A Study on Performance Improvement of Portfolio Asset Allocation Using Recurrent Reinforcement Learning

The present invention relates to a portfolio asset allocation performance increasing method using regression enhancement learning. In addition, in order to increase the performance of a regression enhancement learning model for portfolio asset allocation, the present invention presents a concrete implementation model for a method of generating and utilizing asset prediction value data and artificially generated data in addition to past asset data, and proves that such a model is effective through experiments. The regression enhancement learning model generating and utilizing the asset prediction data and the artificially generated data created by the present invention is implemented by long short-term memory (LSTM). The asset prediction value data is generated by generating and using a virtual prediction value for the rise and fall of asset prices based on prediction accuracy over the period of operation and the artificially generated data is generated by using a Gaussian process.