Before revision:  We consider predictive distributions resulting from high-frequency based covariance matrix estimates, daily stochastic volatility factor models and regularized rolling window covariance estimates, among others. Using datacapturing several hundred Nasdaq stocks over more than 10 years , we illustrate that transaction cost regularization (even to small extent) is crucial in order to produce allocations with positive Sharpe ratios.
After revision:  We consider predictive distributions resulting from high-frequency and low-frequency data. In an extensive empirical study , we illustrate that transaction cost regularization (even to small extent) is crucial in order to produce allocations with positive Sharpe ratios.