We consider the problem of maximizing the out-of-sample Sharpe ratio when portfolio weights have to be estimated. We apply an improved bootstrap-based estimator, and an approximative estimator derived from a Taylor series. In a simulation study and empirical analysis with 15 datasets the proposed estimators outperform the minimum variance and equally weighted portfolio strategies. Out-of-sample Sharpe ratios improve by 15 and 32 percent on average, respectively, in the empirical analysis. While effectively dealing with estimation risks, the estimators produce considerable amounts of turnover. Realized net Sharpe ratios improve by 40 percent on average when the effects of accruing transaction costs are incorporated ex-ante into estimation of portfolio weights. When adding a risk-free asset, net Sharpe ratios remain of the same magnitude and portfolio volatility does not exceed a predefined target level.

当必须估计投资组合权重时，我们考虑最大化样本外夏普比率的问题。我们应用了一个改进的基于引导的估计器，以及一个从泰勒级数推导出来的近似估计器。在对 15 个数据集进行的模拟研究和实证分析中，建议的估计量优于最小方差和同等加权的投资组合策略。在实证分析中，样本外夏普比率平均分别提高了 15% 和 32%。在有效应对估算风险的同时，估算人员产生了可观的营业额。当应计交易成本的影响事前纳入投资组合权重的估计中时，已实现的净夏普比率平均提高 40%。添加无风险资产时，