A Predictive Analysis of the Indian FMCG Sector using Time Series Decomposition - Based Approach
Abstract. Stock price movements being random in its nature, prediction of stock prices using time series analysis presents a very difficult and challenging problem to the research community. However, over the last decade, due to rapid development and evolution of sophisticated algorithms for complex statistical analysis of large volume of time series data, and availability of high-performance hardware and parallel computing architecture, it has become possible to efficiently process and effectively analyze voluminous and highly diverse stock market time series data effectively, in real-time. Robust predictive models are being built for accurate forecasting of values of highly random variables such as stock price movements. This paper has presented a highly reliable and accurate forecasting framework for predicting the time series index values of the fast moving consumer goods (FMCG) sector in India. A time series decomposition approach is followed to understand the behavior of the FMCG sector time series for the period January 2010 till December 2016. Based on the structural analysis of the time series, six methods of forecast are designed. These methods are applied to predict the time series index values for the months of 2016. Extensive results are presented to demonstrate the effectiveness ofthe proposed decomposition approaches of time series and the efficiency of the six forecasting methods.
Keywords. Time series decomposition, Trend, Seasonal, Random, Holt Winters Forecasting model, Auto Regression (AR), Moving Average (MA), Auto Regressive Integrated Moving Average (ARIMA), Partial Auto Correlation Function (PACF), Auto Correlation Function (ACF).
JEL. G11, G14, G17, C63.
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. Accessed on: May 10, 2017. [Retrieved from].
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
Doganis, P., Alexandridis, A., Patrinos, P., & Sarimveis, H. (2006). Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing. Journal of Food Engineering, 75(2), 196-204. doi. 10.1016/j.jfoodeng.2005.03.056
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.
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
Kunc, M. (2005). Illustrating the competitive dynamics of an industry: the fast-moving consumer goods industry case study. Proceedings of the 23rd International Conference of the System Dynamics Society, pp. 1-42, Boston, MA, USA, July, 2005.
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), 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
Lewandowska, J. (2012). Identification losses in the FMCG1 sector in the light of the European and global researchers. Proceedings of FIKUSZ’12 Symposium for Young Researchers, pp. 101-110, Budapest, Hungary, November 2012.
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
Rakicevic, Z., & Vujosevic, M. (2015). Focus forecasting in supply chain: the case study of fast moving consumer goods company in Serbia. Serbian Journal of Management, 10(1), 3-17. doi. 10.5937/sjm10-7075
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.
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
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, pp. 457–464, Springer-Verlag, Heidelberg, Germany. doi. 10.1007/978-3-540-72393-6_55
Singhi, A., Jain, N. & Puri, N. (2015). Re-Imagining FMCG in India. CII National Summit Report, December 2015, pp, 1-68. Accessed on: May 10, 2017. [Retrieved from].
Thenmozhi, M. (2006). Forecasting stock index numbers using neural networks. Delhi Business Review, 7(2), 59-69. Accessed on: May 10, 2017. [Retrieved from].
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.
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.
Vayvay, O., Dogan, O., & Ozel, S. (2013). Forecasting techniques in fast moving consumer goods supply chain: a model proposal. International Journal of Information Technology and Business Management, 16(1), 118 – 128.
Vriens, A., & Versteijnen, E. (2007). Forecasting & Planning in the Food Industry. EyeOn White Paper. Accessed on: May 10, 2017. [Retrieved from].
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.2007.06.023
- There are currently no refbacks.
Journal of Economics Library - J. Econ. Lib. - JEL - www.kspjournals.org
Copyright © KSP Library