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Dynamically Adjustable Moving Average (AMA’) technical analysis indicator to forecast Asian Tigers’ futures markets

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  • Phooi M’ng, Jacinta Chan

Abstract

To determine the predictability of futures time series, the daily stock market indices’ futures returns from the Asia Tigers countries, namely Hong Kong’s Hang Seng Futures (HSF), South Korea’s KOSPI Futures (KOSPIF), Singapore’s SiMSCI Futures (SiMSCIF) and Taiwan’s TAIEX Futures (TAIEXF) from 2006 to 2013 are examined for profitability results using technical analysis indicators. A dynamic volatility indicator, named Adjustable Moving Average (AMA’) is used as a trade timing devise to seek returns above the threshold buy and hold strategy. Using the information learnt during this in-sample period, further tests are conducted on an out-of-sample period, 2014–2015 to validate the viability of AMA’. AMA’ adjusts to the prevailing market conditions, to avoid some whipsaws (trading losses) in range trading and capture a larger portion of profit by entering into the new trends early. Using the trading signals from AMA’ and the other moving averages rules, evidence of returns after transaction costs above those of the threshold passive buy and hold strategy are found in these time series’ returns, especially more so for AMA’. The results here suggest that it is worthwhile to investigate the profitability of moving averages trading rules, especially the more adjustable ones.

Suggested Citation

  • Phooi M’ng, Jacinta Chan, 2018. "Dynamically Adjustable Moving Average (AMA’) technical analysis indicator to forecast Asian Tigers’ futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 336-345.
  • Handle: RePEc:eee:phsmap:v:509:y:2018:i:c:p:336-345
    DOI: 10.1016/j.physa.2018.06.010
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    as
    1. Gencay, Ramazan, 1998. "The predictability of security returns with simple technical trading rules," Journal of Empirical Finance, Elsevier, vol. 5(4), pages 347-359, October.
    2. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871.
    3. Jiang, Zhi-Qiang & Xie, Wen-Jie & Zhou, Wei-Xing, 2014. "Testing the weak-form efficiency of the WTI crude oil futures market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 235-244.
    4. Podobnik, Boris & Fu, Dongfeng & Jagric, Timotej & Grosse, Ivo & Eugene Stanley, H., 2006. "Fractionally integrated process for transition economics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 362(2), pages 465-470.
    5. Epaminondas Panas, 2001. "Estimating fractal dimension using stable distributions and exploring long memory through ARFIMA models in Athens Stock Exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 11(4), pages 395-402.
    6. Christian L Dunis & Gary Shannon, 2005. "Emerging markets of South-East and Central Asia: Do they still offer a diversification benefit?," Journal of Asset Management, Palgrave Macmillan, vol. 6(3), pages 168-190, October.
    7. Cajueiro, Daniel O. & Tabak, Benjamin M., 2006. "Testing for predictability in equity returns for European transition markets," Economic Systems, Elsevier, vol. 30(1), pages 56-78, March.
    8. Fernandez-Rodriguez, Fernando & Gonzalez-Martel, Christian & Sosvilla-Rivero, Simon, 2000. "On the profitability of technical trading rules based on artificial neural networks:: Evidence from the Madrid stock market," Economics Letters, Elsevier, vol. 69(1), pages 89-94, October.
    9. 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.
    10. Timotej Jagric & Boris Podobnik & Marko Kolanovic, 2005. "Does the Efficient Market Hypothesis Hold?: Evidence from Six Transition Economies," Eastern European Economics, Taylor & Francis Journals, vol. 43(4), pages 79-103, August.
    11. Ki-Yeol Kwon & Richard Kish, 2002. "Technical trading strategies and return predictability: NYSE," Applied Financial Economics, Taylor & Francis Journals, vol. 12(9), pages 639-653.
    12. Li, Kai & Sarkar, Asani & Wang, Zhenyu, 2003. "Diversification benefits of emerging markets subject to portfolio constraints," Journal of Empirical Finance, Elsevier, vol. 10(1-2), pages 57-80, February.
    13. Neely, Christopher J. & Weller, Paul A. & Ulrich, Joshua M., 2009. "The Adaptive Markets Hypothesis: Evidence from the Foreign Exchange Market," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 44(2), pages 467-488, April.
    14. Alvarez-Ramirez, Jose & Rodriguez, Eduardo & Espinosa-Paredes, Gilberto, 2012. "Is the US stock market becoming weakly efficient over time? Evidence from 80-year-long data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5643-5647.
    15. 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.
    16. 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.
    17. Kwon, Ki-Yeol & Kish, Richard J., 2002. "A comparative study of technical trading strategies and return predictability: an extension of Brock, Lakonishok, and LeBaron (1992) using NYSE and NASDAQ indices," The Quarterly Review of Economics and Finance, Elsevier, vol. 42(3), pages 611-631.
    18. Mario A Bertella & Felipe R Pires & Ling Feng & Harry Eugene Stanley, 2014. "Confidence and the Stock Market: An Agent-Based Approach," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-9, January.
    19. Julián Andrada-Félix & Fernando Fernández-Rodríguez, 2008. "Improving moving average trading rules with boosting and statistical learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 433-449.
    20. Dima Alberg & Haim Shalit & Rami Yosef, 2008. "Estimating stock market volatility using asymmetric GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(15), pages 1201-1208.
    21. Taylor, Nick, 2014. "The rise and fall of technical trading rule success," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 286-302.
    22. Benoit Mandelbrot & Adlai Fisher & Laurent Calvet, 1997. "A Multifractal Model of Asset Returns," Cowles Foundation Discussion Papers 1164, Cowles Foundation for Research in Economics, Yale University.
    23. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
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