Before revision:  We build portfolios by adjusting potential return threshold levels used to classify assets, and obtain  risk-return profiles examining their statistical properties.
After revision:  We build portfolios by adjusting potential return threshold levels used to classify assets, and obtain 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 profiles examining their statistical properties.