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The Profitability of Technical Analysis: A Review

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  • Park, Cheol-Ho
  • Irwin, Scott H.

Abstract

The purpose of this report is to review the evidence on the profitability of technical analysis. To achieve this purpose, the report comprehensively reviews survey, theoretical and empirical studies regarding technical trading strategies. We begin by overviewing survey studies that have directly investigated market participants’ experience and views on technical analysis. The survey literature indicates that technical analysis has been widely used by market participants in futures markets and foreign exchange markets, and that about 30% to 40% of practitioners appear to believe that technical analysis is an important factor in determining price movement at shorter time horizons up to 6 months. Then we provide an overview of theoretical models that include implications about the profitability of technical analysis. Conventional efficient market theories, such as the martingale model and random walk models, rule out the possibility of technical trading profits in speculative markets, while relatively recent models such as noisy rational expectation models or behavioral models suggest that technical trading strategies may be profitable due to noise in the market or investors’ irrational behavior. Finally, empirical studies are surveyed. In this report, the empirical literature is categorized into two groups, “early” and “modern” studies, according to the characteristics of testing procedures. Early studies indicated that technical trading strategies were profitable in foreign exchange markets and futures markets, but not in stock markets before the 1980s. Modern studies indicated that technical trading strategies consistently generated economic profits in a variety of speculative markets at least until the early 1990s. Among a total of 92 modern studies, 58 studies found positive results regarding technical trading strategies, while 24 studies obtained negative results. Ten studies indicated mixed results. Despite the positive evidence on the profitability of technical trading strategies, it appears that most empirical studies are subject to various problems in their testing procedures, e.g., data snooping, ex post selection of trading rules or search technologies, and difficulties in estimation of risk and transaction costs. Future research must address these deficiencies in testing in order to provide conclusive evidence on the profitability of technical trading strategies.

Suggested Citation

  • Park, Cheol-Ho & Irwin, Scott H., 2004. "The Profitability of Technical Analysis: A Review," AgMAS Project Research Reports 37487, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
  • Handle: RePEc:ags:uiucrr:37487
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    Cited by:

    1. Ben Marshall & Sun Qian & Martin Young, 2009. "Is technical analysis profitable on US stocks with certain size, liquidity or industry characteristics?," Applied Financial Economics, Taylor & Francis Journals, vol. 19(15), pages 1213-1221.
    2. Olivier Brandouy & Philippe Mathieu, 2006. "A Broad-Spectrum Computational Approach for Market Efficiency," Computing in Economics and Finance 2006 492, Society for Computational Economics.
    3. Cheol-Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    4. Park, Cheol-Ho & Irwin, Scott H., 2004. "The Profitability Of Technical Trading Rules In Us Futures Markets: A Data Snooping Free Test," 2004 Conference, April 19-20, 2004, St. Louis, Missouri 19011, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    5. Schulmeister, Stephan, 2006. "The interaction between technical currency trading and exchange rate fluctuations," Finance Research Letters, Elsevier, vol. 3(3), pages 212-233, September.
    6. Alexeev, Vitali & Tapon, Francis, 2011. "Testing weak form efficiency on the Toronto Stock Exchange," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 661-691, September.
    7. Stephan Schulmeister, 2007. "The Interaction Between the Aggregate Behaviour of Technical Trading Systems and Stock Price Dynamics," WIFO Working Papers 290, WIFO.
    8. Roscoe, Philip & Howorth, Carole, 2009. "Identification through technical analysis: A study of charting and UK non-professional investors," Accounting, Organizations and Society, Elsevier, vol. 34(2), pages 206-221, February.
    9. Perlin, M., 2007. "M of a kind: A Multivariate Approach at Pairs Trading," MPRA Paper 8309, University Library of Munich, Germany.
    10. Lukas Menkhoff & Mark P. Taylor, 2007. "The Obstinate Passion of Foreign Exchange Professionals: Technical Analysis," Journal of Economic Literature, American Economic Association, vol. 45(4), pages 936-972, December.
    11. Schulmeister, Stephan, 2009. "Profitability of technical stock trading: Has it moved from daily to intraday data?," Review of Financial Economics, Elsevier, vol. 18(4), pages 190-201, October.
    12. Achilleas Zapranis & Prodromos E. Tsinaslanidis, 2012. "Identifying and evaluating horizontal support and resistance levels: an empirical study on US stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 22(19), pages 1571-1585, October.
    13. Marshall, Ben R. & Cahan, Rochester H. & Cahan, Jared M., 2008. "Can commodity futures be profitably traded with quantitative market timing strategies?," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1810-1819, September.
    14. David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
    15. Fang, Jiali & Jacobsen, Ben & Qin, Yafeng, 2014. "Predictability of the simple technical trading rules: An out-of-sample test," Review of Financial Economics, Elsevier, vol. 23(1), pages 30-45.
    16. Stephan Schulmeister, 2008. "Components of the profitability of technical currency trading," Applied Financial Economics, Taylor & Francis Journals, vol. 18(11), pages 917-930.
    17. Hannah Thinyane & Jonathan Millin, 2011. "An Investigation into the Use of Intelligent Systems for Currency Trading," Computational Economics, Springer;Society for Computational Economics, vol. 37(4), pages 363-374, April.
    18. Charlotte, Christiansen, 2011. "Intertemporal risk-return trade-off in foreign exchange rates," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(4), pages 535-549, October.
    19. Chen, Shi & Bao, Si & Zhou, Yu, 2016. "The predictive power of Japanese candlestick charting in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 148-165.
    20. Park, Cheol-Ho & Irwin, Scott H., 2005. "A Reality Check on Technical Trading Rule Profits in US Futures Markets," 2005 Conference, April 18-19, 2005, St. Louis, Missouri 19039, NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management.
    21. Perlin, M., 2007. "Evaluation of pairs trading strategy at the Brazilian financial market," MPRA Paper 8308, University Library of Munich, Germany.
    22. Batchelor, Roy & Kwan, Tai Yeong, 2007. "Judgemental bootstrapping of technical traders in the bond market," International Journal of Forecasting, Elsevier, vol. 23(3), pages 427-445.

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