arXiv cs.AI· Hrishikesh Singh, Aarti Sharma, Millie Pant·· 12 小时前AI 评分20
面向 Actor-Critic 智能体的金融上下文丰富策略：一种用于股票交易的深度强化学习方法
Financial Context-Rich Strategy for Actor-Critic based Agents: A Deep Reinforcement Learning Approach for Stock Trading
AI 导读
该研究提出一种基于情感分析部分与深度注入的深度强化学习方法，用于股票交易。实验对比了仅策略优化的 A2C、PPO 与结合 Q-learning 的 DDPG、TD3、SAC 两类 actor-critic 智能体，发现后者在情感深度注入后，在利润最大化和风险规避交易上均表现出一致且显著的提升。
正文 · 原文
Abstract:In modern financial markets, more than 3/4th of the large volume trade is executed by algorithmic trading based on advanced trading strategies to attain higher precision and speed than traditional human traders. The conventional strategies focused on price-based indicators are inadequate in capturing the market dynamism arising from real-world events such as social sentiments, political instability, or sudden economic shifts. With the rise of Deep Reinforcement Learning (DRL) and data availability, there is a requirement for more efficient algorithms that can incorporate these external factors in trading strategies. DRL has shown potential in simulating real-world scenarios and in simplifying complex decision-making since DRL agents can automate the trading process i.e. executing trades in a manner comparable to human traders. They have shown reliable performance since they can update their policies based on risk and quantitative factors. In this paper, we propose a DRL approach based on partial and deep-infusion of sentimental analysis. It features distinct policy designs of actor-critic-based agents: only policy optimization (A2C & PPO) and policy optimization along with Q-learning principles (DDPG, TD3 & SAC). Our findings reveal, that the agents based on policy optimization combined with Q-learning, significantly and consistently demonstrate improved performance upon sentiment deep-infusion in profit maximization as well as risk-averse trading.
| Subjects: | Artificial Intelligence (cs.AI); Computational Finance (q-fin.CP); Portfolio Management (q-fin.PM) |
| Cite as: | arXiv:2610.32237 [cs.AI] |
| (or arXiv:2610.32237v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.32237 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Hrishikesh Singh [view email]
[v1]
Fri, 23 Oct 2026 06:46:09 UTC (865 KB)
来源：arXiv cs.AI · arxiv.org