  This paper aims at developing a new method by which to build a data-driven
portfolio featuring a target risk-return. We first present a comparative study
of recurrent neural network models (RNNs), including a simple RNN, long
short-term memory (LSTM), and gated recurrent unit (GRU) for selecting the best
predictor to use in portfolio construction. The models are applied to the
investment universe consisted of ten stocks in the S&P500. The experimental
results shows that LSTM outperforms the others in terms of hit ratio of
one-month-ahead forecasts. We then build predictive threshold-based portfolios
(TBPs) that are subsets of the universe satisfying given threshold criteria for
the predicted returns. The TBPs are rebalanced monthly to restore equal weights
to each security within the TBPs. We find that the risk and return profile of
the realized TBP represents a monotonically increasing frontier on the
risk-return plane, where the equally weighted portfolio (EWP) of all ten stocks
plays a role in their lower bound. This shows the availability of TBPs in
targeting specific risk-return levels, and an EWP based on all the assets plays
a role in the reference portfolio of TBPs. In the process, thresholds play
dominant roles in characterizing risk, return, and the prediction accuracy of
the subset. The TBP is more data-driven in designing portfolio target risk and
return than existing ones, in the sense that it requires no prior knowledge of
finance such as financial assumptions, financial mathematics, or expert
insights. In a practical application, we present the TBP management procedure
for a time horizon extending over multiple time periods; we also discuss their
application to mean-variance portfolios to reduce estimation risk.
