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用弹性网络同业组合估计统计套利风险溢价

文章 arXiv papers · 作者: Raymond C. W. Leung et al.
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总结

本研究提出一种方法，用于估计个股所承受、但与相似股票不共享的风险对应的收益，即使底层风险因子未知。研究将每只股票的历史收益对其他股票的收益进行投影，构建同业复制组合。弹性网络生成稀疏的组合权重，个股与其同业组合之间的差异代表因子残差风险。该残差的预期收益称为统计套利风险溢价。

同业拟合 R 平方较低的股票被归类为统计套利风险较高的股票。报告的横截面结果显示，高风险股票的月度残差溢价和超额收益高于低风险股票；股票整体的平均风险呈逆周期变化。作者称，在控制已知因子和公司特征后，结果依然成立。摘要未说明实施成本、组合换手率或样本外表现，因此无法判断计入交易摩擦后，测得的溢价是否可直接获取。

核心观点

  • 即使因子本身未知，同业组合也有助于对冲个股的因子敞口。
  • 弹性网络将每只股票的历史收益对其他股票进行投影，以生成稀疏的同业组合权重。
  • 同业拟合 R 平方较低，表明个股特有的统计套利风险较高。
  • 研究报告称，高风险股票具有更高的残差溢价和超额收益。
  • 报告的关系在控制其他因素后仍成立，但文中未说明交易成本和样本外结果。

标签

股票机器学习因子投资套利统计学
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# Statistical Arbitrage Risk Premium by Machine Learning


# Statistical Arbitrage Risk Premium by Machine Learning









How to hedge factor risks without knowing the identities of the factors? We first prove a general theoretical result: even if the exact set of factors cannot be identified, any risky asset can use some portfolio of similar peer assets to hedge against its own factor exposures. A long position of a risky asset and a short position of a "replicate portfolio" of its peers represent that asset's factor residual risk. We coin the expected return of an asset's factor residual risk as its Statistical Arbitrage Risk Premium (SARP). The challenge in empirically estimating SARP is finding the peers for each asset and constructing the replicate portfolios. We use the elastic-net, a machine learning method, to project each stock's past returns onto that of every other stock. The resulting high-dimensional but sparse projection vector serves as investment weights in constructing the stocks' replicate portfolios. We say a stock has high (low) Statistical Arbitrage Risk (SAR) if it has low (high) R-squared with its peers. The key finding is that "unique" stocks have both a higher SARP and higher excess returns than "ubiquitous" stocks: in the cross-section, high SAR stocks have a monthly SARP (monthly excess returns) that is 1.101% (0.710%) greater than low SAR stocks. The average SAR across all stocks is countercyclical. Our results are robust to controlling for various known priced factors and characteristics.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。