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Financial Context-Rich Strategy for Actor-Critic based Agents: A Deep Reinforcement Learning Approach for Stock Trading — arXiv:2610.32237

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.32237
License (as archived): CC0-1.0
Archived: 2026-10-26T19:50:29.650Z
SHA-256 of the archived text: 10bb1f6bccae64bfa1d38e86f5a87516073ee6de047abadb7aa2e8dd2c18bc78
Stable URL of this page: https://metraipolis.com/documento/dc_fff27d712704

How to cite

Financial Context-Rich Strategy for Actor-Critic based Agents: A Deep Reinforcement Learning Approach for Stock Trading — arXiv:2610.32237 — arXiv (en), https://arxiv.org/abs/2610.32237 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fff27d712704 (Metraipolis archive dc_fff27d712704, sha256 10bb1f6bccae64bfa1d38e86f5a87516073ee6de047abadb7aa2e8dd2c18bc78)

BibTeX

@misc{dcfff27d712704,
  title = {Financial Context-Rich Strategy for Actor-Critic based Agents: A Deep Reinforcement Learning Approach for Stock Trading — arXiv:2610.32237},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.32237},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fff27d712704; sha256 10bb1f6bccae64bfa1d38e86f5a87516073ee6de047abadb7aa2e8dd2c18bc78},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.32237. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computational Finance (q-fin.CP); Portfolio Management (q-fin.PM).
Pagina del lavoro: https://arxiv.org/abs/2610.32237 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-26T19:45:33.297Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).