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A Computational Exploration of the Efficacy of Fibonacci Sequences in Technical Analysis and Trading

Author

Listed:
  • Sukanto Bhattacharya

    (Department of Business Administration Alaska Pacific University)

  • Kuldeep Kumar

    (School of Information Technology Bond University)

Abstract

Among the vast assemblage of technical analysis tools, the ones based on Fibonacci recurrences in asset prices are relatively more scientific. In this paper, we review some of the popular technical analysis methodologies based on Fibonacci sequences and also advance a theoretical rationale as to why security prices may be seen to follow such sequences. We also analyse market data for an indicative empirical validation of the efficacy or otherwise of such sequences in predicting critical security price retracements that may be useful in constructing automated trading systems.

Suggested Citation

  • Sukanto Bhattacharya & Kuldeep Kumar, 2006. "A Computational Exploration of the Efficacy of Fibonacci Sequences in Technical Analysis and Trading," Annals of Economics and Finance, Society for AEF, vol. 7(1), pages 185-196, May.
    • Handle: RePEc:cuf:journl:y:2006:v:7:i:1:p:185-196
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    References listed on IDEAS

    as
    1. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    2. repec:bla:jfinan:v:55:y:2000:i:4:p:1705-1770 is not listed on IDEAS
    3. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
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    Cited by:

    1. Ikhlaas Gurrib, 2022. "Technical Analysis, Energy Cryptos and Energy Equity Markets," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 249-267, March.
    2. Ikhlaas Gurrib & Mohammad Nourani & Rajesh Kumar Bhaskaran, 2022. "Energy crypto currencies and leading U.S. energy stock prices: are Fibonacci retracements profitable?," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-27, December.
    3. Jianrong Wei & Jiping Huang, 2012. "An Exotic Long-Term Pattern in Stock Price Dynamics," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-5, December.
    4. Marañon, Matias & Kumral, Mustafa, 2018. "Exploring the Elliott Wave Principle to interpret metal commodity price cycles," Resources Policy, Elsevier, vol. 59(C), pages 125-138.

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    JEL classification:

    • G1 - Financial Economics - - General Financial Markets

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