英文标题：
《RACORN-K: Risk-Aversion Pattern Matching-based Portfolio Selection》
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作者：
Yang Wang, Dong Wang, Yaodong Wang, You Zhang
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最新提交年份：
2018
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英文摘要：
  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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中文摘要：
投资组合选择是资产管理的中心任务，但事实证明，这非常具有挑战性。基于模式匹配的方法，尤其是CORN-K算法，在多个股票市场上取得了良好的效果。然而，现有模式匹配方法的一个关键不足是，在优化投资组合时，风险在很大程度上被忽视，这可能导致不可靠的利润，尤其是在动荡的市场中。我们提出了一种风险规避的CORN-K算法RACORN-K，该算法在搜索最优投资组合时惩罚风险。在四个数据集（道琼斯工业平均指数、摩根士丹利资本国际指数、SP500（N）、恒生指数）上的实验表明，新算法可以在收益率、夏普比率和最大提取率方面提供显著而可靠的改进，尤其是在波动性市场上。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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