL USING JMP SOFTWARE

Abstract

This study explores the usage of the ARIMA (Auto Regressive Integrated Moving Average) approach for time series analysis and forecasting of stock trends in NIFTY Midcap 50 index. The NIFTY Midcap 50 index is the performance indicator of mid-cap companies in the Indian stock exchange which constitutes very important sector for investors. This research focuses on short- term prediction of price fluctuations by utilizing the historical stock data and applying ARIMA model, which is widely used for non-stationary time series data. The research methodology comprises a set of techniques focused on data preprocessing such as logarithmic transformation, differencing, and ACF (Autocorrelation Function) to enhance the accuracy of the model. The parameters of an ARIMA model were adjusted using the AIC with the aim of achieving the best results. The paper ends with stating that ARIMA is of high utility in price prediction of stocks but employing other forecasting models will enhance the reliability in the long run.