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Technical trading revisited: False discoveries, persistence tests, and transaction costs

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  • Bajgrowicz, Pierre
  • Scaillet, Olivier

Abstract

We revisit the apparent historical success of technical trading rules on daily prices of the Dow Jones Industrial Average index from 1897 to 2011, and we use the false discovery rate (FDR) as a new approach to data snooping. The advantage of the FDR over existing methods is that it selects more outperforming rules, which allows diversifying against model uncertainty. Persistence tests show that, even with the more powerful FDR technique, an investor would never have been able to select ex ante the future best-performing rules. Moreover, even in-sample, the performance is completely offset by the introduction of low transaction costs. Overall, our results seriously call into question the economic value of technical trading rules that has been reported for early periods.

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  • 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.
  • Handle: RePEc:eee:jfinec:v:106:y:2012:i:3:p:473-491
    DOI: 10.1016/j.jfineco.2012.06.001
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    5. Patrick Gagliardini & Elisa Ossola & Olivier Scaillet, 2016. "Time‐Varying Risk Premium in Large Cross‐Sectional Equity Data Sets," Econometrica, Econometric Society, vol. 84, pages 985-1046, May.
    6. 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.
    7. Ana Lorena Jiménez-Preciado & Salvador Cruz-Aké & Francisco Venegas-Martínez, 2017. "Persistency of Price Patterns in the International Oil Industry, 2001-2016," International Journal of Energy Economics and Policy, Econjournals, vol. 7(1), pages 9-18.
    8. Gebka, Bartosz & Hudson, Robert S. & Atanasova, Christina V., 2015. "The benefits of combining seasonal anomalies and technical trading rules," Finance Research Letters, Elsevier, vol. 14(C), pages 36-44.
    9. Olivier Scaillet & Adrien Treccani & Christopher Trevisan, 2017. "High-Frequency Jump Analysis of the Bitcoin Market," Papers 1704.08175, arXiv.org, revised Jun 2017.
    10. 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.
    11. Campbell R. Harvey & Yan Liu & Heqing Zhu, 2014. ". . . and the Cross-Section of Expected Returns," NBER Working Papers 20592, National Bureau of Economic Research, Inc.
    12. 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.
    13. 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.
    14. Kong Xin-Bing & Xu Qin-Feng, 2015. "On False Discovery and Non-discovery Proportions of the Dynamic Adaptive Procedure," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 530-544, June.
    15. Roy L. Hayes & Jingwei Wu & Ruijra Chaysiri & Jean Bae & Peter A. Beling & William T. 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.
    16. Hsu, Po-Hsuan & Taylor, Mark P, 2014. "Forty Years, Thirty Currencies and 21,000 Trading Rules: A Large-scale, Data-Snooping Robust Analysis of Technical Trading in the Foreign Exchange Market," CEPR Discussion Papers 10018, C.E.P.R. Discussion Papers.
    17. 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.
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    23. Chen, Chien-Hua & Su, Xuan-Qi & Lin, Jun-Biao, 2016. "The role of information uncertainty in moving-average technical analysis: A study of individual stock-option issuance in Taiwan," Finance Research Letters, Elsevier, vol. 18(C), pages 263-272.

    More about this item

    Keywords

    Technical trading; False discovery rate; Persistence; Transaction costs;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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