Market Turbulence Prediction and Risk Control with Improved A3C Reinforcement Learning

Authors:
Preprint
Publisher website
Google Scholar
Abstract
As the complexity and volatility of global financial markets increase, traditional market forecasting and risk control methods can no longer effectively cope with the various uncertainties in the modern financial environment. Therefore, financial market prediction and risk control based on deep reinforcement learning has become a research hotspot. This paper proposes a market turbulence prediction and risk control model based on the improved A3C algorithm, aiming to improve the accuracy of market volatility prediction and the precision of risk control through reinforcement learning methods. Through experimental verification on multiple public financial data sets, the model proposed in this paper is superior to traditional reinforcement learning algorithms such as Q-learning, DQN, and PPO in terms of prediction accuracy, risk control accuracy, and convergence speed, showing strong learning ability and adaptability. In terms of model design, the parallel mechanism and advantage function are combined to significantly improve the training efficiency and model stability. Experimental results show that the model proposed in this paper can not only effectively capture the patterns of market turbulence but also adjust strategies according to real-time market data, thereby achieving effective control of risks. In addition, this paper also analyzes the contribution of each module to the model performance through ablation experiments, further verifying the effectiveness and advantages of the model. Finally, this paper looks forward to future research on market forecasting and risk control technology based on deep reinforcement learning. It is believed that with the continuous advancement of technology, AI-based financial risk management will play an increasingly important role in practical applications. It has become a powerful tool to enhance the stability of financial markets and respond to unexpected market risks.

This publication has 9 references indexed in Scilit: