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Technical trading rules in the European Monetary System

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  • Neely, Christopher J.
  • Weller, Paul A.

Abstract

Using the genetic programming methodology developed in Neely, Weller and Dittmar (1997), we find trading rules that generate significant excess returns for three of four EMS exchange rates over the out-of-sample period 1986-1996. Permitting the rules to use information about the interest rate differential proved to be important. The reduction in volatility resulting from the imposition of a narrower band may reduce trading rule profitability. The currency for which there was least evidence of significant excess returns was the Dutch guilder, which was also the only currency that remained within a band of 2.25% throughout our sample period. Our results cannot be duplicated by the moving average or filter rules commonly used by technical analysts or by two trading rules designed specifically to exploit known features of target zone exchange rates. The observed excess returns cannot be explained as compensation for bearing systematic risk.
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Suggested Citation

  • 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.
  • Handle: RePEc:eee:jimfin:v:18:y:1999:i:3:p:429-458
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    References listed on IDEAS

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    1. Sweeney, Richard J, 1986. " Beating the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 41(1), pages 163-182, March.
    2. Lee, Chun I. & Mathur, Ike, 1996. "Trading rule profits in european currency spot cross-rates," Journal of Banking & Finance, Elsevier, vol. 20(5), pages 949-962, June.
    3. Levich, Richard M. & Thomas, Lee III, 1993. "The significance of technical trading-rule profits in the foreign exchange market: a bootstrap approach," Journal of International Money and Finance, Elsevier, vol. 12(5), pages 451-474, October.
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    5. Neely, Christopher & Weller, Paul & Dittmar, Rob, 1997. "Is Technical Analysis in the Foreign Exchange Market Profitable? A Genetic Programming Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, pages 405-426.
    6. Krugman, Paul & Miller, Marcus, 1993. "Why have a target zone?," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 38(1), pages 279-314, June.
    7. Neely, Christopher J., 1999. "Target zones and conditional volatility: The role of realignments," Journal of Empirical Finance, Elsevier, pages 177-192.
    8. Hodrick, Robert J. & Srivastava, Sanjay, 1986. "The covariation of risk premiums and expected future spot exchange rates," Journal of International Money and Finance, Elsevier, vol. 5(1, Supple), pages 5-21, March.
    9. Neely, Christopher J. & Weller, Paul A., 2001. "Technical analysis and central bank intervention," Journal of International Money and Finance, Elsevier, pages 949-970.
    10. repec:fth:pennfi:70 is not listed on IDEAS
    11. 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.
    12. Franklin Allen & Risto Karjalainen, "undated". "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.
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