英文标题：
《Noise Fit, Estimation Error and a Sharpe Information Criterion》
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作者：
Dirk Paulsen and Jakob S\\\"ohl
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最新提交年份：
2019
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英文摘要：
When the in-sample Sharpe ratio is obtained by optimizing over a k-dimensional parameter space, it is a biased estimator for what can be expected on unseen data (out-of-sample). We derive (1) an unbiased estimator adjusting for both sources of bias: noise fit and estimation error. We then show (2) how to use the adjusted Sharpe ratio as model selection criterion analogously to the Akaike Information Criterion (AIC). Selecting a model with the highest adjusted Sharpe ratio selects the model with the highest estimated out-of-sample Sharpe ratio in the same way as selection by AIC does for the log-likelihood as measure of fit.
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中文摘要：
当通过在k维参数空间上进行优化获得样本内夏普比时，它是对未知数据（样本外）的有偏估计。我们推导了（1）一个无偏估计器，该估计器同时调整了两个偏差来源：噪声拟合和估计误差。然后，我们展示（2）如何使用调整后的夏普比率作为模型选择标准，类似于Akaike信息标准（AIC）。选择具有最高调整夏普比的模型，选择具有最高估计样本外夏普比的模型，方法与AIC选择对数似然作为拟合度量的方法相同。
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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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