Stock market prediction through deep reinforcement learning

In this study, attempts are made to utilize the deep reinforcement learning (DRL)-based models for predicting stock market and investment strategy optimization. We compare three DRL architectures, Deep Q-Network (DQN), Double Deep Q-Network (Double DQN) and Dueling Deep Q-Network (Dueling DQN), on the Pakistan Stock Exchange. We adopt an empirical approach from a set of 30 stocks in combination with descriptive statistical analysis to evaluate representativeness. The results indicate that the three kinds of models can all earn positive profits and Double DQN maintains the most average profits in both training dataset and test dataset. We then attempt to mitigate the potential overfit and analyze implications for trading strategies. In addition, we consider personality and nonconvex heuristic money and risk management in a mathematical agent-based model to study the foraging behavior of a population of interacting, moving agents in an assigned background.