mlsanityData to 14 Oct 2026

A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis

arXiv 2606.10412 ↗First posted 9 Jun 2026cs.AIcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously. This paper presents a groundbreaking unified framework that seamlessly integrates Proximal Policy Optimization for robo-advisory systems, advanced time-series prediction models for high-frequency trading, in-context learning mechanisms for dynamic investment advisory, game-theoretic approaches for competitive banking scenarios, and unified embeddings for cross-modal financial sentiment analysis. Our comprehensive framework addresses the critical gap in existing literature where these technologies have been developed in isolation, failing to leverage their synergistic potential. Through extensive experimentation across multiple financial datasets and real-world scenarios, we demonstrate that our integrated approach achieves superior performance compared to specialized single-domain systems. Specifically, our framework shows a 23.7% improvement in portfolio optimization metrics, reduces prediction error in high-frequency trading by 31.2%, enhances investment recommendation accuracy by 18.9%, optimizes competitive banking strategies with a 27.4% increase in Nash equilibrium convergence speed, and improves sentiment analysis accuracy by 15.6% through cross-modal fusion. The theoretical foundation of our work establishes convergence guarantees for the integrated optimization problem, while our empirical results validate the practical applicability across diverse financial institutions. This research not only advances the state-of-the-art in financial AI but also provides a blueprint for developing comprehensive intelligent systems that can adapt to the complex, interconnected nature of modern financial markets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 24 of its 38 references that are in the map. First posted on arXiv 9 Jun 2026.

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24 of 38 references.

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Same direction, newest first

  1. 2025Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy15 Nov

Other references · 23

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2017Proximal Policy Optimization Algorithmscited by 15,215
  4. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  5. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  6. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  7. 2020Gradient Surgery for Multi-Task Learningcited by 1,256
  8. 2023BloombergGPT: A Large Language Model for Financecited by 939
  9. 2017Tensor Fusion Network for Multimodal Sentiment Analysiscited by 433
  10. 2019Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019cited by 273
  11. 2017A Unified Game-Theoretic Approach to Multiagent Reinforcement Learningcited by 268
  12. 2023FinGPT: Open-Source Financial Large Language Modelscited by 253
11 more
  1. 2023PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Financecited by 222
  2. 2020FinBERT: A Pretrained Language Model for Financial Communicationscited by 161
  3. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 128
  4. 2018The Mechanics of n-Player Differentiable Gamescited by 118
  5. 2021AI in Finance: Challenges, Techniques and Opportunitiescited by 81
  6. 2018Universal features of price formation in financial markets: perspectives from Deep Learningcited by 60
  7. 2021FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Financecited by 58
  8. 2017Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methodscited by 49
  9. 2020An Application of Deep Reinforcement Learning to Algorithmic Tradingcited by 30
  10. 2021Reinforcement Learning for Quantitative Tradingcited by 24
  11. 2018A Generalised Method for Empirical Game Theoretic Analysiscited by 15

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