IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v122y2016icp69-80.html
   My bibliography  Save this article

Minimal variability OWA operator combining ANFIS and fuzzy c-means for forecasting BSE index

Author

Listed:
  • Kaur, Gurbinder
  • Dhar, Joydip
  • Guha, Rangan Kumar

Abstract

Stock data sets usually consist of many varied components or multiple periods of stock prices, resulting in a tedious stock market prediction using such high dimensional data. To reduce data dimensions, it is crucial to fuse high dimensional data into a useful forecasting factor without losing information contained in the original variables. Decision makers may desire low variability associated with a chosen weighting vector, further complicating proper weight assignment for past stock prices. In this paper a new time series algorithm is proposed to overcome above mentioned shortcomings, which employs a minimal variation order weighted average (OWA) operator to aggregate values of high dimensional data into a single attribute. Based on the proposed model a hybrid network based fuzzy inference system combined with fuzzy c-means clustering is used to forecast Bombay Stock Exchange Index (BSE30).

Suggested Citation

  • Kaur, Gurbinder & Dhar, Joydip & Guha, Rangan Kumar, 2016. "Minimal variability OWA operator combining ANFIS and fuzzy c-means for forecasting BSE index," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 122(C), pages 69-80.
    • Handle: RePEc:eee:matcom:v:122:y:2016:i:c:p:69-80
    DOI: 10.1016/j.matcom.2015.12.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475415002530
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2015.12.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Graham Smith & Keith Jefferis & Hyun-Jung Ryoo, 2002. "African stock markets: multiple variance ratio tests of random walks," Applied Financial Economics, Taylor & Francis Journals, vol. 12(7), pages 475-484.
    2. Huarng, Kunhuang & Yu, Tiffany Hui-Kuang, 2006. "The application of neural networks to forecast fuzzy time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 363(2), pages 481-491.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    5. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    6. Cheng, Ching-Hsue & Wei, Liang-Ying & Liu, Jing-Wei & Chen, Tai-Liang, 2013. "OWA-based ANFIS model for TAIEX forecasting," Economic Modelling, Elsevier, vol. 30(C), pages 442-448.
    7. Richard Hathaway & James Bezdek, 1988. "Recent convergence results for the fuzzy c-means clustering algorithms," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 237-247, September.
    8. Aladag, Cagdas Hakan & Yolcu, Ufuk & Egrioglu, Erol, 2010. "A high order fuzzy time series forecasting model based on adaptive expectation and artificial neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(4), pages 875-882.
    9. Jilani, Tahseen Ahmed & Burney, Syed Muhammad Aqil, 2008. "A refined fuzzy time series model for stock market forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(12), pages 2857-2862.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Jianzhou & Dong, Yunxuan & Zhang, Kequan & Guo, Zhenhai, 2017. "A numerical model based on prior distribution fuzzy inference and neural networks," Renewable Energy, Elsevier, vol. 112(C), pages 486-497.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wei, Liang-Ying, 2013. "A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX," Economic Modelling, Elsevier, vol. 33(C), pages 893-899.
    2. Cheng, Ching-Hsue & Wei, Liang-Ying & Liu, Jing-Wei & Chen, Tai-Liang, 2013. "OWA-based ANFIS model for TAIEX forecasting," Economic Modelling, Elsevier, vol. 30(C), pages 442-448.
    3. Alagidede, Paul & Panagiotidis, Theodore, 2009. "Modelling stock returns in Africa's emerging equity markets," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 1-11, March.
    4. repec:wyi:journl:002087 is not listed on IDEAS
    5. Aggarwal, Divya, 2019. "Do bitcoins follow a random walk model?," Research in Economics, Elsevier, vol. 73(1), pages 15-22.
    6. David G. McMillan & Pako Thupayagale, 2009. "The efficiency of African equity markets," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 26(4), pages 275-292, October.
    7. Cheng, Ching-Hsue & Wei, Liang-Ying, 2014. "A novel time-series model based on empirical mode decomposition for forecasting TAIEX," Economic Modelling, Elsevier, vol. 36(C), pages 136-141.
    8. F. DePenya & L. Gil-Alana, 2006. "Testing of nonstationary cycles in financial time series data," Review of Quantitative Finance and Accounting, Springer, vol. 27(1), pages 47-65, August.
    9. Khim-Sen Liew & Kian-Ping Lim & Chee-Keong Choong, 2003. "On The Forecastability Of Asean-5 Stock Markets Returns Using Time Series Models," Finance 0307012, University Library of Munich, Germany.
    10. Eric Hillebrand, 2003. "The Effects of Japanese Foreign Exchange Intervention: GARCH Estimation and Change Point Detection," Departmental Working Papers 2003-10, Department of Economics, Louisiana State University.
    11. Eymen Errais & Dhikra Bahri, 2016. "Is Standard Deviation a Good Measure of Volatility? the Case of African Markets with Price Limits," Annals of Economics and Finance, Society for AEF, vol. 17(1), pages 145-165, May.
    12. Claudeci Da Silva & Hugo Agudelo Murillo & Joaquim Miguel Couto, 2014. "Early Warning Systems: Análise De Ummodelo Probit De Contágio De Crise Dos Estados Unidos Para O Brasil(2000-2010)," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 110, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    13. Frédy Pokou & Jules Sadefo Kamdem & François Benhmad, 2024. "Hybridization of ARIMA with Learning Models for Forecasting of Stock Market Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 63(4), pages 1349-1399, April.
    14. Xekalaki, Evdokia & Degiannakis, Stavros, 2005. "Evaluating volatility forecasts in option pricing in the context of a simulated options market," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 611-629, April.
    15. Bruno Solnik, 1991. "Finance Theory and Investment Management," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 127(III), pages 303-324, September.
    16. Fatma SIALA GUERMEZI, & Amani BOUSSAADA, 2016. "The Weak Form Of Informational Efficiency: Case Of Tunisian Banking Sector," EcoForum, "Stefan cel Mare" University of Suceava, Romania, Faculty of Economics and Public Administration - Economy, Business Administration and Tourism Department., vol. 5(1), pages 1-1, January.
    17. Haque Mahfuzul & Hassan M. Kabir & Maroney Neal C & Sackley William H, 2004. "An Empirical Examination of Stability, Predictability, and Volatility of Middle Eastern and African Emerging Stock Markets," Review of Middle East Economics and Finance, De Gruyter, vol. 2(1), pages 18-41, April.
    18. Siddique, Maryam, 2023. "Does the Adaptive Market Hypothesis Exist in Equity Market? Evidence from Pakistan Stock Exchange," OSF Preprints 9b5dx, Center for Open Science.
    19. Ben Rejeb, Aymen & Boughrara, Adel, 2013. "Financial liberalization and stock markets efficiency: New evidence from emerging economies," Emerging Markets Review, Elsevier, vol. 17(C), pages 186-208.
    20. De Santis, Giorgio & imrohoroglu, Selahattin, 1997. "Stock returns and volatility in emerging financial markets," Journal of International Money and Finance, Elsevier, vol. 16(4), pages 561-579, August.
    21. Shimokawa, Tetsuya & Suzuki, Kyoko & Misawa, Tadanobu, 2007. "An agent-based approach to financial stylized facts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 379(1), pages 207-225.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:matcom:v:122:y:2016:i:c:p:69-80. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.