IDEAS home Printed from https://ideas.repec.org/a/eee/econom/v169y2012i2p301-309.html
   My bibliography  Save this article

Spurious regressions in technical trading

Author

Listed:
  • Shintani, Mototsugu
  • Yabu, Tomoyoshi
  • Nagakura, Daisuke

Abstract

This paper investigates the spurious effect in forecasting asset returns when signals from technical trading rules are used as predictors. Against economic intuition, the simulation result shows that, even if past information has no predictive power, buy or sell signals based on the difference between the short-period and long-period moving averages of past asset prices can be statistically significant when the forecast horizon is relatively long. The theoretical analysis reveals that both ‘momentum’ and ‘contrarian’ strategies can be falsely supported, while the probability of obtaining each result depends on the type of the test statistics employed.

Suggested Citation

  • Shintani, Mototsugu & Yabu, Tomoyoshi & Nagakura, Daisuke, 2012. "Spurious regressions in technical trading," Journal of Econometrics, Elsevier, vol. 169(2), pages 301-309.
    • Handle: RePEc:eee:econom:v:169:y:2012:i:2:p:301-309
    DOI: 10.1016/j.jeconom.2012.01.019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304407612000292
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jeconom.2012.01.019?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. 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.
    2. LeBaron, Blake, 1999. "Technical trading rule profitability and foreign exchange intervention," Journal of International Economics, Elsevier, vol. 49(1), pages 125-143, October.
    3. Sun, Yixiao, 2004. "A CONVERGENT t-STATISTIC IN SPURIOUS REGRESSIONS," Econometric Theory, Cambridge University Press, vol. 20(5), pages 943-962, October.
    4. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
    5. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    6. Richardson, Matthew & Stock, James H., 1989. "Drawing inferences from statistics based on multiyear asset returns," Journal of Financial Economics, Elsevier, vol. 25(2), pages 323-348, December.
    7. Park, Joon Y. & Phillips, Peter C.B., 1999. "Asymptotics For Nonlinear Transformations Of Integrated Time Series," Econometric Theory, Cambridge University Press, vol. 15(3), pages 269-298, June.
    8. 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.
    9. Cheung, Yin-Wong & Chinn, Menzie David, 2001. "Currency traders and exchange rate dynamics: a survey of the US market," Journal of International Money and Finance, Elsevier, vol. 20(4), pages 439-471, August.
    10. Mototsugu Shintani & Tomoyoshi Yabu & Daisuke Nagakura, 2008. "Spurious Regressions in Technical Trading: Momentum or Contrarian?," IMES Discussion Paper Series 08-E-09, Institute for Monetary and Economic Studies, Bank of Japan.
    11. 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.
    12. Valkanov, Rossen, 2003. "Long-horizon regressions: theoretical results and applications," Journal of Financial Economics, Elsevier, vol. 68(2), pages 201-232, May.
    13. 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)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ni, Yensen & Liao, Yi-Ching & Huang, Paoyu, 2015. "MA trading rules, herding behaviors, and stock market overreaction," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 253-265.
    2. Huang, Paoyu & Ni, Yensen, 2017. "Board structure and stock price informativeness in terms of moving average rules," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 161-169.
    3. Ni, Yensen & Day, Min-Yuh & Huang, Paoyu & Yu, Shang-Ru, 2020. "The profitability of Bollinger Bands: Evidence from the constituent stocks of Taiwan 50," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    4. Keiichi Goshima & Hiroshi Ishijima & Mototsugu Shintani & Hiroki Yamamoto, 2019. "Forecasting Japanese inflation with a news-based leading indicator of economic activities," CARF F-Series CARF-F-458, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    5. Min-Yuh Day & Yensen Ni & Chinning Hsu & Paoyu Huang, 2022. "Do Investment Strategies Matter for Trading Global Clean Energy and Global Energy ETFs?," Energies, MDPI, vol. 15(9), pages 1-15, May.

    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. Mototsugu Shintani & Tomoyoshi Yabu & Daisuke Nagakura, 2008. "Spurious Regressions in Technical Trading: Momentum or Contrarian?," IMES Discussion Paper Series 08-E-09, Institute for Monetary and Economic Studies, Bank of Japan.
    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. Stephan Schulmeister, 2009. "Technical Trading and Trends in the Dollar-Euro Exchange Rate," WIFO Studies, WIFO, number 37582.
    4. Frömmel, Michael & Lampaert, Kevin, 2016. "Does frequency matter for intraday technical trading?," Finance Research Letters, Elsevier, vol. 18(C), pages 177-183.
    5. Stephan Schulmeister, 2007. "The Interaction Between the Aggregate Behaviour of Technical Trading Systems and Stock Price Dynamics," WIFO Working Papers 290, WIFO.
    6. Neely, Christopher J. & Weller, Paul A. & Ulrich, Joshua M., 2009. "The Adaptive Markets Hypothesis: Evidence from the Foreign Exchange Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(2), pages 467-488, April.
    7. Yung-Ho Chang, 2019. "Cross-market information spillover and the performance of technical trading in the foreign exchange market," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 43(2), pages 211-227, April.
    8. 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.
    9. Hsu, Po-Hsuan & Taylor, Mark P. & Wang, Zigan, 2016. "Technical trading: Is it still beating the foreign exchange market?," Journal of International Economics, Elsevier, vol. 102(C), pages 188-208.
    10. Panopoulou, Ekaterini & Souropanis, Ioannis, 2019. "The role of technical indicators in exchange rate forecasting," Journal of Empirical Finance, Elsevier, vol. 53(C), pages 197-221.
    11. Fong, Tom Pak Wing & Wu, Shui Tang, 2020. "Predictability in sovereign bond returns using technical trading rules: Do developed and emerging markets differ?," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    12. Christopher J. Neely & Paul A. Weller, 2011. "Technical analysis in the foreign exchange market," Working Papers 2011-001, Federal Reserve Bank of St. Louis.
    13. 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.
    14. Souropanis, Ioannis & Vivian, Andrew, 2023. "Forecasting realized volatility with wavelet decomposition," Journal of Empirical Finance, Elsevier, vol. 74(C).
    15. de Zwart, Gerben & Markwat, Thijs & Swinkels, Laurens & van Dijk, Dick, 2009. "The economic value of fundamental and technical information in emerging currency markets," Journal of International Money and Finance, Elsevier, vol. 28(4), pages 581-604, June.
    16. Zarrabi, Nima & Snaith, Stuart & Coakley, Jerry, 2017. "FX technical trading rules can be profitable sometimes!," International Review of Financial Analysis, Elsevier, vol. 49(C), pages 113-127.
    17. 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.
    18. 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.
    19. Yamamoto, Ryuichi, 2012. "Intraday technical analysis of individual stocks on the Tokyo Stock Exchange," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 3033-3047.
    20. 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.

    More about this item

    Keywords

    Efficient market hypothesis; Nonstationary time series; Random walk; Technical analysis;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

    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:econom:v:169:y:2012:i:2:p:301-309. 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/jeconom .

    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.