A Robust Analysis and Forecasting Framework for the Indian Mid Cap Sector Using Times Series Decomposition Approach

Jaydip Sen

Abstract


Prediction of stock prices using econometrics and machine learning based approaches poses significant challenges to the research community since the movement of stock prices are essentially random in its nature. However, significant development and rapid evolution of sophisticated and complex algorithms which are capable of analyzing large volume of time series data, coupled with availability of high-performance hardware and parallel computing architecture over the last decade, has made it possible to efficiently process and effectively analyze voluminous stock market time series data in an almost real-time environment. In this paper, we propose a decomposition-based approach for time series analysis of the Indian mid cap sector and also present a highly robust and accurate prediction framework consisting of six forecasting methods for predicting the future values of the time series. Extensive results are presented on the performance of each forecasting method and the reasons why a particular method has performed better than the others have been critically analyzed.   


Full Text:

PDF

References


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. Operations Research in Emerging Economics, 30(6), 901– 923. DOI: 10.1016/S0305-0548(02)00037-0

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, China, pp. 1646–1650. DOI: 10.1109/ICNNB.2005.1614946

Coghlan, A. (2015). A Little Book of R for Time Series, Release 02. Available at: Link (Accessed on: August 30, 2017)

de Faria, E. L., Albuquerque, M. P., Gonzalez, J. L., 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. DOI: 10.1016/j.eswa.2009.04.032

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(3), 283-295. DOI: 10.1177/097265270600500305

Hamid, S. A., Iqbal, Z. (2004). Using neural networks for forecasting volatility of S&P 500 index futures prices. Journal of Business Research, 57(10), 1116–1125. DOI: 10.1016/S0148-2963(03)00043-2

Hammad, A. A. A., Ali, S. M. A. & Hall, E. L. (2007). Forecasting the Jordanian stock price using artificial neural network. Intelligent Engineering Systems through Artificial Neural Networks, Vol 17, Digital Collection of The American Society of Mechanical Engineers. DOI: 10.1115/1.802655.paper42

Hanias, M., Curtis, P. & Thalassinos, J. (2007). Prediction with neural networks: the Athens stock exchange price indicator. European Journal of Economics, Finance and Administrative Sciences, 9, 21–27. Available at: Link (Accessed on: August 30, 2017)

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. DOI: 10.3386/w4718

Ihaka, R. & Gentleman, R. (1996). A language for data analysis and graphics. Journal of Computational and Graphical Statistics, 5(3), 299 – 314. DOI: 10.2307/1390807

Jaruszewicz, M. & Mandziuk, J. (2004). One day prediction of NIKKEI index considering information from other stock markets. Proceedings of the International Conference on Artificial Intelligence and Soft Computing, 3070, 1130–1135. DOI: 10.1007/978-3-540-24844-6_177

Kim, K.-J. (2004). Artificial neural networks with feature transformation based on domain knowledge for the prediction of stock index futures. Intelligent Systems in Accounting, Finance & Management, 12(3), 167–176. DOI: 10.1002/isaf.252

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, San Diego, pp. 1 – 16, CA, USA, . DOI: 10.1109/IJCNN.1990.137535

Leigh, W., Hightower, R. and 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), 1-8. DOI: 10.1016/j.eswa.2004.08.001

Leung, M. T., Daouk, H. & Chen, A.-S. (2000). Forecasting stock indices: a comparison of classification and level estimation models. International Journal of Forecasting, 16(2), 173–190. DOI: 10.1016/S0169-2070(99)00048-5

Moshiri, S. & Cameron, N. (2010). Neural network versus econometric models in forecasting inflation. Journal of Forecasting, 19(3), 201-217. DOI: 10.1002/(SICI)1099-131X(200004)19:3<201::AID-FOR753>3.0.CO;2-4

Mostafa, M. (2010). Forecasting stock exchange movements using neural networks: empirical evidence from Kuwait. Expert Systems with Application, 37(9), 6302-6309. DOI: 10.1016/j.eswa.2010.02.091

