2017 Volume 82 Issue 1 Pages 2-40
This paper considers investment methods with deep learning in neural networks. In particular, as one can create various investment strategies by different specifications of a loss function, the current work presents two examples based on return anomalies detected by supervised deep learning (SL) or profit maximization by deep reinforcement learning (RL). It also applies learning of individual asset return dynamics to portfolio strategies.
Moreover, an empirical study shows that the investment performance are quite sensitive to exogenously specified items such as frequency of input data (e.g.monthly or daily returns), selection of a learning method, update of learning, number of layers in a network and number of units in intermediate layers. Especially, it is observed that RL provides relatively fine records in portfolio investment.
