A Comparative Study of Future Stock Price Prediction Through Artificial Neural Network and ARIMA Modelling

Abstract

Stock price estimation and prediction have been the popular topics for ages for various market participants, including investors, traders, financial analysts and researchers. Understanding the expected direction of stock prices can provide insights into broader market trends and sentiments. Different methods have been tried to find out future predictions with the least degree of error. Autoregressive integrated moving average (ARIMA) and artificial neural networks (ANN) are two popular methods for time series forecasting, including stock price prediction. This paper attempts to forecast stock prices with more accuracy by using ARIMA and ANN tool modelling with secondary data NIFTY 50 of the National Stock Exchange from December 2005 to July 2019. Stock price prediction through ARIMA and ANN was calculated and compared. Results obtained revealed that the ANN method of forecasting stock prices has more accuracy as compared to ARIMA modelling. Future studies can be based on different sector-based data across different sectors and stock markets.