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

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 (IT) and Capital Goods (CG) for the period January 2009 – April 2016 and have studied the relationships of these two time series with the time series of DJIA indices, NIFTY indices and the US Dollar to Indian Rupees exchange rate. We established by graphical and statistical tests that while the IT sector of India has a strong association with DJIA indices and the Dollar to Rupee exchange rate, the Indian CG sector exhibits a strong association with the NIFTY indices. 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 is the reflection of India’s internal economic growth. 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.    


Full Text:

PDF

References


Basalto, N., Bellotti, R., De Carlo, F., Facchi, P., & Pascazio, S. (2005). Clustering stock market companies via chaotic map synchronization. Physica A, 345, 196-206.

Bentes, S. R., Menezes, R., & Mendes, D. A. (2008). Long memory and volatility clustering: is the empirical evidence consistent across stock markets? Physica A: Statistical Mechanics and its Applications, 387(15), 3826-3830.

Chen, A.-S., Leung, M. T. & Daouk, H. (2003). Application of neural networks to an emerging financial market: forecasting and trading the Taiwan stock index. Computers & Operations Research, 30(6), 901– 923.

Chen, Y., Dong, X. & Zhao, Y. (2005). Stock index modeling using EDA based local linear wavelet neural network. Proceedings of International Conference on Neural Networks and Brain, Beijing, Vol 3, 1646–1650.

Coghlan, A. (2015). A Little Book of R for Time Series, Release 02.

Available online: https://media.readthedocs.org/pdf/a-little-book-of-r-for-time-series/latest/a-little-book-of-r-for-time-series.pdf. (last accessed on: May 9, 2016).

de Faria, E. L, Albuquerque, M. P., Gonzalez, J., Cavalcante, J. T. P. & Albuquerque, M. P. (2009). Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing methods. Expert Systems with Applications, 36(10), 12506 – 12509.

Dutta, G., Jha, P., Laha, A. & Mohan, N. (2006). Artificial neural network models for forecasting stock price index in the Bombay stock exchange. Journal of Emerging Market Finance, 5, 283-295.

Fu, T-C, Chung, F-L., Luk, R., & Ng, C-M, (2008). Representing financial time series based on data point importance. Engineering Applications of Artificial Intelligence, 21(2), 277-300.

Hammad, A. A., Ali, S. M. & Hall, E. L. (2007). Forecasting the Jordanian stock price using artificial neural network. Intelligent Engineering Systems through Artificial Neural Networks, ASME Press, USA.

Hanias, M., Curtis, P. & Thalassinos, J. (2012). Times series prediction with neural networks for the Athens stock exchange indicator. European Research Studies, 15(2), 23 – 32.

Hornik, K. (1989). Multilayer feedforward networks are universal approximators. Neural Networks, 2(5), 359-366.

Hutchinson, J. M., Lo, A. W., & Poggio, T. (1994). A nonparametric approach to pricing and hedging derivative securities via learning networks. Journal of Finance, 49(3), 851-889.

Ihaka, R. & Gentleman, R. (1996). A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3), 299-314.

Kimoto, T., Asakawa, K., Yoda, M. & Takeoka, M. (1990). Stock market prediction system with modular neural networks. Proceedings of the IEEE International Conference on Neural Networks, 1-16.

Leigh, W., Hightower, R. & Modani, N. (2005). Forecasting the New York stock exchange composite index with past price and interest rate on condition of volume spike. Expert Systems with Applications, 28, 1-8.

Liao, S-H., Ho, H-H., Lin, H-W. (2008). Mining stock category association and cluster on Taiwan stock market. Expert System with Applications, 35(2008), 19-29.

Moshiri, S. & Cameron, N. (2010). Neural network versus econometric models in forecasting inflation. Journal of Forecasting, 19, 201-217.

Mostafa, M. (2010). Forecasting stock exchange movements using neural networks: empirical evidence from Kuwait. Expert Systems with Application, 37, 6302-6309.

Ning, B., Wu, J., Peng, H. & Zhao, J. (2009). Using chaotic neural network to forecast stock index. Advances in Neural Networks, Lecture Notes in Computer Science (LNCS), 5551, 870–876.

Phua, P. K. H., Ming, D., & Lin, W. (2000). Neural network with genetic algorithms for stocks prediction. 5th Conference of the Association of Asian-Pacific Operations Research Societies, Singapore.

Roh, T. H. (2007). Forecasting the volatility of stock price index. Expert Systems with Applications, 33(4), 916-922.

Sen J. & Datta Chaudhuri, T. (2016 a). Decomposition of time series data of stock markets and its implications for prediction – an application for the Indian auto sector. Proceedings of the 2nd National Conference on Advances in Business Research and Management Practices (ABRMP’16), Kolkata, India, January 8 -9, 2016.

Available online at: https://arxiv.org/abs/1601.02407 (last accessed on: May 31, 2016)

Sen, J. & Datta Chaudhuri, T. (2016b). A framework for predictive analysis of stock market indices – a study of the Indian auto sector. Calcutta Business School (CBS) Journal of Management Practices, 2(2), 1 – 19.

Available online: https://arxiv.org/abs/1604.04044 (last accessed on May 31, 2016)

Sen, J. & Datta Chaudhuri, T. (2016c). An alternative framework for time series decomposition and forecasting and its relevance for portfolio choice: a comparative study of the Indian consumer durable and small cap sectors. Journal of Economics Library, 3(2). Accepted for publication.

Available online: https://arxiv.org/abs/1605.03930 (last accessed on May 31, 2016)

Shen, J., Fan, H. & Chang, S. (2007). Stock index prediction based on adaptive training and pruning algorithm. Advances in Neural Networks, Lecture Notes in Computer Science, Vol 4492, 457–464.

Shumway, R. H. & Stoffer. D. S. (2011). Time Series Analysis and Its Applications. Springer-Verlag, New York, USA.

Thenmozhi, M. (2006). Forecasting stock index numbers using neural networks. Delhi Business Review, 7(2), 59-69.

Tsai, C.-F. & Wang, S.-P. (2009). Stock price forecasting by hybrid machine learning techniques. Proceedings of International Multi Conference of Engineers and Computer Scientists, 1.

Tseng, K-C., Kwon, O., & Tjung, L. C. (2012). Time series and neural network forecast of daily stock prices. Investment Management and Financial Innovations, 9(1), 32-54.

Wang, W. & Nie, S. (2008). The performance of several combining forecasts for stock index. International Seminar on Future Information Technology and Management Engineering, Leicestershire, UK, 450– 455.

Wu, Q., Chen, Y. & Liu, Q. Z. (2008). Ensemble model of intelligent paradigms for stock market forecasting. Proceedings of the IEEE 1st International Workshop on Knowledge Discovery and Data Mining, 205 – 208, Washington, DC, USA.

Zhang, D., Jiang, Q., & Li, X. (2007). Application of neural networks in financial data mining. International Journal of Computer, Electrical, Automation, and Information Engineering, 1(1), 225-228, World Academy of Science, Engineering and Technology.

Zhu, X., Wang, H., Xu, L. & Li, H. (2008). Predicting stock index increments by neural networks: the role of trading volume under different horizons. Expert Systems Applications, 34(4), 3043–3054.


Refbacks



Copyright (c) 2016 Jaydip Sen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.