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Research on futures trend trading strategy based on short term chart pattern

Author

Listed:
  • Saulius Masteika
  • Aleksandras Vytautas Rutkauskas

Abstract

The main task of this paper is to examine a short term trend trading strategy in futures market based on chart pattern recognition, time series and computational analysis. Specifications of historical data for technical analysis and equations for futures profitability calculations together with position size measurement are also discussed in the paper. A contribution of this paper lies in a novel chart pattern related to fractal formation and chaos theory and its application to short term up-trend trading. Trading strategy was tested with historical data of the most active futures contracts. The results have given significantly better and stable returns compared to the change of market benchmark (CRB index). The results of experimental research related to the size of trading portfolio and trade execution slippage are also discussed in the paper. The proposed strategy can be attractive for futures market participants and be applied as a decision support tool in technical analysis.

Suggested Citation

  • Saulius Masteika & Aleksandras Vytautas Rutkauskas, 2012. "Research on futures trend trading strategy based on short term chart pattern," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 13(5), pages 915-930, June.
    • Handle: RePEc:taf:jbemgt:v:13:y:2012:i:5:p:915-930
    DOI: 10.3846/16111699.2012.705252
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    Citations

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    Cited by:

    1. Yensen Ni & Min-Yuh Day & Yirung Cheng & Paoyu Huang, 2022. "Can investors profit by utilizing technical trading strategies? Evidence from the Korean and Chinese stock markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-21, December.
    2. Jacinta Chan Phooi M'ng & Azmin Azliza Aziz, 2016. "Using Neural Networks to Enhance Technical Trading Rule Returns: A Case with KLCI," Athens Journal of Business & Economics, Athens Institute for Education and Research (ATINER), vol. 2(1), pages 63-70, January.
    3. Yarovaya, Larisa & Brzeszczyński, Janusz & Lau, Chi Keung Marco, 2017. "Asymmetry in spillover effects: Evidence for international stock index futures markets," International Review of Financial Analysis, Elsevier, vol. 53(C), pages 94-111.
    4. Min-Yuh Day & Yensen Ni & Chinning Hsu & Paoyu Huang, 2022. "Do Investment Strategies Matter for Trading Global Clean Energy and Global Energy ETFs?," Energies, MDPI, vol. 15(9), pages 1-15, May.
    5. Chien-Liang Chiu & Paoyu Huang & Min-Yuh Day & Yensen Ni & Yuhsin Chen, 2024. "Mastery of “Monthly Effects”: Big Data Insights into Contrarian Strategies for DJI 30 and NDX 100 Stocks over a Two-Decade Period," Mathematics, MDPI, vol. 12(2), pages 1-21, January.
    6. Day, Min-Yuh & Ni, Yensen, 2023. "The profitability of seasonal trading timing: Insights from energy-related markets," Energy Economics, Elsevier, vol. 128(C).
    7. Yensen Ni & Yirung Cheng & Yulu Liao & Paoyu Huang, 2022. "Does board structure affect stock price overshooting informativeness measured by stochastic oscillator indicators?," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2290-2302, April.

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