Abstract—Stock portfolio optimization is the process of
constant re-distribution of money to a pool of various stocks. In
this paper, we will formulate the problem such that we can
apply Reinforcement Learning for the task properly. To
maintain a realistic assumption about the market, we will
incorporate transaction cost and risk factor into the state as
well. On top of that, we will apply various state-of-the-art Deep
Reinforcement Learning algorithms for comparison. Since the
action space is continuous, the realistic formulation were tested
under a family of state-of-the-art continuous policy gradients
algorithms: Deep Deterministic Policy Gradient (DDPG),
Generalized Deterministic Policy Gradient (GDPG) and
Proximal Policy Optimization (PPO), where the former two
perform much better than the last one. Next, we will present
the end-to-end solution for the task with Minimum Variance
Portfolio Theory for stock subset selection, and Wavelet
Transform for extracting multi-frequency data pattern.
Observations and hypothesis were discussed about the results,
as well as possible future research directions.
Index Terms—Reinforcement learning, stock trading, deep
learning, deterministic policy gradient, proximal policy
optimization, stock portfolio optimization
Le Trung Hieu is with National University of Singapore, Singapore (email:
e0072405@u.nus.edu).
[PDF]
Cite: Le Trung Hieu, "Deep Reinforcement Learning for Stock Portfolio
Optimization," International Journal of Modeling and Optimization vol. 10, no. 5, pp. 139-144, 2020.
