  The high sensitivity of optimized portfolios to estimation errors has
prevented their practical application. To mitigate this sensitivity, we propose
a new portfolio model called a Deeply Equal-Weighted Subset Portfolio (DEWSP).
DEWSP is a subset of top-N ranked assets in an asset universe, the members of
which are selected based on the predicted returns from deep learning algorithms
and are equally weighted. Herein, we evaluate the performance of DEWSPs of
different sizes N in comparison with the performance of other types of
portfolios such as optimized portfolios and historically equal-weighed subset
portfolios (HEWSPs), which are subsets of top-N ranked assets based on the
historical mean returns. We found the following advantages of DEWSPs: First,
DEWSPs provides an improvement rate of 0.24% to 5.15% in terms of monthly
Sharpe ratio compared to the benchmark, HEWSPs. In addition, DEWSPs are built
using a purely data-driven approach rather than relying on the efforts of
experts. DEWSPs can also target the relative risk and return to the baseline of
the EWP of an asset universe by adjusting the size N. Finally, the DEWSP
allocation mechanism is transparent and intuitive. These advantages make DEWSP
competitive in practice.
