Review of Professional Management
issue front

Kahkashan Khan1 and Dileep Singh1

First Published 7 Jul 2022.
Article Information Volume 20, Issue 1 June 2022
Corresponding Author:

Kahkashan Khan, Humanities and Management Science Department, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh 273010, India.

1 Humanities and Management Science Department, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ( which permits non-Commercial use, reproduction and distribution of the work without further permission provided the original work is attributed.


Forecasting of stock prices is a very important subject in the financial world and economics. For many years, investors have been interested in making better forecasting models. The autoregressive integrated moving average (ARIMA) model was used previously for time series forecasting. This article shows the process of stock price forecasting using an ARIMA model. Historical stock data for analysis is obtained from the National Stock Exchange (NSE) and is used along with the stock price for forecasting using an ARIMA model. The result obtained from an ARIMA model is better for short-term forecasting and can be proven with existing methods for stock price prediction.


ARIMA model, stock price prediction, stock market, short-term prediction


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