IDEAS home Printed from https://ideas.repec.org/a/anm/alpnmr/v7y2019i2p289-300.html
   My bibliography  Save this article

Comparison of the Global, Local and Semi-Local Chaotic Prediction Methods for Stock Markets: The Case of FTSE-100 Index

Author

Listed:
  • Ayşe İşi
  • Fatih Çemrek

Abstract

Chaotic prediction methods are classified as global, local and semi-local methods. In this paper, unlike the studies in the literature, it is aimed to compare all these methods together for stock markets in terms of prediction performance and to determine the best prediction method for stock markets. For this purpose, Multi-Layer Perceptron (MLP) neural networks from global methods, nearest neighbour method from local methods, radial basis functions from semi-local methods are used. The FTSE-100 index is selected to represent the stock market and applied the all methods to these data. The prediction performance is measured in term of root mean square error (RMSE) and normalized mean square error (NMSE). As a result of the analysis; it has been determined that the best prediction method for the FTSE-100 index is the semi-local method. While it is possible to make a maximum of 5 days prediction with global and local methods, it has been determined that up to 20 days prediction can be made with the semi-local prediction methods. The results show that semi-local prediction methods are successful in predicting the behaviour of stock market.

Suggested Citation

  • Ayşe İşi & Fatih Çemrek, 2019. "Comparison of the Global, Local and Semi-Local Chaotic Prediction Methods for Stock Markets: The Case of FTSE-100 Index," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 7(2), pages 289-300, December.
    • Handle: RePEc:anm:alpnmr:v:7:y:2019:i:2:p:289-300
    DOI: http://dx.doi.org/10.17093/alphanumeric.629722
    as

    Download full text from publisher

    File URL: https://www.alphanumericjournal.com/media/Issue/volume-7-issue-2-2019/comparison-of-the-global-local-and-semi-local-chaotic-predic_3kKyFA2.pdf
    Download Restriction: no
    File URL: https://alphanumericjournal.com/article/comparison-of-the-global-local-and-semi-local-chaotic-prediction-methods-for-stock-markets-the-case-of-ftse-100-index/
    Download Restriction: no
    File URL: https://libkey.io/http://dx.doi.org/10.17093/alphanumeric.629722?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
    ---><---

    References listed on IDEAS

    as
    1. Webel, Karsten, 2012. "Chaos in German stock returns — New evidence from the 0–1 test," Economics Letters, Elsevier, vol. 115(3), pages 487-489.
    2. Shang, Pengjian & Li, Xuewei & Kamae, Santi, 2005. "Chaotic analysis of traffic time series," Chaos, Solitons & Fractals, Elsevier, vol. 25(1), pages 121-128.
    3. Ravi Vaidyanathan & Tim Krehbiel, 1992. "Does the S&P 500 futures mispricing series exhibit nonlinear dependence across time?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 12(6), pages 659-677, December.
    4. Hsieh, David A, 1991. "Chaos and Nonlinear Dynamics: Application to Financial Markets," Journal of Finance, American Finance Association, vol. 46(5), pages 1839-1877, December.
    5. Mayfield, E Scott & Mizrach, Bruce, 1992. "On Determining the Dimension of Real-Time Stock-Price Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(3), pages 367-374, July.
    6. Dominique Guegan & Ludovic Mercier, 2005. "Prediction in Chaotic Time series : Methods and Comparisons with an application to financial intra day data," Post-Print halshs-00180862, HAL.
    7. D. Guegan & L. Mercier, 2005. "Prediction in chaotic time series: methods and comparisons with an application to financial intra-day data," The European Journal of Finance, Taylor & Francis Journals, vol. 11(2), pages 137-150.
    8. Michael Hanias & Lykourgos Magafas & P. Konstantaki, 2013. "Non Linear Analysis Of S&P Index," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 8(4), pages 125-135, December.
    9. Abhyankar, A & Copeland, L S & Wong, W, 1995. "Nonlinear Dynamics in Real-Time Equity Market Indices: Evidence from the United Kingdom," Economic Journal, Royal Economic Society, vol. 105(431), pages 864-880, July.
    Full references (including those not matched with items on IDEAS)

