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The predictive power of price patterns

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  • G. Caginalp
  • H. Laurent

Abstract

Using two sets of data, including daily prices (open, close, high and low) of all S&P 500 stocks between 1992 and 1996, we perform a satistical test of the predictive capability of candlestick patterns. Out-of-sample tests indicate statistical significance at the level of 36 standard deviations from the null hypothesis, and indicate a profit of almost 1% during a two-day holding period. An essentially non-parametric test utilizes standard definitions of three-day candlestick patterns and removes conditions on magnitudes. The results provide evidence that traders are influenced by price behaviour. To the best of our knowledge, this is the first scientific test to provide strong evidence in favour of any trading rule or pattern on a large unrestricted scale.

Suggested Citation

  • G. Caginalp & H. Laurent, 1998. "The predictive power of price patterns," Applied Mathematical Finance, Taylor & Francis Journals, vol. 5(3-4), pages 181-205.
  • Handle: RePEc:taf:apmtfi:v:5:y:1998:i:3-4:p:181-205
    DOI: 10.1080/135048698334637
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    1. 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.
    2. Shiller, Robert J, 1981. "The Use of Volatility Measures in Assessing Market Efficiency," Journal of Finance, American Finance Association, vol. 36(2), pages 291-304, May.
    3. Rosenthal, Robert W., 1981. "Games of perfect information, predatory pricing and the chain-store paradox," Journal of Economic Theory, Elsevier, vol. 25(1), pages 92-100, August.
    4. T. Randolph Beard & Richard O. Beil, 1994. "Do People Rely on the Self-Interested Maximization of Others? An Experimental Test," Management Science, INFORMS, vol. 40(2), pages 252-262, February.
    5. Blume, Lawrence & Easley, David & O'Hara, Maureen, 1994. "Market Statistics and Technical Analysis: The Role of Volume," Journal of Finance, American Finance Association, vol. 49(1), pages 153-181, March.
    6. David Porter & Vernon Smith, 1994. "Stock market bubbles in the laboratory," Applied Mathematical Finance, Taylor & Francis Journals, vol. 1(2), pages 111-128.
    7. 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. Lu, Tsung-Hsun & Chen, Yi-Chi & Hsu, Yu-Chin, 2015. "Trend definition or holding strategy: What determines the profitability of candlestick charting?," Journal of Banking & Finance, Elsevier, vol. 61(C), pages 172-183.
    2. Chiang, Mi-Hsiu & Chiu, Hsin-Yu & Kuo, Wei-Yu, 2021. "Predictive ability of similarity-based futures trading strategies," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    3. Lu, Tsung-Hsun, 2014. "The profitability of candlestick charting in the Taiwan stock market," Pacific-Basin Finance Journal, Elsevier, vol. 26(C), pages 65-78.
    4. Shangkun Deng & Zhihao Su & Yanmei Ren & Haoran Yu & Yingke Zhu & Chenyang Wei, 2022. "Can Japanese Candlestick Patterns be Profitable on the Component Stocks of the SSE50 Index?," SAGE Open, , vol. 12(3), pages 21582440221, August.
    5. Tsung‐Hsun Lu & Yung‐Ming Shiu & Tsung‐Chi Liu, 2012. "Profitable candlestick trading strategies—The evidence from a new perspective," Review of Financial Economics, John Wiley & Sons, vol. 21(2), pages 63-68, April.
    6. Piyapas Tharavanij & Vasan Siraprapasiri & Kittichai Rajchamaha, 2017. "Profitability of Candlestick Charting Patterns in the Stock Exchange of Thailand," SAGE Open, , vol. 7(4), pages 21582440177, October.
    7. Horton, Marshall J., 2009. "Stars, crows, and doji: The use of candlesticks in stock selection," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 283-294, May.
    8. Boainain, Pedro G. & Valls Pereira, Pedro L., 2009. "“Ombro-Cabeça-Ombro”: Testando a Lucratividade do Padrão Gráfico de Análise Técnica no Mercado de Ações Brasileiro [Head and Shoulder: testing the profitability of graphic pattern of technical anal," MPRA Paper 15653, University Library of Munich, Germany.
    9. Jessica James & Louis Yang, 2010. "Stop-losses, maximum drawdown-at-risk and replicating financial time series with the stationary bootstrap," Quantitative Finance, Taylor & Francis Journals, vol. 10(1), pages 1-12.
    10. U, JuHyok & Lu, PengYu & Kim, ChungSong & Ryu, UnSok & Pak, KyongSok, 2020. "A new LSTM based reversal point prediction method using upward/downward reversal point feature sets," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
    11. Tsung-Hsun Lu & Yung-Ming Shiu, 2016. "Can 1-day candlestick patterns be profitable on the 30 component stocks of the DJIA?," Applied Economics, Taylor & Francis Journals, vol. 48(35), pages 3345-3354, July.
    12. Lu, Tsung-Hsun & Shiu, Yung-Ming & Liu, Tsung-Chi, 2012. "Profitable candlestick trading strategies—The evidence from a new perspective," Review of Financial Economics, Elsevier, vol. 21(2), pages 63-68.
    13. Shangkun Deng & Yingke Zhu & Xiaoru Huang & Shuangyang Duan & Zhe Fu, 2022. "High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method," Future Internet, MDPI, vol. 14(6), pages 1-21, June.
    14. Gil Cohen, 2022. "Artificial Intelligence in Trading the Financial Markets," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 101-110.
    15. Gil Cohen, 2021. "Optimizing candlesticks patterns for Bitcoin's trading systems," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 1155-1167, October.
    16. Ahmet Duran & Michael Bommarito, 2011. "A profitable trading and risk management strategy despite transaction costs," Quantitative Finance, Taylor & Francis Journals, vol. 11(6), pages 829-848.
    17. Osama El Ansary & Mona Atuea, 2017. "Testing the Effect of Technical Analysis Strategies on Achieving Abnormal Return: Evidence from Egyptian Stock Market," Accounting and Finance Research, Sciedu Press, vol. 6(2), pages 1-26, May.
    18. Zhu, Min & Atri, Said & Yegen, Eyub, 2016. "Are candlestick trading strategies effective in certain stocks with distinct features?," Pacific-Basin Finance Journal, Elsevier, vol. 37(C), pages 116-127.
    19. 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.
    20. Tsung-Hsun Lu & Yung-Ming Shiu, 2012. "Tests for Two-Day Candlestick Patterns in the Emerging Equity Market of Taiwan," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 48(0), pages 41-57, January.
    21. Yaohu Lin & Shancun Liu & Haijun Yang & Harris Wu & Bingbing Jiang, 2021. "Improving stock trading decisions based on pattern recognition using machine learning technology," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-25, August.
    22. Gil Cohen, 2020. "Best Candlesticks Pattern to Trade Stocks," International Journal of Economics and Financial Issues, Econjournals, vol. 10(2), pages 256-261.
    23. Huiwen Wang & Wenyang Huang & Shanshan Wang, 2021. "Forecasting open-high-low-close data contained in candlestick chart," Papers 2104.00581, arXiv.org.
    24. Fonseca, Carla L.G. & de Resende, Charlene C. & Fernandes, Danilo H.C. & Cardoso, Rodrigo T.N. & de Magalhães, A.R. Bosco, 2021. "Is the choice of the candlestick dimension relevant in econophysics?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
    25. 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.

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