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RACORN-K: Risk-Aversion Pattern Matching-based Portfolio Selection


  • Yang Wang
  • Dong Wang
  • Yaodong Wang
  • You Zhang


Portfolio selection is the central task for assets management, but it turns out to be very challenging. Methods based on pattern matching, particularly the CORN-K algorithm, have achieved promising performance on several stock markets. A key shortage of the existing pattern matching methods, however, is that the risk is largely ignored when optimizing portfolios, which may lead to unreliable profits, particularly in volatile markets. We present a risk-aversion CORN-K algorithm, RACORN-K, that penalizes risk when searching for optimal portfolios. Experiments on four datasets (DJIA, MSCI, SP500(N), HSI) demonstrate that the new algorithm can deliver notable and reliable improvements in terms of return, Sharp ratio and maximum drawdown, especially on volatile markets.

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  • Yang Wang & Dong Wang & Yaodong Wang & You Zhang, 2018. "RACORN-K: Risk-Aversion Pattern Matching-based Portfolio Selection," Papers 1802.10244,
  • Handle: RePEc:arx:papers:1802.10244

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    1. Jegadeesh, Narasimhan, 1990. "Evidence of Predictable Behavior of Security Returns," Journal of Finance, American Finance Association, vol. 45(3), pages 881-898, July.
    2. David P. Helmbold & Robert E. Schapire & Yoram Singer & Manfred K. Warmuth, 1998. "On‐Line Portfolio Selection Using Multiplicative Updates," Mathematical Finance, Wiley Blackwell, vol. 8(4), pages 325-347, October.
    3. László Györfi & Gábor Lugosi & Frederic Udina, 2006. "Nonparametric Kernel‐Based Sequential Investment Strategies," Mathematical Finance, Wiley Blackwell, vol. 16(2), pages 337-357, April.
    4. Györfi László & Udina Frederic & Walk Harro, 2008. "Nonparametric nearest neighbor based empirical portfolio selection strategies," Statistics & Risk Modeling, De Gruyter, vol. 26(2), pages 145-157, March.
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