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

  • Li-Xin Wang
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    In Part III of this paper, we apply the price dynamical model with big buyers and big sellers developed in Part I of this paper to the daily closing data of the top 20 stocks in Hang Seng Index in 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 develop 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 all holdings of this 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 all holdings of the stock once the big seller strength is larger than the big buyer strength. Based on the testing over 198 two-year intervals uniformly distributed across the six-year period from 03-July-2007 to 28-June-2013 which includes a variety of scenarios, the net profits would increase 47% or 64% 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.

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    File URL: http://arxiv.org/pdf/1401.1892
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    Paper provided by arXiv.org in its series Papers with number 1401.1892.

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    Date of creation: Jan 2014
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    Handle: RePEc:arx:papers:1401.1892
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    1. 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.
    2. 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.
    3. Andrew W. Lo & Harry Mamaysky & Jiang Wang, 2000. "Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation," NBER Working Papers 7613, National Bureau of Economic Research, Inc.
    4. 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.
    5. 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 151-78, March.
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