  Portfolio Selection is an important real-world financial task and has
attracted extensive attention in artificial intelligence communities. This
task, however, has two main difficulties: (i) the non-stationary price series
and complex asset correlations make the learning of feature representation very
hard; (ii) the practicality principle in financial markets requires controlling
both transaction and risk costs. Most existing methods adopt handcraft features
and/or consider no constraints for the costs, which may make them perform
unsatisfactorily and fail to control both costs in practice. In this paper, we
propose a cost-sensitive portfolio selection method with deep reinforcement
learning. Specifically, a novel two-stream portfolio policy network is devised
to extract both price series patterns and asset correlations, while a new
cost-sensitive reward function is developed to maximize the accumulated return
and constrain both costs via reinforcement learning. We theoretically analyze
the near-optimality of the proposed reward, which shows that the growth rate of
the policy regarding this reward function can approach the theoretical optimum.
We also empirically evaluate the proposed method on real-world datasets.
Promising results demonstrate the effectiveness and superiority of the proposed
method in terms of profitability, cost-sensitivity and representation
abilities.
