mlsanityData to 14 Oct 2026

Integrating Large Language Models in Financial Investments and Market Analysis: A Survey

arXiv 2507.01990 ↗First posted 29 Jun 2025q-fin.GN cs.AI cs.LGcited by 4

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Abstract (arXiv)

Large Language Models (LLMs) have been employed in financial decision making, enhancing analytical capabilities for investment strategies. Traditional investment strategies often utilize quantitative models, fundamental analysis, and technical indicators. However, LLMs have introduced new capabilities to process and analyze large volumes of structured and unstructured data, extract meaningful insights, and enhance decision-making in real-time. This survey provides a structured overview of recent research on LLMs within the financial domain, categorizing research contributions into four main frameworks: LLM-based Frameworks and Pipelines, Hybrid Integration Methods, Fine-Tuning and Adaptation Approaches, and Agent-Based Architectures. This study provides a structured review of recent LLMs research on applications in stock selection, risk assessment, sentiment analysis, trading, and financial forecasting. By reviewing the existing literature, this study highlights the capabilities, challenges, and potential directions of LLMs in financial markets.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 35 of its 48 references that are in the map. First posted on arXiv 29 Jun 2025.

Builds on

35 of 48 references.

References · 35

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23 more
  1. 2024AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Frameworkcited by 47
  2. 2024Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Modelscited by 36
  3. 2024Can Large Language Models Beat Wall Street? Unveiling the Potential of AI in Stock Selectioncited by 29
  4. 2024When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environmentscited by 26
  5. 2024Automate Strategy Finding with LLM in Quant Investmentcited by 26
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  7. 2025MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agentscited by 16
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  19. 2023Multimodal Gen-AI for Fundamental Investment Researchcited by 7
  20. 2025LLM-Based Routing in Mixture of Experts: A Novel Framework for Tradingcited by 7
  21. 2024Combining Financial Data and News Articles for Stock Price Movement Prediction Using Large Language Modelscited by 6
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025UltraHorizon: Benchmarking Agent Capabilities in Ultra Long-Horizon Scenarioscited by 26
  2. 2026Unlocking Data Value in Finance: A Study on Distillation and Difficulty-Aware Trainingcited by 4
  3. 2026Even GPT-5.2 Can't Count to Five: The Case for Zero-Error Horizons in Trustworthy LLMscited by 1
  4. 2026SoK: Trading Agents or Market Crashers? Dissecting Robustness and Security Failures in Academic Financial LLM Trading Schemescited by 0