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Evaluating the performance of adapting trading strategies with different memory lengths

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  • Andreas Krause

Abstract

We propose a prediction model based on the minority game in which traders continuously evaluate a complete set of trading strategies with different memory lengths using the strategies' past performance. Based on the chosen trading strategy they determine their prediction of the movement for the following time period of a single asset. We find empirically using stocks from the S&P500 that our prediction model yields a high success rate of over 51.5% and produces higher returns than a buy-and-hold strategy. Even when taking into account trading costs we find that using the predictions will generate superior investment portfolios.

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  • Andreas Krause, 2009. "Evaluating the performance of adapting trading strategies with different memory lengths," Papers 0901.0447, arXiv.org.
  • Handle: RePEc:arx:papers:0901.0447
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    File URL: http://arxiv.org/pdf/0901.0447
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    References listed on IDEAS

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    1. 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.
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    3. Marshall, Ben R. & Young, Martin R. & Rose, Lawrence C., 2006. "Candlestick technical trading strategies: Can they create value for investors?," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2303-2323, August.
    4. Challet, Damien & Marsili, Matteo & Zhang, Yi-Cheng, 2001. "Stylized facts of financial markets and market crashes in Minority Games," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 294(3), pages 514-524.
    5. Challet, Damien & Marsili, Matteo & Zhang, Yi-Cheng, 2000. "Modeling market mechanism with minority game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 276(1), pages 284-315.
    6. Challet, Damien & Zhang, Yi-Cheng, 1998. "On the minority game: Analytical and numerical studies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 256(3), pages 514-532.
    7. Allen, Franklin & Karjalainen, Risto, 1999. "Using genetic algorithms to find technical trading rules," Journal of Financial Economics, Elsevier, vol. 51(2), pages 245-271, February.
    8. 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.
    9. Nam, Kiseok & Washer, Kenneth M. & Chu, Quentin C., 2005. "Asymmetric return dynamics and technical trading strategies," Journal of Banking & Finance, Elsevier, vol. 29(2), pages 391-418, February.
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