The purpose of this paper is to present a new approach to construct optimal portfolios and investment strategies based on stock return predictions. Recurrent Neural Networks (RNNs) are applied to stock return predictions. We build portfolios by adjusting potential return threshold levels used to classify assets, and obtain risk-return profiles examining their statistical properties. The results show conclusively that the thresholds control the level of risk-return trade-off, which can be usefully implemented in allocating assets across asset classes based on individual risk capacity and risk tolerance levels. Next, we obtain frontier lines representing the risk-reward relationship for portfolios built using three strategies (e.g., long, short, and long-short), and then determine the efficient frontier that maximizes returns at specific risk levels. We thus find optimal investment strategies using portfolios tailored to an investor's risk preference.
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