Before revision:  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. We moreover show that performance differences between individual models decline when transaction costs are considered. Nevertheless, it turns out that adaptive mixtures based on high-frequency and low-frequency information yield the highest performance. Portfolio bootstrap reveals that naive 1/N-allocations and global minimum variance allocations (with and without short sales constraints) are significantly outperformed in terms of Sharpe ratios and utility gains .
After revision:  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 construct empirically well-performing portfolios .