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Technical Analysis and Discrete False Discovery Rate: Evidence from MSCI Indices

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  • Georgios Sermpinis
  • Arman Hassanniakalager
  • Charalampos Stasinakis
  • Ioannis Psaradellis

Abstract

We investigate the performance of dynamic portfolios constructed using more than 21,000 technical trading rules on 12 categorical and country-specific markets over the 2004-2015 study period, on rolling forward structures of different lengths. We also introduce a discrete false discovery rate (DFRD+/-) method for controlling data snooping bias. Compared to the existing methods, DFRD+/- is adaptive and more powerful, and accommodates for discrete p-values. The profitability, persistence and robustness of the technical rules are examined. Technical analysis still has short-term value in advanced, emerging and frontier markets. Financial stress, the economic environment and market development seem to affect the performance of trading rules. A cross-validation exercise highlights the importance of frequent rebalancing and the variability of profitability in trading with technical analysis.

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  • Georgios Sermpinis & Arman Hassanniakalager & Charalampos Stasinakis & Ioannis Psaradellis, 2018. "Technical Analysis and Discrete False Discovery Rate: Evidence from MSCI Indices," Papers 1811.06766, arXiv.org, revised Jun 2019.
  • Handle: RePEc:arx:papers:1811.06766
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    References listed on IDEAS

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    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. Joseph P. Romano & Michael Wolf, 2005. "Stepwise Multiple Testing as Formalized Data Snooping," Econometrica, Econometric Society, vol. 73(4), pages 1237-1282, July.
    3. Romano, Joseph P. & Shaikh, Azeem M. & Wolf, Michael, 2008. "Formalized Data Snooping Based On Generalized Error Rates," Econometric Theory, Cambridge University Press, vol. 24(2), pages 404-447, April.
    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. Laurent Barras & Olivier Scaillet & Russ Wermers, 2010. "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Journal of Finance, American Finance Association, vol. 65(1), pages 179-216, February.
    6. Taylor, Nick, 2014. "The rise and fall of technical trading rule success," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 286-302.
    7. Kun Liang & Dan Nettleton, 2012. "Adaptive and dynamic adaptive procedures for false discovery rate control and estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 74(1), pages 163-182, January.
    8. Bena, Jan & Ferreira, Miguel A & Matos, Pedro & Pires, Pedro, 2017. "Are foreign investors locusts? The long-term effects of foreign institutional ownership," Journal of Financial Economics, Elsevier, vol. 126(1), pages 122-146.
    9. Demirguc-Kunt, Ash & Levine, Ross, 1996. "Stock Market Development and Financial Intermediaries: Stylized Facts," The World Bank Economic Review, World Bank, vol. 10(2), pages 291-321, May.
    10. John D. Storey & Jonathan E. Taylor & David Siegmund, 2004. "Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 187-205, February.
    11. Kun Liang, 2016. "False discovery rate estimation for large-scale homogeneous discrete p-values," Biometrics, The International Biometric Society, vol. 72(2), pages 639-648, June.
    12. 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.
    13. Genovese, Christopher R. & Wasserman, Larry, 2006. "Exceedance Control of the False Discovery Proportion," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1408-1417, December.
    14. Elena Kulinskaya & Alex Lewin, 2009. "On fuzzy familywise error rate and false discovery rate procedures for discrete distributions," Biometrika, Biometrika Trust, vol. 96(1), pages 201-211.
    15. Cesari, Riccardo & Cremonini, David, 2003. "Benchmarking, portfolio insurance and technical analysis: a Monte Carlo comparison of dynamic strategies of asset allocation," Journal of Economic Dynamics and Control, Elsevier, vol. 27(6), pages 987-1011, April.
    16. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
    17. 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.
    18. 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.
    19. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    20. Jianqing Fan & Xu Han, 2017. "Estimation of the false discovery proportion with unknown dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1143-1164, September.
    21. Yoav Benjamini, 2010. "Discovering the false discovery rate," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 405-416, September.
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