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, Vol 5551, pp. 870–876 Springer-Verlag, Heidelberg, Germany. DOI: 10.1007/978-3-642-01507-6_98

Pan, H., Tilakaratne, C. & Yearwood, J. (2005). Predicting the Australian stock market index using neural networks exploiting dynamical swings and intermarket influences. Journal of Research and Practice in Information Technology, 37(1), 43–55. DOI: 10.1007/978-3-540-89378-3_53

Perez-Rodriguez, J. V., Torra, S. & Andrada-Felix, J. (2005). Star and ANN models: forecasting performance on the Spanish IBEX-35 stock index. Journal of Empirical Finance, 12(3), 490–509. DOI: 10.1016/j.jempfin.2004.03.001

Sen J. & Datta Chaudhuri, T. (2016a). 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), pp. 15 – 28, Kolkata, India, January, 2016. DOI: 10.13140/RG.2.1.3232.0241.

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), 1-20. DOI: 10.13140/RG.2.1.2178.3448.

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 Economic Library, 3(2), 303-326. DOI: 10.1453/jel.v3i2.787.

Sen, J. & Datta Chaudhuri, T. (2016d). 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. Journal of Insurance and Financial Management, 1(4), 68-132. Available at: Link (Accessed on: August 30, 2017)

Sen, J. & Datta Chaudhuri, T. (2016e). Decomposition of time series data to check consistency between fund style and actual fund composition of mutual funds. Proceedings of the 4th International Conference on Business Analytics and Intelligence (ICBAI 2016), Bangalore, India, December 19-21. DOI: 10.13140/RG.2.2.14152.93443.

Sen, J. & Datta Chaudhuri, T. (2017a). A time series analysis-based forecasting framework for the Indian healthcare sector. Journal of Insurance and Financial Management, 3(1), 66 - 94. Available at: Link (Accessed on: August 30, 2017)

Sen, J. & Datta Chaudhuri, T. (2017b). A predictive analysis of the Indian FMCG sector using time series decomposition-based approach. Journal of Economic Library, 4(2), 206 - 226. DOI: 10.1453/jel.v4i2.1282.

Sen, J. (2017a). A time series analysis-based forecasting approach for the Indian realty sector. International Journal of Applied Economic Studies, 5(4), 8 - 27. Available at: Link (Accessed on: August 30, 2017)

Sen, J. (2017b). A study of the Indian metal sector using time series decomposition-based approach. Book Chapter No 8 in: Analysis and Forecasting of Financial Time Series Using R: Models and Applications, Sen, J. & Datta Chaudhuri, T., pp. 223- 255, August 2017, Scholars' Press, Germany. ISBN: 978-3-330-65386-3.

Shen, J., Fan, H. & Chang, S. (2017e). Stock index prediction based on adaptive training and pruning algorithm. Advances in Neural Networks, Lecture Notes in Computer Science, Vol 4492, pp. 457–464, Springer-Verlag, Heidelberg, Germany. DOI: 10.1007/978-3-540-72393-6_55

Thenmozhi, M. (2006). Forecasting stock index numbers using neural networks. Delhi Business Review, 7(2), 59-69. Available online at: Link (Accessed on: August 30, 2017)

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, pp. 755 – 765, Hong Kong, March 2009. Available at: Link (Accessed on: August 30, 2017)

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. Available at: Link (Accessed on: August 30, 2017)

Wang, W. & Nie, S. (2008). The performance of several combining forecasts for stock index. International Seminar on Future Information Technology and Management Engineering, pp. 450– 455, Leicestershire, United Kingdom, November 2008. DOI: 10.1109/FITME.2008.42

Wu, Q., Chen, Y. & Liu, Z. (2008). Ensemble model of intelligent paradigms for stock market forecasting. Proceedings of the 1stInternational Workshop on Knowledge Discovery and Data Mining, pp. 205 – 208, Washington, DC, USA, January 2008. DOI: 10.1109/WKDD.2008.54

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 with Applications, 34(4), 3043–3054. DOI: 10.1016/j.eswa.2


Refbacks

  • There are currently no refbacks.


Copyright (c) 2017 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.