时机选择超额收益率一种跨宇宙的alpha方法
Timing Excess Returns A cross-universe approach to alpha
摘要：
我们提出了一个简单的模型，使用时间序列的势头，以构建战略，系统地超越他们的基准。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指标，不需要回归或其他模型来估计我们的参数。我们发现，我们的“一刀切”方法在股票和债券市场都取得了显著的优异表现，同时满足了事前风险要求，在仅做多的回测中，年回报率比MSCI世界指数和彭博巴克莱欧元综合企业债券基准高出近一倍。然后，我们将这两种方法结合到一个绝对回报策略中，通过对Eonia总回报指数进行基准测试，发现夏普比率为1.8时表现显著。此外，我们证明了我们的模型提供了一个固定的平均权重的静态投资组合的好处，显示出超额收益率动量的时间有一个相当大的好处与静态分配。这也适用于被动可投资的股票因子，我们在统计上显著优于静态因子风险投资组合。此外，我们表明，我们的模型提供了一个阿尔法扣除交易成本后。
英文摘要：
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.
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。
