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Success and Failure of Technical Trading Strategies in the Cocoa Futures Market

Author

Listed:
  • Gerwin Griffioen

    (CeNDEF, University of Amsterdam)

  • Peter Boswijk
  • Cars Hommes

    (CeNDEF, University of Amsterdam)

Abstract

A large set of 5350 trend following technical trading rules is applied to LIFFE and CSCE cocoa futures prices, and to the Pound-Dollar exchange rate, in the period 1983:1 - 1997:6. We find that 72% of the trading rules generate positive profits, even when correcting for transaction and borrowing costs, when applied to the LIFFE cocoa futures prices. Moreover, a large set of trading rules exhibits statistically significant forecasting power of the LIFFE cocoa futures series. On the other hand the same set of strategies performs poor on the CSCE cocoa futures prices, with only 18% generating positive net profits and hardly any statistically significant forecasting power. The large difference in the performance of technical trading may be attributed to a combination of the demand/supply mechanism in the cocoa market and an accidental influence of the Pound-Dollar exchange rate, reinforcing trends in the LIFFE cocoa futures but weakening trends in the CSCE cocoa futures. Our case study suggests a connection between the success and failure of technical trading and the relative magnitudes of trend and volatility of the underlying series.

Suggested Citation

  • Gerwin Griffioen & Peter Boswijk & Cars Hommes, 2001. "Success and Failure of Technical Trading Strategies in the Cocoa Futures Market," CeNDEF Workshop Papers, January 2001 4A.4, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    • Handle: RePEc:ams:cdws01:4a.4
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    Cited by:

    1. Chiarella, Carl & He, Xue-Zhong & Hommes, Cars, 2006. "A dynamic analysis of moving average rules," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1729-1753.
    2. He, Xue-Zhong & Zheng, Min, 2010. "Dynamics of moving average rules in a continuous-time financial market model," Journal of Economic Behavior & Organization, Elsevier, vol. 76(3), pages 615-634, December.
    3. Guanqing Liu, 2019. "Technical Trading Behaviour: Evidence from Chinese Rebar Futures Market," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 669-704, August.
    4. 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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