An actor-critic-based portfolio investment method inspired by benefit-risk optimization

Abstract

How to get maximal benefit within a range of risk in securities market is a very interesting and widely concerned issue. Meanwhile, as there are many complex factors that affect securities’ activity, such as the risk and uncertainty of the benefit, it is very difficult to establish an appropriate model for investment. Aiming at solving the curse of dimension and model disaster caused by the problem, we use the approximate dynamic programming to set up a Markov decision model for the multi-time segment portfolio with transaction cost. A model-based actor-critic algorithm under uncertain environment is proposed, where the optimal value function is obtained by iteration on the basis of the constrained risk range and a limited number of funds, and the optimal investment of each period is solved by using the dynamic planning of limited number of fund ratio. The experiment indicated that the algorithm could get a stable investment, and the income could grow steadily.