Stock Ranking with Multi-Task Learning

Abstract

Stock prediction, aiming at predicting the future trends of stocks, plays a key role in stock investment. Towards the investment target, the primary task is selecting the stocks with potentials to obtain the highest excess returns, always regarded as stock ranking. List-wise stock ranking is able to consider the relative comparisons of multiple stocks, approaching the essence of stock ranking most. However, most existing methods fail in list-wise stock ranking, because the information complexity and small number of samples bring in training difficulties.To address these limitations, a novel Deep Multi-Task Learning (DMTL) solution is proposed, called Multi-Task Stock Ranking (MTSR). It utilizes the joint learning framework of DMTL to learn the list-wise stock ranking with the enhancements of auxiliary tasks. With DMTL, the easily-trained tasks act as learning guider, providing extra gradient backpropagation, to help learn the hardly-trained list-wise ranking task. Additionally, Task Relation Attention is utilized to capture the dynamic task relations to achieve better knowledge transfer between tasks. The experiments conducted on real-world stock datasets demonstrate the superiority of MTSR over several state-of-the-art methods.