IDEAS home Printed from https://ideas.repec.org/a/eee/jbfina/v61y2015icp172-183.html
   My bibliography  Save this article

Trend definition or holding strategy: What determines the profitability of candlestick charting?

Author

Listed:
  • Lu, Tsung-Hsun
  • Chen, Yi-Chi
  • Hsu, Yu-Chin

Abstract

We ask what determines the profitability of candlestick trading strategies. Is it the definition of trend and/or the holding strategy that one uses in candlestick charting analysis? To answer this, we systematically consider three definitions of trend and four holding strategies. Applying candlestick trading strategies to the DJIA component data, we find that regardless of which definition of trend is used, eight three-day reversal patterns with a Caginalp–Laurent holding strategy are profitable when we set the transaction cost at 0.5% and after we account for data-snooping bias, while the patterns with a Marshall–Young–Rose holding strategy are not profitable. For sensitivity analysis, we also find that our results are not qualitatively changed on a lower transaction cost of 0.1%, or when we conduct the subsample analyses based on three equal periods and three distinct market conditions. When considering a more volatile market, evidence in favor of candlestick trading strategies is strengthened.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jbfina:v:61:y:2015:i:c:p:172-183
    DOI: 10.1016/j.jbankfin.2015.09.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378426615002678
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jbankfin.2015.09.009?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Joseph P. Romano & Michael Wolf, 2005. "Stepwise Multiple Testing as Formalized Data Snooping," Econometrica, Econometric Society, vol. 73(4), pages 1237-1282, July.
    2. Lunde A. & Timmermann A., 2004. "Duration Dependence in Stock Prices: An Analysis of Bull and Bear Markets," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 253-273, July.
    3. Hendrik Bessembinder & Kalok Chan, 1998. "Market Efficiency and the Returns to Technical Analysis," Financial Management, Financial Management Association, vol. 27(2), Summer.
    4. Ryan Sullivan & Allan Timmermann & Halbert White, 1999. "Data‐Snooping, Technical Trading Rule Performance, and the Bootstrap," Journal of Finance, American Finance Association, vol. 54(5), pages 1647-1691, October.
    5. Matthieu Duvinage & Paolo Mazza & Mikael Petitjean, 2013. "The intra-day performance of market timing strategies and trading systems based on Japanese candlesticks," Quantitative Finance, Taylor & Francis Journals, vol. 13(7), pages 1059-1070, January.
    6. 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.
    7. Lo, Andrew W & MacKinlay, A Craig, 1990. "When Are Contrarian Profits Due to Stock Market Overreaction?," The Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 175-205.
    8. Ben Marshall & Martin Young & Rochester Cahan, 2008. "Are candlestick technical trading strategies profitable in the Japanese equity market?," Review of Quantitative Finance and Accounting, Springer, vol. 31(2), pages 191-207, August.
    9. De Bondt, Werner F M & Thaler, Richard, 1985. "Does the Stock Market Overreact?," Journal of Finance, American Finance Association, vol. 40(3), pages 793-805, July.
    10. Bruce N. Lehmann, 1990. "Fads, Martingales, and Market Efficiency," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 105(1), pages 1-28.
    11. Pastor, Lubos & Veronesi, Pietro, 2006. "Was there a Nasdaq bubble in the late 1990s?," Journal of Financial Economics, Elsevier, vol. 81(1), pages 61-100, July.
    12. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    13. Kwok-Wah Fung, Alexander & Lam, Kin, 2004. "Overreaction of index futures in Hong Kong," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 331-351, June.
    14. Marshall, Ben R. & Young, Martin R. & Rose, Lawrence C., 2006. "Candlestick technical trading strategies: Can they create value for investors?," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2303-2323, August.
    15. Jegadeesh, Narasimhan & Titman, Sheridan, 1995. "Overreaction, Delayed Reaction, and Contrarian Profits," Review of Financial Studies, Society for Financial Studies, vol. 8(4), pages 973-993.
    16. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    17. 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.
    18. 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.
    19. Levy, Robert A, 1971. "The Predicitive Significance of Five-Point Chart Patterns," The Journal of Business, University of Chicago Press, vol. 44(3), pages 316-323, July.
    20. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    21. Jegadeesh, Narasimhan, 1990. "Evidence of Predictable Behavior of Security Returns," Journal of Finance, American Finance Association, vol. 45(3), pages 881-898, July.
    22. Han, Yufeng & Yang, Ke & Zhou, Guofu, 2013. "A New Anomaly: The Cross-Sectional Profitability of Technical Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(5), pages 1433-1461, October.
    23. G. Caginalp & H. Laurent, 1998. "The predictive power of price patterns," Applied Mathematical Finance, Taylor & Francis Journals, vol. 5(3-4), pages 181-205.
    24. Hsu, Po-Hsuan & Hsu, Yu-Chin & Kuan, Chung-Ming, 2010. "Testing the predictive ability of technical analysis using a new stepwise test without data snooping bias," Journal of Empirical Finance, Elsevier, vol. 17(3), pages 471-484, June.
    25. Adrian R. Pagan & Kirill A. Sossounov, 2003. "A simple framework for analysing bull and bear markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 23-46.
    26. 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.
    27. 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.
    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. 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).
    2. 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.
    3. Krzysztof Piasecki & Anna Łyczkowska-Hanćkowiak, 2019. "Representation of Japanese Candlesticks by Oriented Fuzzy Numbers," Econometrics, MDPI, vol. 8(1), pages 1-24, December.
    4. 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.
    5. Huadong Chang & Guozhi An, 2019. "Will History Repeat Itself? Empirical Research on A-Share Candlesticks in China Based on Matching Method," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 9(5), pages 1-8.
    6. 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.
    7. 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.

