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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

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  • Jaydip Sen
  • Tamal Datta Chaudhuri

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.

Suggested Citation

  • Jaydip Sen & Tamal Datta Chaudhuri, 2017. "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," Papers 1706.07821, arXiv.org.
    • Handle: RePEc:arx:papers:1706.07821
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    References listed on IDEAS

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    1. Basalto, N. & Bellotti, R. & De Carlo, F. & Facchi, P. & Pascazio, S., 2005. "Clustering stock market companies via chaotic map synchronization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 345(1), pages 196-206.
    2. Jaydip SEN & Tamal DATTA CHAUDHURI, 2016. "An Alternative Framework for Time Series Decomposition and Forecastingand its Relevance for Portfolio Choice – A Comparative Study of the Indian Consumer Durable and Small Cap Sectors," Journal of Economics Library, KSP Journals, vol. 3(2), pages 303-326, June.
    3. Goutam Dutta & Pankaj Jha & Arnab Kumar Laha & Neeraj Mohan, 2006. "Artificial Neural Network Models for Forecasting Stock Price Index in the Bombay Stock Exchange," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 5(3), pages 283-295, December.
    4. Jaydip Sen & Tamal Datta Chaudhuri, 2016. "Decomposition of Time Series Data of Stock Markets and its Implications for Prediction: An Application for the Indian Auto Sector," Papers 1601.02407, arXiv.org.
    5. Bentes, Sónia R. & Menezes, Rui & Mendes, Diana A., 2008. "Long memory and volatility clustering: Is the empirical evidence consistent across stock markets?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3826-3830.
    6. M. Hanias & P. Curtis & E. Thalassinos, 2012. "Time Series Prediction with Neural Networks for the Athens Stock Exchange Indicator," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 23-32.
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