Application of Time Series Models in Forecasting Automobile Sectors Volatility for Selected Period

Abstract

The Bombay Stock Exchange is extensive and fully regulated trading system in India. Exploration and forecasting of stock market time series data have developed considerable interest from the researchers over the last decade. Time Series modelling techniques perform pivotal role in prediction of data for future demands. Volatility forecasting has become crucial for investors, policy holders, retailers since bringing preciseness in estimating the future is very difficult. Automotive sector has gone through severe crash in their operations in last decade mostly due to policy changes, policy paralysis and confusion among retailers about several new changes to be brought by the authority. This paper suggested a review on some of the most crucial works gives a meticulous view of recent machine learning (ML) techniques in the quantitative share price prediction showing that these are the methods transcend some traditional approaches. This paper using time series analysis found out the volatility forecasting using machine learning and by applying volatility forecasting model ARIMA. The present study focuses on analyzing the suitability of ARIMA model for forecasting share prices of four major companies of automobile sectors Hero Motor Corp, Ashok Leyland, TVS Motors, Eicher Motors. The data collection was done on monthly basis for the period 11th August, 2014 to 16th August,2019 from the website of Bombay stock exchange.

Keywords: Automobile sector, ARIMA modelling, volatility forecasting, machine learning, volatility estimators, data analysis, BSE, time series analysis.