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  • Volume 2017

    Selection of Best ARIMA Modeling Approach for Forecasting Time Series Patterns; A Case Study on Colombo Stock Exchange
    (International Journal of Business, Economics and Management Works)

    Vol. 4, Issue 11, PP. 1-5, November 2017
    DOI
    Keywords: Modeling, Stock exchange, ARIMA

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    Abstract

    Stock market indexes provide a yardstick with which investors can compare the performance of their individual stock portfolios. The propose of this paper is to examine  a suitable model for forecasting stock prices under the volatility in the Colombo Stock Exchange (CSE), Sri Lanka.Since the data has a non-seasonal linear trend, an autoregressive integrated moving average model has used for modeling and forecasting. The results suggested that ARIMA model is more suitable for forecasting ASPI index under the volatility.

    Author

    1. Madushani M.L.P*: Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya, Sri Lanka.
    2. Erandi M.W.A*: Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya, Sri Lanka.
    3. Madurangi L.H.L.S*: Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya, Sri Lanka.
    4. Sivaraj L.B.M*: Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya, Sri Lanka.
    5. Weerasinghe W.D .D*: Department of Statistics & Computer Science, Faculty of Science, University of Kelaniya, Sri Lanka.
    6. R.M KapilaTharangaRathnayaka: Faculty of Sciences, Sabaragamuwa University of Sri Lanka,Sri Lanka, kapila.tr@gmail.com

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    Cite

    Madushani M.L.P, Erandi M.W.A, Madurangi L.H.L.S, Sivaraj L.B.M, Weerasinghe W.D .D, R.M KapilaTharangaRathnayaka, "Selection of Best ARIMA Modeling Approach for Forecasting Time Series Patterns; A Case Study on Colombo Stock Exchange" International Journal of Business, Economics and Managment Works, Vol. 4, Issue 11, PP. 1-5, November 2017.

