论文标题
定时超额回报跨宇宙的方法
Timing Excess Returns A cross-universe approach to alpha
论文作者
论文摘要
我们提出了一个简单的模型，该模型使用时间序列动量来构建系统地超过其基准的策略。我们模型的简单性是优雅的：我们只需要一个基准时间序列和几个相关的可投资提示，而不需要回归或其他模型来估算我们的参数。我们发现，我们的一件尺寸适合所有方法在满足前风险要求的同时，在股票和债券市场上都表现出色，每年几乎每年的收益与MSCI World和MSCI World和Bloomberg Barclays Euro Euroly Eurol Contricate Corporate Corporate Corporate Corporate Corporate benchmarks持续回头期。然后，我们将这两种方法结合在一起，通过对Eonia总回报指数进行基准测试，并以1.8的尖锐比率找到明显的超越性能。此外，我们证明我们的模型提供了福利与具有固定平均权重的静态投资组合，这表明超额回流动量的时机具有相当大的好处，而静态分配也很大。这也适用于被动投资股权因素，在这种情况下，我们的表现均优于具有统计意义的静态因素暴露组合。另外，我们表明我们的模型在扣除交易成本后会提供Alpha。
We present a simple model that uses time series momentum in order to construct strategies that systematically outperform their benchmark. The simplicity of our model is elegant: We only require a benchmark time series and several related investable indizes, not requiring regression or other models to estimate our parameters. We find that our one size fits all approach delivers significant outperformance in both equity and bond markets while meeting the ex-ante risk requirements, nearly doubling yearly returns vs. the MSCI World and Bloomberg Barclays Euro Aggregate Corporate Bond benchmarks in a long-only backtest. We then combine both approaches into an absolute return strategy by benchmarking vs. the Eonia Total Return Index and find significant outperformance at a sharpe ratio of 1.8. Furthermore, we demonstrate that our model delivers a benefit versus a static portfolio with fixed mean weights, showing that timing of excess return momentum has a sizeable benefit vs. static allocations. This also applies to the passively investable equity factors, where we outperform a static factor exposure portfolio with statistical significance. Also, we show that our model delivers an alpha after deducting transaction costs.
