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Trading Performance Analysis: A Comparisons Between the Original MA Crossover and Modified MA Crossover Strategy

Author

Listed:
  • Afiruddin Tapa*

    (School of Economic, Finance and Banking, College of Business Universiti Utara Malaysia, Malaysia)

  • Mohd Hasimi Yaacob

    (Faculty of Economics and Management, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia)

  • Ahmad Husni Hamzah

    (Universiti Sultan Zainal Abidin, Terengganu, Malaysia)

  • Yean Soh Chuen

    (Universiti Utara Malaysia Kuala Lumpur, Malaysia)

Abstract

This paper empirically analyses the Trading Performance by using technical analysis approach. The original moving-average (MA) crossover strategy as compare with the modified moving-average crossover strategy. The modified trading rules are the rules that been established to trading rules such as entry rule, exit rule, holding rule, and stop-loss rule. The results show The MAshort of 10-period for modified strategy underperform the original strategy, except for MA (10,100). The modified MA (20,200), (50,100), (50,200), and (100,200) underperform the original strategy. Only modified MA (20,50) and (20,100) outperform the original strategy. The outperformance and underperformance due to the stricter additional trading rule that reduces trading signals, and thus lower number of trades.

Suggested Citation

  • Afiruddin Tapa* & Mohd Hasimi Yaacob & Ahmad Husni Hamzah & Yean Soh Chuen, 2018. "Trading Performance Analysis: A Comparisons Between the Original MA Crossover and Modified MA Crossover Strategy," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 933-941:6.
  • Handle: RePEc:arp:tjssrr:2018:p:933-941
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    References listed on IDEAS

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    1. J. Andrew Coutts & Kwong-C. Cheung, 2000. "Trading rules and stock returns: some preliminary short run evidence from the Hang Seng 1985-1997," Applied Financial Economics, Taylor & Francis Journals, vol. 10(6), pages 579-586.
    2. Bertrand Maillet & Thierry Michel, 2000. "Further insights on the puzzle of technical analysis profitability," The European Journal of Finance, Taylor & Francis Journals, vol. 6(2), pages 196-224.
    3. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    4. 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, vol. 32(4), pages 405-426, December.
    5. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1770, August.
    6. Valeriy Zakamulin, 2014. "The real-life performance of market timing with moving average and time-series momentum rules," Journal of Asset Management, Palgrave Macmillan, vol. 15(4), pages 261-278, August.
    7. LeBaron, Blake, 1999. "Technical trading rule profitability and foreign exchange intervention," Journal of International Economics, Elsevier, vol. 49(1), pages 125-143, October.
    8. Neftci, Salih N, 1991. "Naive Trading Rules in Financial Markets and Wiener-Kolmogorov Prediction Theory: A Study of "Technical Analysis."," The Journal of Business, University of Chicago Press, vol. 64(4), pages 549-571, October.
    9. Lo, Andrew W & MacKinlay, A Craig, 1990. "Data-Snooping Biases in Tests of Financial Asset Pricing Models," The Review of Financial Studies, Society for Financial Studies, vol. 3(3), pages 431-467.
    10. Sullivan, Ryan & Timmermann, Allan & White, Halbert, 2003. "Forecast evaluation with shared data sets," International Journal of Forecasting, Elsevier, vol. 19(2), pages 217-227.
    11. Shynkevich, Andrei, 2012. "Performance of technical analysis in growth and small cap segments of the US equity market," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 193-208.
    12. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," Journal of Finance, American Finance Association, vol. 55(4), pages 1705-1765, August.
    13. Bessembinder, Hendrik & Chan, Kalok, 1995. "The profitability of technical trading rules in the Asian stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 3(2-3), pages 257-284, July.
    14. Thomas Gehrig & Lukas Menkhoff, 2006. "Extended evidence on the use of technical analysis in foreign exchange," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 11(4), pages 327-338.
    15. Zhu, Yingzi & Zhou, Guofu, 2009. "Technical analysis: An asset allocation perspective on the use of moving averages," Journal of Financial Economics, Elsevier, vol. 92(3), pages 519-544, June.
    16. Fama, Eugene F. & Schwert, G. William, 1977. "Asset returns and inflation," Journal of Financial Economics, Elsevier, vol. 5(2), pages 115-146, November.
    17. Campbell, John Y., 1987. "Stock returns and the term structure," Journal of Financial Economics, Elsevier, vol. 18(2), pages 373-399, June.
    18. Szakmary, Andrew C. & Shen, Qian & Sharma, Subhash C., 2010. "Trend-following trading strategies in commodity futures: A re-examination," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 409-426, February.
    19. Blume, Lawrence & Easley, David & O'Hara, Maureen, 1994. "Market Statistics and Technical Analysis: The Role of Volume," Journal of Finance, American Finance Association, vol. 49(1), pages 153-181, March.
    20. Parisi, Franco & Vasquez, Alejandra, 2000. "Simple technical trading rules of stock returns: evidence from 1987 to 1998 in Chile," Emerging Markets Review, Elsevier, vol. 1(2), pages 152-164, September.
    21. John H. Cochrane, 2008. "The Dog That Did Not Bark: A Defense of Return Predictability," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1533-1575, July.
    22. Gunasekarage, Abeyratna & Power, David M., 2001. "The profitability of moving average trading rules in South Asian stock markets," Emerging Markets Review, Elsevier, vol. 2(1), pages 17-33, March.
    23. 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.
    24. Bertrand Maillet & Thierry Michel, 2000. "Further Insights on the Puzzle of Technical Analysis Profitability," Post-Print hal-00308986, HAL.
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