    References

    1. [1]     Akuffo, B., & Ampaw, E. M. (2013). An Autoregressive Integrated Moving Average (ARIMA) Model For Ghanas Inflation(1985 - 2011). Mathematical Theory and Modeling, 3, 213 - 216. Retrieved 2016
    2. [2]     RM Kapila Tharanga Rathnayaka, DMKN Seneviratna, Wei Jianguo, “Grey system based novel forecasting and portfolio mechanism on CSE”, Grey Systems: Theory and Application, 6(2), 126-142, 2016, Emerald Group Publishing Limited; http://dx.doi.org/10.1108/GS-02-2016-0004
    3. [3]     Ho, S. L., & XIE, M. (1998). THE USE OF ARIMA MODELS FOR RELIABILITY FORECASTING AND ANALYSIS. 23rd International Conference on Computers and Industrial Engineering (pp. 213 - 216). Great Britain: Elsevier Science Ltd.
    4. [4]     RM Kapila Tharanga Rathnayaka, DMKN Seneviratna, Wei Jianguo, “Grey system based novel approach for stock market forecasting”, Grey Systems: Theory and Application, 5(2), 178-193, 2015, Emerald Group Publishing Limited ; http://dx.doi.org/10.1108/GS-04-2015-0014
    5. [5]     Arumawadu, H.I., Rathnayaka, R.M.K.T. & Seneviratna, D.M.K.N., “New Proposed Mobile Telecommunication Customer Call Center Roster Scheduling Under the Graph Coloring Approach” , International Journal of Computer Applications Technology and Research, 5(4), pp.234–237, 2016, www.ijcat.com.
    6. [6]     Hasitha Indika Arumawadu, RM Kapila Tharanga Rathnayaka, SK Illangarathne, “K-Means Clustering For Segment Web Search Results”, International Journal of Engineering Works, 2(8), 79-83, 2015, kwpublisher.com.
    7. [7]     R.M. Kapila Tharanga  Rathnayaka and D.M.K.N Seneviratne, “G M (1, 1) Analysis and Forecasting for Efficient Energy Production and Consumption”, International Journal of Business, Economics and Managment works, Kambohwell Publisher Enterprises, 1 (1), 6-11, 2014, www.kwpublisher.com 
    8. [8]     Hasitha Indika Arumawadu, RM Kapila Tharanga Rathnayaka, SK Illangarathne, “Mining Profitability of Telecommunication Customers Using K-Means Clustering”, Journal of Data Analysis and Information Processing, 3(3), 63, 2015, DOI: 10.4236/jdaip.2015.3300.
    9. [9]     R.M. Kapila Tharanga  Rathnayaka and D.M.K.N Seneviratne, “A Comparative Analysis of Stock Price Behaviors on the Colombo and Nigeria Stock Exchanges”, International Journal of Business, Economics and Managment works, Kambohwell Publisher Enterprises, 2 (2), 12-16, 2014, www.kwpublisher.com .
    10. [10]  Jayathileke, P. M. B., and Rathnayaka, R.M. K. T.  “Testing the Link between Inflation and Economic Growth: Evidence from Asia”, Modern Economy,4, 87, 2013, www.scirp.org .
    11. [11]  R.M Kapila Tharanga  Rathnayaka, D.M. Kumudu Nadeeshani Seneviratne and Zhong- jun Wang, “An Investigation of Statistical Behaviors of the Stock Market Fluctuations in the Colombo Stock Market: ARMA & PCA Approach”, Journal of Scientific Research & Reports 3(1): 130-138, 2013; Article no. JSRR, www.sciencedomain.org
    12. [12]  R.M. Kapila Tharanga  Rathnayaka and Zhong-jun Wang, “Enhanced Greedy Optimization Algorithm with Data Warehousing for Automated Nurse Scheduling System”, E-Health Telecommunication Systems and Networks ,1(4), 2012, www.SciRP.org/journal/etsn
    13. [13]  Khashei, M., & Bijari, M. (2011). A novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing, 2664 - 2675.
    14. [14]  R.M. Kapila Tharanga  Rathnayaka, D.M.K.N Seneviratne, Wei Jianguo and Hasitha Indika Arumawadu, “A   Hybrid Statistical Approach for Stock Market Forecasting Based on Artificial   Neural Network and ARIMA Time Series Models”, The 2nd International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC’2015-IEEE), Nanjing, China, 2015.  http://besc2015.njue.edu.cn
    15. [15]  R.M. Kapila Tharanga  Rathnayaka, Wei Jianguo and D.M.K.N Seneviratne, “Geometric Brownian Motion with Ito lemma Approach to evaluate market fluctuations: A case study on Colombo Stock Exchange”, 2014 International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC’2014- IEEE), Shanghai, China, 2014. http://datamining.it.uts.edu.au/conferences/besc14
    16. [16]  R.M. Kapila Tharanga Rathnayaka, D.M.K.N. Seneviratna, Wei Jianguo and Hasitha Indika Arumawadu, “An unbiased GM(1,1)-based new hybrid approach for time series forecasting”, Grey Systems: Theory and Application, 6(3), 322-340, 2016, Emerald Group Publishing Limited; http://dx.doi.org/10.1108/GS-04-2016-0009.
    17. [17]  Konarasinghe, S. W., Abenayake, R. N., & HGunaratne, P. L. (2015). ARIMA Models on Forecasting Sri Lankan Share Market Returns. International Journal of Novel Research in Physics Chemistry & Mathematics, 6-12.
    18. [18]  D.M.K.N. Seneviratna and Mao Shuhua, “Forecasting the Twelve Month Treasury Bill Rates in Sri Lanka: Box Jenkins Approach ”, IOSR Journal of Economics and Finance (IOSR-JEF), 1(1), 42-47, 2013.
    19. [19]  McGough, T., & Tsolacos, S. (1995). Forecasting commercial rental values using ARIMA models. Journal of Property Valuation & Investment, 6-22.
    20. [20]  Morawakage, S. P., & Nimal, D. P. (2015). Equity Market Volatility Behavior in Sri Lankan Context. Kelaniya Journal of Management, 4(2).
    21. [21]  Donglin Chen, Dissanayaka M. K. N. Seneviratna, “Using Feed Forward BPNN for Forecasting All Share Price Index”, Journal of Data Analysis and Information Processing, 2(1), 87-94, 2014.
    22. [22]  Samarakoon, L. P. (1996). STOCK MARKET RETURNS AND INFLATION:Sri Lankan Evidence. Sri Lankan Journal of Management, 1(4), 293-311.
    23. [23]  Samayawardena, D. N., Dharmarathne, H. A., & Tilakaratne, C. D. (November 2015). Volatility Models for World Stock Indices and Behavior of All Share Price Index. Proceedings of 8th International Research Conference,KDU, (pp. 175-182).
    24. [24]  Stevenson, S. (2007). A comparison of the forecasting ability of ARIMA models. Journal of Property Investment & Finance, 223-578.
    25. [25]  Tsay, R. S. (2005). Analysis of Financial Time Series (2nd ed.). United States of America: Wiley - Interscience.
    26. [26]  Wickremasinghe, G. (2011). The Sri Lankan stock market and the macroeconomy: an empirical investigation. Studies in Economics and Finance, 179 -195.
    27. [27]  Zhang, G. P. (2003). Time Series forecasting using a hybrid ARIMA and nural network model. Neurocomputing, 159-175. Retrieved November 10, 2016, from http://www.elsevier.com/locate/neucom Capital market information center. Retrieved 4th December 2016, from
    28. [28]  http://www.cmic.sec.gov.lk/wp-content/uploads/2012/10/08Market-Indices-Monthly2.xls
    29. [29]  User, S. (2016). Stock Market. Taxplusinvestment.com. Retrieved 6 December 2016, from http://taxplusinvestment.com/index.php/stock-market
    30. [30]  Colombo Stock Exchange (CSE) | Colombo Stock Watch. (2016). Colombostockwatch.com. Retrieved 6 December 2016, from http://colombostockwatch.com/cse/