An Investigation of the Structural Characteristics of the Indian IT Sector and the Capital Goods Sector: An Application of the R Programming in Time Series Decomposition and Forecasting

TL;DR

This study analyzes the relationships between Indian IT and Capital Goods sectors and global indices using R-based time series decomposition and forecasting, revealing sector-specific economic linkages and demonstrating effective predictive models.

Contribution

It applies R programming for time series analysis of Indian sectors, uncovering sector-specific economic relationships and developing accurate forecasting models.

Findings

IT sector strongly linked to DJIA and USD/INR exchange rate

Capital Goods sector strongly linked to NIFTY index

Forecasting models show very low error rates

Abstract

Time series analysis and forecasting of stock market prices has been a very active area of research over the last two decades. Availability of extremely fast and parallel architecture of computing and sophisticated algorithms has made it possible to extract, store, process and analyze high volume stock market time series data very efficiently. In this paper, we have used time series data of the two sectors of the Indian economy: Information Technology and Capital Goods for the period January 2009 till April 2016 and have studied the relationships of these two time series with the time series of DJIA index, NIFTY index and the US Dollar to Indian Rupee exchange rate. We establish by graphical and statistical tests that while the IT sector of India has a strong association with DJIA index and the Dollar to Rupee exchange rate, the Indian CG sector exhibits a strong association with the NIFTY index. We contend that these observations corroborate our hypotheses that the Indian IT sector is strongly coupled with the world economy whereas the CG sector of India reflects internal economic growth of India. We also present several models of regression between the time series which exhibit strong association among them. The effectiveness of these models have been demonstrated by very low values of their forecasting errors.