IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1401.1892.html
   My bibliography  Save this paper

Dynamical Models of Stock Prices Based on Technical Trading Rules Part III: Application to Hong Kong Stocks

Author

Listed:
  • Li-Xin Wang

Abstract

In Part III of this study, we apply the price dynamical model with big buyers and big sellers developed in Part I of this paper to the daily closing prices of the top 20 banking and real estate stocks listed in the Hong Kong Stock Exchange. The basic idea is to estimate the strength parameters of the big buyers and the big sellers in the model and make buy/sell decisions based on these parameter estimates. We propose two trading strategies: (i) Follow-the-Big-Buyer which buys when big buyer begins to appear and there is no sign of big sellers, holds the stock as long as the big buyer is still there, and sells the stock once the big buyer disappears; and (ii) Ride-the-Mood which buys as soon as the big buyer strength begins to surpass the big seller strength, and sells the stock once the opposite happens. Based on the testing over 245 two-year intervals uniformly distributed across the seven years from 03-July-2007 to 02-July-2014 which includes a variety of scenarios, the net profits would increase 67% or 120% on average if an investor switched from the benchmark Buy-and-Hold strategy to the Follow-the-Big-Buyer or Ride-the-Mood strategies during this period, respectively.

Suggested Citation

  • Li-Xin Wang, 2014. "Dynamical Models of Stock Prices Based on Technical Trading Rules Part III: Application to Hong Kong Stocks," Papers 1401.1892, arXiv.org, revised Feb 2016.
  • Handle: RePEc:arx:papers:1401.1892
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1401.1892
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrei A. Kirilenko & Andrew W. Lo, 2013. "Moore's Law versus Murphy's Law: Algorithmic Trading and Its Discontents," Journal of Economic Perspectives, American Economic Association, vol. 27(2), pages 51-72, Spring.
    2. 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.
    3. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    4. repec:bla:jfinan:v:55:y:2000:i:4:p:1705-1770 is not listed on IDEAS
    5. Jean-Philippe Bouchaud & J. Doyne Farmer & Fabrizio Lillo, 2008. "How markets slowly digest changes in supply and demand," Papers 0809.0822, arXiv.org.
    6. Andrew W. Lo, 2012. "Reading about the Financial Crisis: A Twenty-One-Book Review," Journal of Economic Literature, American Economic Association, vol. 50(1), pages 151-178, March.
    7. Menkhoff, Lukas, 2010. "The use of technical analysis by fund managers: International evidence," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2573-2586, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li-Xin Wang, 2014. "Dynamical Models of Stock Prices Based on Technical Trading Rules Part I: The Models," Papers 1401.1888, arXiv.org, revised Feb 2016.
    2. Li-Xin Wang, 2014. "Dynamical Models of Stock Prices Based on Technical Trading Rules Part II: Analysis of the Models," Papers 1401.1891, arXiv.org, revised Feb 2016.
    3. Jin, Xiaoye, 2021. "What do we know about the popularity of technical analysis in foreign exchange markets? A skewness preference perspective," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 71(C).
    4. Gerritsen, Dirk F., 2016. "Are chartists artists? The determinants and profitability of recommendations based on technical analysis," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 179-196.
    5. Christopher J. Neely & Paul A. Weller, 2011. "Technical analysis in the foreign exchange market," Working Papers 2011-001, Federal Reserve Bank of St. Louis.
    6. Jasmina Hasanhodzic & Andrew Lo & Emanuele Viola, 2011. "A computational view of market efficiency," Quantitative Finance, Taylor & Francis Journals, vol. 11(7), pages 1043-1050.
    7. Ebert, Sebastian & Hilpert, Christian, 2019. "Skewness preference and the popularity of technical analysis," Journal of Banking & Finance, Elsevier, vol. 109(C).
    8. Jying‐Nan Wang & Hung‐Chun Liu & Jiangze Du & Yuan‐Teng Hsu, 2019. "Economic benefits of technical analysis in portfolio management: Evidence from global stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 24(2), pages 890-902, April.
    9. Hung, Chiayu & Lai, Hung-Neng, 2022. "Information asymmetry and the profitability of technical analysis," Journal of Banking & Finance, Elsevier, vol. 134(C).
    10. Farias Nazário, Rodolfo Toríbio & e Silva, Jéssica Lima & Sobreiro, Vinicius Amorim & Kimura, Herbert, 2017. "A literature review of technical analysis on stock markets," The Quarterly Review of Economics and Finance, Elsevier, vol. 66(C), pages 115-126.
    11. Leandro Maciel, 2020. "Technical analysis based on high and low stock prices forecasts: evidence for Brazil using a fractionally cointegrated VAR model," Empirical Economics, Springer, vol. 58(4), pages 1513-1540, April.
    12. Chia-Lin Chang & Jukka Ilomäki & Hannu Laurila & Michael McAleer, 2018. "Long Run Returns Predictability and Volatility with Moving Averages," Risks, MDPI, vol. 6(4), pages 1-18, September.
    13. Bell, Peter N, 2013. "New Testing Procedures to Assess Market Efficiency with Trading Rules," MPRA Paper 46701, University Library of Munich, Germany.
    14. Stephan Schulmeister, 2000. "Technical Analysis and Exchange Rate Dynamics," WIFO Studies, WIFO, number 25857.
    15. Mahata, Ajit & Rai, Anish & Nurujjaman, Md. & Prakash, Om, 2021. "Modeling and analysis of the effect of COVID-19 on the stock price: V and L-shape recovery," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    16. Kun Xing & Honggang Li, 2024. "The profitability of interacting trading strategies from an ecological perspective," Annals of Finance, Springer, vol. 20(3), pages 377-394, September.
    17. Erdemlioglu, Deniz & Petitjean, Mikael & Vargas, Nicolas, 2021. "Market instability and technical trading at high frequency: Evidence from NASDAQ stocks," Economic Modelling, Elsevier, vol. 102(C).
    18. Sandrine Jacob Leal & Mauro Napoletano & Andrea Roventini & Giorgio Fagiolo, 2016. "Rock around the clock: An agent-based model of low- and high-frequency trading," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 49-76, March.
    19. Hubert Dichtl, 2020. "Investing in the S&P 500 index: Can anything beat the buy‐and‐hold strategy?," Review of Financial Economics, John Wiley & Sons, vol. 38(2), pages 352-378, April.
    20. Michael McAleer & John Suen & Wing Keung Wong, 2016. "Profiteering from the Dot-Com Bubble, Subprime Crisis and Asian Financial Crisis," The Japanese Economic Review, Japanese Economic Association, vol. 67(3), pages 257-279, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1401.1892. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.