    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. 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.
    2. 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.
    3. 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).
    4. 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.
    5. Shynkevich, Andrei, 2012. "Performance of technical analysis in growth and small cap segments of the US equity market," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 193-208.
    6. Isakov, Dusan & Marti, Didier, 2011. "Technical Analysis with a Long-Term Perspective: Trading Strategies and Market Timing Ability," FSES Working Papers 421, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    7. Hung, Chiayu & Lai, Hung-Neng, 2022. "Information asymmetry and the profitability of technical analysis," Journal of Banking & Finance, Elsevier, vol. 134(C).
    8. Stephen A. Gorman & Frank J. Fabozzi, 2021. "The ABC’s of the alternative risk premium: academic roots," Journal of Asset Management, Palgrave Macmillan, vol. 22(6), pages 405-436, October.
    9. Jin, Xiaoye, 2022. "Performance of intraday technical trading in China’s gold market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 76(C).
    10. Shynkevich, Andrei, 2013. "Time-series momentum as an intra- and inter-industry effect: Implications for market efficiency," Journal of Economics and Business, Elsevier, vol. 69(C), pages 64-85.
    11. Bajgrowicz, Pierre & Scaillet, Olivier, 2012. "Technical trading revisited: False discoveries, persistence tests, and transaction costs," Journal of Financial Economics, Elsevier, vol. 106(3), pages 473-491.
    12. Friesen, Geoffrey C. & Weller, Paul A. & Dunham, Lee M., 2009. "Price trends and patterns in technical analysis: A theoretical and empirical examination," Journal of Banking & Finance, Elsevier, vol. 33(6), pages 1089-1100, June.
    13. Han, Yufeng & Zhou, Guofu & Zhu, Yingzi, 2016. "A trend factor: Any economic gains from using information over investment horizons?," Journal of Financial Economics, Elsevier, vol. 122(2), pages 352-375.
    14. 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.
    15. 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.
    16. Shynkevich, Andrei, 2016. "Predictability in bond returns using technical trading rules," Journal of Banking & Finance, Elsevier, vol. 70(C), pages 55-69.
    17. Dan Anghel, 2013. "How Reliable is the Moving Average Crossover Rule for an Investor on the Romanian Stock Market?," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 5(2), pages 089-115, December.
    18. Alhashel, Bader S. & Almudhaf, Fahad W. & Hansz, J. Andrew, 2018. "Can technical analysis generate superior returns in securitized property markets? Evidence from East Asia markets," Pacific-Basin Finance Journal, Elsevier, vol. 47(C), pages 92-108.
    19. Shynkevich, Andrei, 2012. "Short-term predictability of equity returns along two style dimensions," Journal of Empirical Finance, Elsevier, vol. 19(5), pages 675-685.
    20. Zongwu Cai & Jiancheng Jiang & Jingshuang Zhang & Xibin Zhang, 2015. "A new semiparametric test for superior predictive ability," Empirical Economics, Springer, vol. 48(1), pages 389-405, February.

    More about this item

    Keywords

    Candlestick; Technical analysis; Trading rules; Behavioral finance; Step-SPA test;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    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:eee:jbfina:v:61:y:2015:i:c:p:172-183. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbf .

    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.