    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. Marisa Faggini & Bruna Bruno & Anna Parziale, 2019. "Does Chaos Matter in Financial Time Series Analysis?," International Journal of Economics and Financial Issues, Econjournals, vol. 9(4), pages 18-24.
    2. McKenzie, Michael D., 2001. "Chaotic behavior in national stock market indices: New evidence from the close returns test," Global Finance Journal, Elsevier, vol. 12(1), pages 35-53.
    3. Costas Siriopoulos & Alexandros Leontitsis, 2002. "Nonlinear Noise Estimation in International Capital Markets," Multinational Finance Journal, Multinational Finance Journal, vol. 6(1), pages 43-63, March.
    4. M. Shibley Sadique, 2011. "Testing for Neglected Nonlinearity in Weekly Foreign Exchange Rates," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 77-88, June.
    5. Anagnostidis, Panagiotis & Emmanouilides, Christos J., 2015. "Nonlinearity in high-frequency stock returns: Evidence from the Athens Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 473-487.
    6. Kian-Ping Lim & Melvin J. Hinich & Venus Khim-Sen Liew, 2005. "Statistical Inadequacy of GARCH Models for Asian Stock Markets," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 4(3), pages 263-279, December.
    7. Kian-Ping Lim & Hock-Ann Lee & Venus Khim-Sen Liew, 2003. "International Diversification Benefits in ASEAN Stock Markets: a Revisit," Finance 0308003, University Library of Munich, Germany.
    8. Rachida Hennani & Michel Terraza, 2015. "Contributions of a noisy chaotic model to the stressed Value-at-Risk," Economics Bulletin, AccessEcon, vol. 35(2), pages 1262-1273.
    9. Kian-Ping Lim & Venus Khim-Sen Liew & Hock-Tsen Wong, 2003. "Weak-form Efficient Market Hypothesis, Behavioural Finance and Episodic Transient Dependencies: The Case of the Kuala Lumpur Stock Exchange," Finance 0312012, University Library of Munich, Germany.
    10. Tiwari, Aviral Kumar & Gupta, Rangan, 2019. "Chaos in G7 stock markets using over one century of data: A note," Research in International Business and Finance, Elsevier, vol. 47(C), pages 304-310.
    11. Dominique Guegan, 2009. "Chaos in economics and finance," Post-Print halshs-00187885, HAL.
    12. Ayan Bhattacharya & Rudra Sensarma, 2013. "Non-linearities in Emerging Financial Markets: Evidence from India," IIM Kozhikode Society & Management Review, , vol. 2(2), pages 165-175, July.
    13. Claudio Bonilla & Carlos Maquieira & Rafael Romero-Meza, 2008. "Nonlinear behaviour of emerging market bonds spreads: the Latin American case," Applied Economics, Taylor & Francis Journals, vol. 40(20), pages 2697-2702.
    14. Leontitsis, Alexandros & Vorlow, Constantinos E., 2006. "Accounting for outliers and calendar effects in surrogate simulations of stock return sequences," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 368(2), pages 522-530.
    15. Kian-Ping Lim & M. Azali & M.S. Habibullah & Venus Khim-Sen Liew, 2003. "Are Non-Linear Dynamics a Universal Occurrence? Further Evidence From Asian Stock Markets," Finance 0308001, University Library of Munich, Germany.
    16. Sandubete, Julio E. & Escot, Lorenzo, 2020. "Chaotic signals inside some tick-by-tick financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 137(C).
    17. Antoniou, Antonios & Vorlow, Constantinos E., 2005. "Price clustering and discreteness: is there chaos behind the noise?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 389-403.
    18. Cheung, Yin-Wong & Lai, Kon S., 1995. "A search for long memory in international stock market returns," Journal of International Money and Finance, Elsevier, vol. 14(4), pages 597-615, August.
    19. K.P. Lim & M.J. Hinich & K.S. Liew, 2003. "GARCH Diagnosis with Portmanteau Bicorrelation Test: An Application on the Malaysia's Stock Market," Finance 0307013, University Library of Munich, Germany.
    20. Guglielmo Maria Caporale & Luis A. Gil‐Alana & James C. Orlando, 2016. "Linkages Between the US and European Stock Markets: A Fractional Cointegration Approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 21(2), pages 143-153, April.

    More about this item

    Keywords

    Chaotic Prediction; Chaotic Time Series; FTSE-100 Index; Nearest Neighbour Method; Radial Basis Functions Method; Stock Market;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    Statistics

    Access and download statistics

    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:anm:alpnmr:v:7:y:2019:i:2:p:289-300. 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: Bahadir Fatih Yildirim (email available below). General contact details of provider: https://www.alphanumericjournal.com/ .

    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.