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

  • Bajgrowicz, Pierre
  • Scaillet, Olivier

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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Article provided by Elsevier in its journal Journal of Financial Economics.

Volume (Year): 106 (2012)
Issue (Month): 3 ()
Pages: 473-491

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Handle: RePEc:eee:jfinec:v:106:y:2012:i:3:p:473-491
Contact details of provider: Web page: http://www.elsevier.com/locate/inca/505576

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  1. Dittmar, Robert & Neely, Christopher J & Weller, Paul, 1996. "Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach," CEPR Discussion Papers 1480, C.E.P.R. Discussion Papers.
  2. Joseph P. Romano & Michael Wolf, 2005. "Stepwise Multiple Testing as Formalized Data Snooping," Econometrica, Econometric Society, vol. 73(4), pages 1237-1282, 07.
  3. Evan Gatev & William N. Goetzmann & K. Geert Rouwenhorst, 2006. "Pairs Trading: Performance of a Relative-Value Arbitrage Rule," Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 797-827.
  4. 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 1731-64, December.
  5. Po-Hsuan Hsu & Chung-Ming Kuan, 2005. "Reexamining the Profitability of Technical Analysis with Data Snooping Checks," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 3(4), pages 606-628.
  6. 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.
  7. Laurent BARRAS & Olivier SCAILLET & Russ WERMERS, 2005. "False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas," Swiss Finance Institute Research Paper Series 08-18, Swiss Finance Institute, revised Sep 2008.
  8. Romano, Joseph P. & Shaikh, Azeem M. & Wolf, Michael, 2008. "Formalized Data Snooping Based On Generalized Error Rates," Econometric Theory, Cambridge University Press, vol. 24(02), pages 404-447, April.
  9. Andrew W. Lo & A. Craig MacKinlay, 1989. "Data-Snooping Biases in Tests of Financial Asset Pricing Models," NBER Working Papers 3001, National Bureau of Economic Research, Inc.
  10. Carhart, Mark M, 1997. " On Persistence in Mutual Fund Performance," Journal of Finance, American Finance Association, vol. 52(1), pages 57-82, March.
  11. William N. Goetzmann & Stephen J. Brown, 2005. "Performance Persistence," Yale School of Management Working Papers ysm451, Yale School of Management.
  12. Kenneth A. Kavajecz, 2004. "Technical Analysis and Liquidity Provision," Review of Financial Studies, Society for Financial Studies, vol. 17(4), pages 1043-1071.
  13. Brennan, Michael J. & Schwartz, Eduardo S. & Lagnado, Ronald, 1997. "Strategic asset allocation," Journal of Economic Dynamics and Control, Elsevier, vol. 21(8-9), pages 1377-1403, June.
  14. Jones, Charles M. & Lamont, Owen A., 2002. "Short-sale constraints and stock returns," Journal of Financial Economics, Elsevier, vol. 66(2-3), pages 207-239.
  15. Joseph P. Romano & Azeem M. Shaikh & Michael Wolf, 2008. "Control of the False Discovery Rate under Dependence using the Bootstrap and Subsampling," IEW - Working Papers 337, Institute for Empirical Research in Economics - University of Zurich.
  16. Blanchet-Scalliet, Christophette & Diop, Awa & Gibson, Rajna & Talay, Denis & Tanre, Etienne, 2007. "Technical analysis compared to mathematical models based methods under parameters mis-specification," Journal of Banking & Finance, Elsevier, vol. 31(5), pages 1351-1373, May.
  17. 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.
  18. Wei Huang & Qianqiu Liu & S. Ghon Rhee & Liang Zhang, 2010. "Return Reversals, Idiosyncratic Risk, and Expected Returns," Review of Financial Studies, Society for Financial Studies, vol. 23(1), pages 147-168, January.
  19. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1770, 08.
  20. 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.
  21. Sullivan, Ryan & Timmermann, Allan G & White, Halbert, 1998. "Data-Snooping, Technical Trading Rule Performance and the Bootstrap," CEPR Discussion Papers 1976, C.E.P.R. Discussion Papers.
  22. Graham Elliott & Allan Timmermann, 2008. "Economic Forecasting," Journal of Economic Literature, American Economic Association, vol. 46(1), pages 3-56, March.
  23. Stephen J. Brown & William N. Goetzmann & Alok Kumar, 1998. "The Dow Theory: William Peter Hamilton's Track Record Re-Considered," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-013, New York University, Leonard N. Stern School of Business-.
  24. Kosowski, Robert & Naik, Narayan Y. & Teo, Melvyn, 2007. "Do hedge funds deliver alpha? A Bayesian and bootstrap analysis," Journal of Financial Economics, Elsevier, vol. 84(1), pages 229-264, April.
  25. Paul A. Gompers & Andrew Metrick, . "Institutional Investors and Equity Prices," Rodney L. White Center for Financial Research Working Papers 20-99, Wharton School Rodney L. White Center for Financial Research.
  26. Duffie, Darrell & Garleanu, Nicolae & Pedersen, Lasse Heje, 2002. "Securities lending, shorting, and pricing," Journal of Financial Economics, Elsevier, vol. 66(2-3), pages 307-339.
  27. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
  28. Stoll, Hans R. & Whaley, Robert E., 1983. "Transaction costs and the small firm effect," Journal of Financial Economics, Elsevier, vol. 12(1), pages 57-79, June.
  29. Joseph Romano & Azeem Shaikh & Michael Wolf, 2008. "Rejoinder on: Control of the false discovery rate under dependence using the bootstrap and subsampling," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 17(3), pages 461-471, November.
  30. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
  31. 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.
  32. Hendrik Bessembinder & Kalok Chan, 1998. "Market Efficiency and the Returns to Technical Analysis," Financial Management, Financial Management Association, vol. 27(2), Summer.
  33. Keim, Donald B. & Madhavan, Ananth, 1997. "Transactions costs and investment style: an inter-exchange analysis of institutional equity trades," Journal of Financial Economics, Elsevier, vol. 46(3), pages 265-292, December.
  34. Neftci, Salih N, 1991. "Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of "Technical Analysis."," The Journal of Business, University of Chicago Press, vol. 64(4), pages 549-71, October.
  35. Eric Jondeau & Michael Rockinger, 2006. "Optimal Portfolio Allocation under Higher Moments," European Financial Management, European Financial Management Association, vol. 12(1), pages 29-55.
  36. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
  37. Jegadeesh, Narasimhan, 1990. " Evidence of Predictable Behavior of Security Returns," Journal of Finance, American Finance Association, vol. 45(3), pages 881-98, July.
  38. Fung, William & Hsieh, David A, 1997. "Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds," Review of Financial Studies, Society for Financial Studies, vol. 10(2), pages 275-302.
  39. Efstathios Paparoditis & Dimitris N. Politis, 2003. "Residual-Based Block Bootstrap for Unit Root Testing," Econometrica, Econometric Society, vol. 71(3), pages 813-855, 05.
  40. Mark J Ready, 2002. "Profits from Technical Trading Rules," Financial Management, Financial Management Association, vol. 31(3), Fall.
  41. Alessio Farcomeni, 2007. "Some Results on the Control of the False Discovery Rate under Dependence," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(2), pages 275-297.
  42. Benjamin M. Friedman, 1995. "Economic Implications of Changing Share Ownership," NBER Working Papers 5141, National Bureau of Economic Research, Inc.
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