投资组合optimization，预期股票回报由风险敞口决定
Portfolio optimization when expected stock returns are determined by exposure to risk
摘要：
人们普遍认为，当经典的最优策略与从数据中估计的参数一起应用时，所得到的投资组合权重随着时间的推移是显著波动和不稳定的。对此的主要解释是难以准确估计预期回报。在本文中，我们修改了$n$股票布莱克-斯科尔斯模型，通过引入一个新的参数化的漂移率。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个$n$布朗运动中保持投资于股票的财富的$1/n$。该策略适用于一个大的数据集的样本外。投资组合的权重是稳定的，随着时间的推移，并获得显着更高的夏普比经典的$1/n$战略。
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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