IDEAS home Printed from https://ideas.repec.org/p/bog/wpaper/134.html
   My bibliography  Save this paper

Decomposing the predictive performance of the moving average trading rule of technical analysis: the contribution of linear and non linear dependencies in stock returns

Author

Listed:
  • Alexandros E. Milionis

    () (Bank of Greece and University of Aegean)

  • Evangelia Papanagiotou

    (University of the Aegean)

Abstract

On several occasions technical analysis rules have been shown to have predictive power. The main purpose of this work is to decompose the predictive power of the moving average trading rule and isolate the portion that could be attributed to the possible exploitation of linear and non linear dependencies in stock returns. Data for the General Index of the Athens Stock Exchange are filtered using linear filters so that the resulting simulated “returns” exhibit no serial correlation. Applying moving average trading rules to both the original and the simulated indices and using a statistical testing procedure that takes into account the sensitivity of the performance of the trading rule as a function of moving average length, it is found that the predictive power of the trading rule is clearly weakened when applied to the simulated index indicating that a substantial part of the rule’s predictive power is due to the exploitation of linear dependencies in stock returns. It is also found that the contribution of linear dependencies in stock returns to the performance of the trading rule is increased for shorter moving average lengths.

Suggested Citation

  • Alexandros E. Milionis & Evangelia Papanagiotou, 2011. "Decomposing the predictive performance of the moving average trading rule of technical analysis: the contribution of linear and non linear dependencies in stock returns," Working Papers 134, Bank of Greece.
  • Handle: RePEc:bog:wpaper:134
    as

    Download full text from publisher

    File URL: http://www.bankofgreece.gr/BogEkdoseis/Paper2011134.pdf
    File Function: Full Text
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Alexandros E. Milionis & Evangelia Papanagiotou, 2008. "On the Use of the Moving Average Trading Rule to Test for Weak Form Efficiency in Capital Markets," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 37(2), pages 181-201, July.
    2. Fang, Yue & Xu, Daming, 2003. "The predictability of asset returns: an approach combining technical analysis and time series forecasts," International Journal of Forecasting, Elsevier, vol. 19(3), pages 369-385.
    3. Theodore Panagiotidis, 2010. "Market efficiency and the Euro: the case of the Athens stock exchange," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 37(3), pages 237-251, July.
    4. Neftci, Salih N, 1991. "Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of "Technical Analysis."," The Journal of Business, University of Chicago Press, vol. 64(4), pages 549-571, October.
    5. Dolado, Juan J & Jenkinson, Tim & Sosvilla-Rivero, Simon, 1990. " Cointegration and Unit Roots," Journal of Economic Surveys, Wiley Blackwell, vol. 4(3), pages 249-273.
    6. Ki-Yeol Kwon & Richard Kish, 2002. "Technical trading strategies and return predictability: NYSE," Applied Financial Economics, Taylor & Francis Journals, vol. 12(9), pages 639-653.
    7. Olson, Dennis, 2004. "Have trading rule profits in the currency markets declined over time?," Journal of Banking & Finance, Elsevier, vol. 28(1), pages 85-105, January.
    8. F. FernAndez-RodrIguez & S. Sosvilla-Rivero & J. Andrada-FElix, 2003. "Technical analysis in foreign exchange markets: evidence from the EMS," Applied Financial Economics, Taylor & Francis Journals, vol. 13(2), pages 113-122.
    9. Alexandros Milionis & Evangelia Papanagiotou, 2009. "A study of the predictive performance of the moving average trading rule as applied to NYSE, the Athens Stock Exchange and the Vienna Stock Exchange: sensitivity analysis and implications for weak-for," Applied Financial Economics, Taylor & Francis Journals, vol. 19(14), pages 1171-1186.
    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. Liu, Xiaojia & An, Haizhong & Wang, Lijun & Jia, Xiaoliang, 2017. "An integrated approach to optimize moving average rules in the EUA futures market based on particle swarm optimization and genetic algorithms," Applied Energy, Elsevier, vol. 185(P2), pages 1778-1787.
    2. Wang, Lijun & An, Haizhong & Liu, Xiaojia & Huang, Xuan, 2016. "Selecting dynamic moving average trading rules in the crude oil futures market using a genetic approach," Applied Energy, Elsevier, vol. 162(C), pages 1608-1618.

    More about this item

    Keywords

    Market Efficiency; Technical Analysis; Moving Average Trading Rules; Athens Stock Exchange.;

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:bog:wpaper:134. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christina Tsochatzi). General contact details of provider: http://edirc.repec.org/data/boggvgr.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.