A Deep Temporal Factor Analysis Method for Large Scale Financial Portfolio Selection

Existing machine learning methods are effective in portfolio optimization on a small pool of assets. This is still not optimal because a larger number of assets in markets offers more opportunities for investors. However, existing methods are usually not scalable to large amount of assets which brings new challenges of high dimensionality and computing complexity. In this paper, we present a neural network temporal factor analysis (NN-TFA) model for dimensionality reduction and it enables us to build a scalable deep reinforcement learning method for large-scale portfolio management. Traditional TFA models the relation between asset prices and real economic activities via a small set of independent hidden factors. NN-TFA is developed from the traditional TFA by replacing the linear autoregressive model over the hidden factors with a neural network function, which well captures the complicated temporal patterns. The hidden factors are then sent to a policy network to generate portfolio weights. A calibration module to extract information from other assets features and a ratio module to catch the trend of the selected assets pool are proposed to enhance the performance of the policy network. Extensive tests show that our methods are capable of handling large-scale datasets and achieving promising results.