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Effectiveness of simple technical trading rules in the Hong Kong futures markets

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  • Mahendra Raj
  • David Thurston

Abstract

Weak-form market efficiency states that past information cannot be used to consistently generate excess returns. So, technical analysis which uses past information on securities should not help generate abnormal profits consistently in a weak-form efficient market. In the present study, two simple technical trading strategies - Moving-Average-Oscillator and Trading Range Break-Out - are implemented to test whether they result in excess returns. The study is performed on the Hang Seng Futures Index, traded at the Hong Kong Futures Exchange. It is found that the moving average strategy does not produce significant excess returns, but four out of the six Trading Range Break-Out rules resulted in significant positive returns for the buy signal.

Suggested Citation

  • Mahendra Raj & David Thurston, 1996. "Effectiveness of simple technical trading rules in the Hong Kong futures markets," Applied Economics Letters, Taylor & Francis Journals, vol. 3(1), pages 33-36.
    • Handle: RePEc:taf:apeclt:v:3:y:1996:i:1:p:33-36
    DOI: 10.1080/758525512
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    Citations

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    Cited by:

    1. Pereira, Robert, 1999. "Forecasting Ability But No Profitability: An Empirical Evaluation of Genetic Algorithm-optimised Technical Trading Rules," MPRA Paper 9055, University Library of Munich, Germany.
    2. Fang, Jiali & Jacobsen, Ben & Qin, Yafeng, 2014. "Predictability of the simple technical trading rules: An out-of-sample test," Review of Financial Economics, Elsevier, vol. 23(1), pages 30-45.
    3. Noureddine Kouaissah & Amin Hocine, 2021. "Forecasting systemic risk in portfolio selection: The role of technical trading rules," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(4), pages 708-729, July.
    4. Ślepaczuk Robert & Sakowski Paweł & Zakrzewski Grzegorz, 2018. "Investment Strategies that Beat the Market. What Can We Squeeze from the Market?," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 14(4), pages 36-55, December.
    5. Elaine Y. L. Loh, 2007. "An alternative test for weak form efficiency based on technical analysis," Applied Financial Economics, Taylor & Francis Journals, vol. 17(12), pages 1003-1012.
    6. Corbet, Shaen & Eraslan, Veysel & Lucey, Brian & Sensoy, Ahmet, 2019. "The effectiveness of technical trading rules in cryptocurrency markets," Finance Research Letters, Elsevier, vol. 31(C), pages 32-37.
    7. Metghalchi, Massoud & Chen, Chien-Ping & Hayes, Linda A., 2015. "History of share prices and market efficiency of the Madrid general stock index," International Review of Financial Analysis, Elsevier, vol. 40(C), pages 178-184.
    8. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    9. Panha Heng & Scott J. Niblock, 2014. "Trading with Tigers: A Technical Analysis of Southeast Asian Stock Index Futures," International Economic Journal, Taylor & Francis Journals, vol. 28(4), pages 679-692, December.
    10. Chen, Cheng-Wei & Huang, Chin-Sheng & Lai, Hung-Wei, 2009. "The impact of data snooping on the testing of technical analysis: An empirical study of Asian stock markets," Journal of Asian Economics, Elsevier, vol. 20(5), pages 580-591, September.
    11. Tzu-Pu Chang & Yu-Cheng Chang & Po-Ching Chou, 2022. "The Trend is Your Friend: A Note on An Ensemble Learning Approach to Finding It," Bulletin of Applied Economics, Risk Market Journals, vol. 9(1), pages 19-25.
    12. Robert Ślepaczuk & Grzegorz Zakrzewski & Paweł Sakowski, 2012. "Investment strategies beating the market. What can we squeeze from the market?," Working Papers 2012-04, Faculty of Economic Sciences, University of Warsaw.

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