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Commodity Channel Index: Evaluation of Trading Rule of Agricultural Commodities

Author

Listed:
  • Mansoor Maitah

    (Department of Economics, Faculty of Economics and Management, Czech University of Life Sciences, Prague, Czech Republic,)

  • Petr Prochazka

    (Department of Economics, Faculty of Economics and Management, Czech University of Life Sciences, Prague, Czech Republic,)

  • Michal Cermak

    (Department of Economics, Faculty of Economics and Management, Czech University of Life Sciences, Prague, Czech Republic,)

  • Karel r dl

    (Department of Economic Theories, Faculty of Economics and Management, Czech University of Life Sciences, Prague, Czech Republic)

Abstract

This paper is focused on evaluating the trading rule of indicator commodity channel index (CCI), using selected agricultural commodities. The reason of testing is that this indicator is calculated with respect to fluctuation of commodity market - volatility. The recent issue of commodity markets examines trading under risk. The concept is in analyzing of predictive power of CCI. The main core of this paper is if the trading strategy under evaluating using technical analysis, respectively CCI, reaches positive profit. The returns of trading rule are measured using signals to buying or selling and comparison each of them. Authors of this paper created trading rule based on CCI and tested it on commodity markets. The results are positive in term of % CCI. Findings of the strategy are positive due to measurement volatility involved in indicator. The commodity markets are volatile, time series are fluctuating due to actual announcements or news.

Suggested Citation

  • Mansoor Maitah & Petr Prochazka & Michal Cermak & Karel r dl, 2016. "Commodity Channel Index: Evaluation of Trading Rule of Agricultural Commodities," International Journal of Economics and Financial Issues, International Journal of Economics and Financial Issues, vol. 6(1), pages 176-178.
    • Handle: RePEc:eco:journ1:2016-01-23
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    References listed on IDEAS

    as
    1. Chang, P H Kevin & Osler, Carol L, 1999. "Methodical Madness: Technical Analysis and the Irrationality of Exchange-Rate Forecasts," Economic Journal, Royal Economic Society, vol. 109(458), pages 636-661, October.
    2. Christopher J. Neely, 1997. "Technical analysis in the foreign exchange market: a layman's guide," Review, Federal Reserve Bank of St. Louis, issue Sep, pages 23-38.
    3. Marilyne Huchet-Bourdon, 2011. "Agricultural Commodity Price Volatility: An Overview," OECD Food, Agriculture and Fisheries Papers 52, OECD Publishing.
    4. Svatos, Miroslav & Smutka, Lubos & Smutka, B.A.M. & Mousbah, S.A.A., . "Development of Visegrad Countries’ Agricultural Trade in Relation to Agricultural Production Development," AGRIS on-line Papers in Economics and Informatics, Czech University of Life Sciences Prague, Faculty of Economics and Management, vol. 5(01), pages 1-11.
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    Citations

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    Cited by:

    1. Hongyang Yang & Xiao-Yang Liu & Shan Zhong & Anwar Walid, 2025. "Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy," Papers 2511.12120, arXiv.org.
    2. Aurthur Vimalachandran Thomas Jayachandran, 2022. "The financial crash of 2020 and the retail trader’s boon: a correlation between sentiment and technical analysis," SN Business & Economics, Springer, vol. 2(6), pages 1-8, June.
    3. Pick-Soon Ling & Ruzita Abdul-Rahim, 2017. "Market Efficiency Based on Unconventional Technical Trading Strategies in Malaysian Stock Market," International Journal of Economics and Financial Issues, International Journal of Economics and Financial Issues, vol. 7(3), pages 88-96.
    4. Tidor-Vlad Pricope, 2021. "Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review," Papers 2106.00123, arXiv.org.
    5. Narongsak Sukma & Chakkrit Snae Namahoot, 2025. "Enhancing Trading Strategies: A Multi-indicator Analysis for Profitable Algorithmic Trading," Computational Economics, Springer;Society for Computational Economics, vol. 65(6), pages 3807-3840, June.

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    More about this item

    Keywords

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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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