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Risk adjusted returns from technical trading: a genetic programming approach

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  • Colin Fyfe
  • John Paul Marney
  • Heather Tarbert
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    Abstract

    In this study, Genetic Programming is used to generate technical trading rules. These are assessed in terms of their basic returns and their risk adjusted returns. It is found that while the basic returns are impressive by comparison with buy and hold, they do not outperform buy and hold after risk-adjustment.

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    File URL: http://www.tandfonline.com/doi/abs/10.1080/09603100500306709
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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

    Volume (Year): 15 (2005)
    Issue (Month): 15 ()
    Pages: 1073-1077

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    Handle: RePEc:taf:apfiec:v:15:y:2005:i:15:p:1073-1077

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    Web page: http://www.tandfonline.com/RAFE20

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    1. 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.
    2. C. H. Hommes, 2001. "Financial markets as nonlinear adaptive evolutionary systems," Quantitative Finance, Taylor & Francis Journals, vol. 1(1), pages 149-167.
    3. Boswijk, H.P., Griffioen, G.A.W., Hommes, C.H., 2001. "Succes and Failure of Technical Trading Strategies in the Cocoa Futures Market," Computing in Economics and Finance 2001 120, Society for Computational Economics.
    4. 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-.
    5. Christopher J. Neely & Paul A. Weller & Robert Dittmar, 1997. "Is technical analysis in the foreign exchange market profitable? a genetic programming approach," Working Papers 1996-006, Federal Reserve Bank of St. Louis.
    6. Hendrik Bessembinder & Kalok Chan, 1998. "Market Efficiency and the Returns to Technical Analysis," Financial Management, Financial Management Association, vol. 27(2), Summer.
    7. Brock, William A. & Hommes, Cars H., 1998. "Heterogeneous beliefs and routes to chaos in a simple asset pricing model," Journal of Economic Dynamics and Control, Elsevier, vol. 22(8-9), pages 1235-1274, August.
    8. Franklin Allen & Risto Karjalainen, . "Using Genetic Algorithms to Find Technical Trading Rules (Revised: 20-95)," Rodney L. White Center for Financial Research Working Papers 20-93, Wharton School Rodney L. White Center for Financial Research.
    9. Bessembinder, Hendrik & Chan, Kalok, 1995. "The profitability of technical trading rules in the Asian stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 3(2-3), pages 257-284, July.
    10. Peter Boswijk & Gerwin Griffioen & Cars Hommes, 2001. "Success and Failure of Technical Trading Strategies in the Cocoa Futures Market," Tinbergen Institute Discussion Papers 01-016/1, Tinbergen Institute.
    11. repec:att:wimass:9625 is not listed on IDEAS
    12. Routledge, Bryan R., 2001. "Genetic Algorithm Learning To Choose And Use Information," Macroeconomic Dynamics, Cambridge University Press, vol. 5(02), pages 303-325, April.
    13. Colin Fyfe & John Paul Marney & Heather Tarbert, 1999. "Technical analysis versus market efficiency - a genetic programming approach," Applied Financial Economics, Taylor & Francis Journals, vol. 9(2), pages 183-191.
    14. W. Brian Arthur & John H. Holland & Blake LeBaron & Richard Palmer & Paul Taylor, 1996. "Asset Pricing Under Endogenous Expectation in an Artificial Stock Market," Working Papers 96-12-093, Santa Fe Institute.
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