IDEAS home Printed from https://ideas.repec.org/a/spr/jecfin/v40y2016i1p41-59.html
   My bibliography  Save this article

Effects of time horizon and asset condition on the profitability of technical trading rules

Author

Listed:
  • Roy Hayes
  • Jingwei Wu
  • Ruijra Chaysiri
  • Jean Bae
  • Peter Beling
  • William Scherer

Abstract

In much of the literature, the debate over technical trading strategies has centered around the question of whether an actively managed portfolio, controlled by a technical indicator, can outperform a passively managed portfolio. Typically, the time horizon is considered to be years. Additionally, the trader is assumed to use a technical trading strategy that is independent of asset conditions. These assumptions may not correspond well with reality. Traders often have much shorter time horizons and may switch between rebalancing or trading strategies on the basis of perceived shifts in market condition. This paper presents a study of the profitability of technical trading rules as a function of asset state or condition. Several common technical trading strategies were run on 296 stocks over a 15 year period. Strategies were run with 1 month rolling time horizons, significantly shorter than those used in similar studies in the literature. Stocks were segmented based on volatility and volume, which allowed for the examination of a strategy’s performance in different asset conditions. Several strategies were demonstrated to have consistently better risk-to-reward ratios under specific asset conditions and short time horizons. This finding helps to explain why some practitioners implement technical trading strategies. Copyright Springer Science+Business Media New York 2016

Suggested Citation

  • Roy Hayes & Jingwei Wu & Ruijra Chaysiri & Jean Bae & Peter Beling & William Scherer, 2016. "Effects of time horizon and asset condition on the profitability of technical trading rules," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 40(1), pages 41-59, January.
    • Handle: RePEc:spr:jecfin:v:40:y:2016:i:1:p:41-59
    DOI: 10.1007/s12197-014-9291-5
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s12197-014-9291-5
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s12197-014-9291-5?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. 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.
    2. Robert A. Levy, 1967. "Relative Strength As A Criterion For Investment Selection," Journal of Finance, American Finance Association, vol. 22(4), pages 595-610, December.
    3. de Groot, Wilma & Huij, Joop & Zhou, Weili, 2012. "Another look at trading costs and short-term reversal profits," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 371-382.
    4. Oberlechner, Thomas, 2001. "Importance of Technical and Fundamental Analysis in the European Foreign Exchange Market," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 6(1), pages 81-93, January.
    5. Isakov, D. & Hollistein, M., 1998. "Application of Simple Technical Trading Rules to Swiss Stock Prices: Is It Profitable?," Papers 98.2, Ecole des Hautes Etudes Commerciales, Universite de Geneve-.
    6. Jensen, Michael C & Bennington, George A, 1970. "Random Walks and Technical Theories: Some Additional Evidence," Journal of Finance, American Finance Association, vol. 25(2), pages 469-482, May.
    7. James, F. E., 1968. "Monthly Moving Averages—An Effective Investment Tool?*," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 3(3), pages 315-326, September.
    8. Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October.
    9. LeBaron, B., 1992. "Do Moving Average Trading Rule Results Imply Nonlinearites in Foreign Exchange Markets?," Working papers 9222, Wisconsin Madison - Social Systems.
    10. Ellis, Craig A. & Parbery, Simon A., 2005. "Is smarter better? A comparison of adaptive, and simple moving average trading strategies," Research in International Business and Finance, Elsevier, vol. 19(3), pages 399-411, September.
    11. Menkhoff, Lukas, 1997. "Examining the Use of Technical Currency Analysis," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 2(4), pages 307-318, October.
    12. Taylor, Mark P. & Allen, Helen, 1992. "The use of technical analysis in the foreign exchange market," Journal of International Money and Finance, Elsevier, vol. 11(3), pages 304-314, June.
    13. Cheung, Yin-Wong & Wong, Clement Yuk-Pang, 2000. "A survey of market practitioners' views on exchange rate dynamics," Journal of International Economics, Elsevier, vol. 51(2), pages 401-419, August.
    14. 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.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. Stephan Schulmeister, 2009. "Profitability of technical stock trading: Has it moved from daily to intraday data?," Review of Financial Economics, John Wiley & Sons, vol. 18(4), pages 190-201, October.
    3. Ellis, Craig A. & Parbery, Simon A., 2005. "Is smarter better? A comparison of adaptive, and simple moving average trading strategies," Research in International Business and Finance, Elsevier, vol. 19(3), pages 399-411, September.
    4. Stephan Schulmeister, 2009. "Technical Trading and Trends in the Dollar-Euro Exchange Rate," WIFO Studies, WIFO, number 37582.
    5. Farias Nazário, Rodolfo Toríbio & e Silva, Jéssica Lima & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2017. "A literature review of technical analysis on stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 115-126.
    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. Kevin Rink, 2023. "The predictive ability of technical trading rules: an empirical analysis of developed and emerging equity markets," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 37(4), pages 403-456, December.
    8. Stephan Schulmeister, 2000. "Technical Analysis and Exchange Rate Dynamics," WIFO Studies, WIFO, number 25857.
    9. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, March.
    10. Jin, Xiaoye, 2021. "What do we know about the popularity of technical analysis in foreign exchange markets? A skewness preference perspective," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    11. 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.
    12. Taylor, Mark & Hsu, Po-Hsuan, 2014. "Forty Years, Thirty Currencies and 21,000 Trading Rules: A Large-scale, Data-Snooping Robust Analysis of Technical Trading in t," CEPR Discussion Papers 10018, C.E.P.R. Discussion Papers.
    13. Mr. Christiane Nickel & Mr. Günter Schmidt & Mr. Georg Stadtmann & Mr. Michael Frenkel, 2001. "The Effects of Capital Controls on Exchange Rate Volatility and Output," IMF Working Papers 2001/187, International Monetary Fund.
    14. Schulmeister, Stephan, 2006. "The interaction between technical currency trading and exchange rate fluctuations," Finance Research Letters, Elsevier, vol. 3(3), pages 212-233, September.
    15. M. Frenkel & C. Pierdzionc & G. Stadtmann, 2001. "The foreign exchange market interventions of the European Central Bank," BNL Quarterly Review, Banca Nazionale del Lavoro, vol. 54(218), pages 249-287.
    16. Urquhart, Andrew & Gebka, Bartosz & Hudson, Robert, 2015. "How exactly do markets adapt? Evidence from the moving average rule in three developed markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 127-147.
    17. Gunasekarage, Abeyratna & Power, David M., 2001. "The profitability of moving average trading rules in South Asian stock markets," Emerging Markets Review, Elsevier, vol. 2(1), pages 17-33, March.
    18. Hassanniakalager, Arman & Sermpinis, Georgios & Stasinakis, Charalampos, 2021. "Trading the foreign exchange market with technical analysis and Bayesian Statistics," Journal of Empirical Finance, Elsevier, vol. 63(C), pages 230-251.
    19. Gehrig, Thomas & Menkhoff, Lukas, 2003. "Technical Analysis in Foreign Exchange - The Workhorse Gains Further Ground," Hannover Economic Papers (HEP) dp-278, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    20. Stephan Schulmeister, 2007. "The Interaction Between the Aggregate Behaviour of Technical Trading Systems and Stock Price Dynamics," WIFO Working Papers 290, WIFO.

    More about this item

    Keywords

    Technical trading; Finance; Trading strategies; G17;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    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:spr:jecfin:v:40:y:2016:i:1:p:41-59. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.