英文标题：
《Information ratio analysis of momentum strategies》
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作者：
Fernando F. Ferreira, A. Christian Silva, Ju-Yi Yen
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最新提交年份：
2014
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英文摘要：
In the past 20 years, momentum or trend following strategies have become an established part of the investor toolbox. We introduce a new way of analyzing momentum strategies by looking at the information ratio (IR, average return divided by standard deviation). We calculate the theoretical IR of a momentum strategy, and show that if momentum is mainly due to the positive autocorrelation in returns, IR as a function of the portfolio formation period (look-back) is very different from momentum due to the drift (average return). The IR shows that for look-back periods of a few months, the investor is more likely to tap into autocorrelation. However, for look-back periods closer to 1 year, the investor is more likely to tap into the drift. We compare the historical data to the theoretical IR by constructing stationary periods. The empirical study finds that there are periods/regimes where the autocorrelation is more important than the drift in explaining the IR (particularly pre-1975) and others where the drift is more important (mostly after 1975). We conclude our study by applying our momentum strategy to 100 plus years of the Dow-Jones Industrial Average. We report damped oscillations on the IR for look-back periods of several years and model such oscilations as a reversal to the mean growth rate.
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中文摘要：
在过去20年中，动量或趋势跟踪策略已成为投资者工具箱中的一个既定部分。我们引入了一种通过观察信息比（IR，平均收益除以标准差）来分析动量策略的新方法。我们计算了动量策略的理论IR，并表明，如果动量主要是由于收益的正自相关，则IR作为投资组合形成期（回顾）的函数，与由于漂移（平均收益）而产生的动量非常不同。IR显示，在几个月的回顾期内，投资者更有可能利用自相关性。然而，对于接近1年的回顾期，投资者更有可能利用这种趋势。我们通过构造平稳周期将历史数据与理论IR进行比较。实证研究发现，在解释IR时，有一些时期/制度的自相关比漂移更重要（尤其是1975年之前），还有一些时期/制度的漂移更重要（主要是1975年之后）。我们将动量策略应用于道琼斯工业平均指数的100多年来，以此结束我们的研究。我们报告了几年回顾期的IR衰减振荡，并将这种振荡建模为平均增长率的反转。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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