Corpus ID: 235165886

A parallel-network continuous quantitative trading model with GARCH and PPO

@inproceedings{Wang2021APC,
  title={A parallel-network continuous quantitative trading model with GARCH and PPO},
  author={Zhishun Wang and Wei Lu and Kaixin Zhang and Tianhao Li and Zixi Zhao},
  year={2021}
}
It is a difficult task for both professional investors and individual traders continuously making profit in stock market. With the development of computer science and deep reinforcement learning, Buy&Hold (B&H) has been oversteped by many artificial intelligence trading algorithms. However, the information and process are not enough, which limit the performance of reinforcement learning algorithms. Thus, we propose a parallel-network continuous quantitative trading model with GARCH and PPO to… Expand

Figures and Tables from this paper

References

SHOWING 1-10 OF 10 REFERENCES
A multi-layer and multi-ensemble stock trader using deep learning and deep reinforcement learning
TLDR
This paper proposes a multi-layer and multi-ensemble stock trader, which clearly outperforms all the considered baselines, and even the conventional Buy-and-Hold strategy, which replicates the market behaviour. Expand
A Q-learning agent for automated trading in equity stock markets
TLDR
Two different ways to represent the discrete states of the environment are proposed and the two proposed models outperformed the Buy-and-Hold and Decision-Tree based trading strategy in terms of profitability. Expand
A hybrid deep learning approach by integrating LSTM-ANN networks with GARCH model for copper price volatility prediction
TLDR
The empirical results show that the GARCH forecasts can serve as informative features to significantly increase the predictive power of the neural network model, and the integration of the LSTM and ANN networks is an effective approach to construct useful deep neural network structures to boost the prediction performance. Expand
Stock price prediction using deep learning and frequency decomposition
TLDR
The practical findings confirm this claim and indicate that CNN alongside LSTM and CEEMD or EMD could enhance the prediction accuracy and outperform other counterparts and the suggested algorithm withCEEMD provides better performance compared to EMD. Expand
A novel CNN-DDPG based AI-trader: Performance and roles in business operations
TLDR
A novel “Reinforcement Learning” (RL) framework based AI-trader is built that is shown to outperform other methods with the use of real stock-index future data and an actor-critic RL algorithm called DDPG is adopted to find the optimal policy. Expand
Stock return prediction under GARCH — An empirical assessment
The GARCH model and its numerous variants have been applied widely both in the financial literature and in practice. For purposes of quasi maximum likelihood estimation, innovations to GARCHExpand
Proximal Policy Optimization Algorithms
We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objectiveExpand
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting
TLDR
Experiments on two real-world datasets from the Caltrans Performance Measurement System demonstrate that the proposed ASTGCN model outperforms the state-of-the-art baselines. Expand
Reinforcement learning - an introduction. Adaptive computation and machine learning
  • 1998
Deep reinforcement learning based trading agents: Risk curiosity driven learning for financial rules-based policy