摘要翻译：
人们普遍认为，当经典的最优策略应用于从数据中估计的参数时，所得的投资组合权重随时间的推移具有显著的波动性和不稳定性。对此的主要解释是难以准确估计预期收益。本文通过引入一种新的漂移率参数化，对$N$stock Black-Scholes模型进行了修正。在此框架下，我们解决了Markowitz的连续时间投资组合问题。最优投资组合权重对应于在每一个$N$布朗运动中保持1/N$财富投资于股票。该策略是在样本外应用于一个大数据集。投资组合的权重随着时间的推移是稳定的，并获得明显高于经典的$1/N$策略的夏普比率。
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英文标题：
《Portfolio optimization when expected stock returns are determined by
  exposure to risk》
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作者：
Carl Lindberg
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最新提交年份：
2009
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分类信息：

一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  It is widely recognized that when classical optimal strategies are applied with parameters estimated from data, the resulting portfolio weights are remarkably volatile and unstable over time. The predominant explanation for this is the difficulty of estimating expected returns accurately. In this paper, we modify the $n$ stock Black--Scholes model by introducing a new parametrization of the drift rates. We solve Markowitz' continuous time portfolio problem in this framework. The optimal portfolio weights correspond to keeping $1/n$ of the wealth invested in stocks in each of the $n$ Brownian motions. The strategy is applied out-of-sample to a large data set. The portfolio weights are stable over time and obtain a significantly higher Sharpe ratio than the classical $1/n$ strategy.
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PDF链接：
https://arxiv.org/pdf/0906.2271