基于机器学习的统计套利风险溢价
Statistical Arbitrage Risk Premium by Machine Learning
摘要：
如何在不知道因子的身份的情况下对冲因子风险？我们首先证明了一个一般性的理论结果：即使无法确定确切的因素集，任何风险资产都可以使用类似的同类资产的投资组合来对冲自己的因素风险。风险资产的多头头寸和同类资产的“复制投资组合”的空头头寸代表该资产的要素剩余风险。我们将一项资产的要素剩余风险的预期收益率称为其统计套利风险溢价（SARP）。经验估计SARP的挑战是找到每种资产的同行，并构建复制的投资组合。我们使用弹性网络，一种机器学习方法，将每只股票的过去回报投射到其他股票的回报上。由此产生的高维但稀疏的投影向量作为构建股票复制投资组合的投资权重。我们说一只股票有高（低）统计套利风险（SAR），如果它有低（高）R平方与它的同行。关键的发现是，“独特”的股票有一个更高的SARP和更高的超额收益率比“无处不在”的股票：在横截面中，高SAR股票的每月SARP（每月超额收益率）是1.101%（0.710%）比低SAR股票。所有股票的平均SAR是反周期的。我们的结果是强大的控制各种已知的定价因素和特点。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
