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Using Genetic Algorithms to Find Technical Trading Rules (Revised: 20-95)

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  • Franklin Allen
  • Risto Karjalainen

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Bibliographic Info

Paper provided by Wharton School Rodney L. White Center for Financial Research in its series Rodney L. White Center for Financial Research Working Papers with number 20-93.

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Handle: RePEc:fth:pennfi:20-93

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Cited by:
  1. Spyros Skouras, 1998. "Financial Returns and Efficiency as seen by an Artificial Technical Analyst," Finance 9808001, EconWPA, revised 24 Aug 1998.
  2. Neely, Christopher J. & Weller, Paul A., 1999. "Technical trading rules in the European Monetary System," Journal of International Money and Finance, Elsevier, vol. 18(3), pages 429-458.
  3. Barbara Summers & Evan Griffiths & Robert Hudson, 2004. "Back to the future: an empirical investigation into the validity of stock index models over time," Applied Financial Economics, Taylor & Francis Journals, vol. 14(3), pages 209-214.
  4. Paul Weller & Christopher Neely, 1999. "Technical Analysis and Central Bank Intervention," Working Papers wp99-04, Warwick Business School, Finance Group.
  5. NUĂ‘EZ, Laura, 2002. "An analysis of the robustness of Genetic Algorithm (GA) methodology in the design of trading systems for the Stock Exchange," Computing in Economics and Finance 2002 29, Society for Computational Economics.
  6. N. K. Chidambaran & Chi-Wen Jevons Lee & Joaguin R. Trigueros, 1998. "An Adaptive Evolutionary Approach to Option Pricing via Genetic Programming," New York University, Leonard N. Stern School Finance Department Working Paper Seires 98-086, New York University, Leonard N. Stern School of Business-.
  7. Colin Fyfe & John Paul Marney & Heather Tarbert, 2005. "Risk adjusted returns from technical trading: a genetic programming approach," Applied Financial Economics, Taylor & Francis Journals, vol. 15(15), pages 1073-1077.

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