标题： 当预期股票收益由风险暴露决定时的投资组合优化 显示英文标题

标题： Portfolio optimization when expected stock returns are determined by exposure to risk

摘要： 人们普遍认识到，当用从数据中估计出的参数应用经典的最优策略时，所得到的投资组合权重在时间上表现出显著的波动性和不稳定性。对此的主要解释是准确估计预期收益的难度。 本文通过引入漂移率的新参数化方法，修改了$n$股票的 Black-Scholes 模型。我们在这一框架下解决了马科维茨的连续时间投资组合问题。最优的投资组合权重对应于将财富中的$1/n$投资到每一种由$n$个布朗运动表示的股票中。该策略被应用于一个大型数据集的样本外测试。投资组合权重随时间稳定，并且获得了比经典$1/n$策略显著更高的夏普比率。 显示英文摘要

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.