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Large Language Models in equity markets: applications, techniques, and insights.

Aakanksha Jadhav1, Vishal Mirza1

  • 1Independent Researcher, New York, NY, United States.

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|September 12, 2025
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Summary

Large Language Models (LLMs) are transforming equity investing with advanced data analysis and trading. This review of 84 studies maps LLM applications, methodologies, and datasets, highlighting future research directions for AI in finance.

Keywords:
LLMSLarge Language ModelsNLPalgorithmic tradingequityfinanceinvestingstock

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Area of Science:

  • Artificial Intelligence
  • Computational Finance
  • Machine Learning in Finance

Background:

  • Large Language Models (LLMs) demonstrate significant potential to revolutionize equity investing through sophisticated data analysis, market prediction, and automated trading.
  • The rapid advancement of LLMs necessitates a comprehensive understanding of their current and potential applications within the financial sector.

Purpose of the Study:

  • To conduct a comprehensive review of 84 research studies on Large Language Model (LLM) applications in stock investing from 2022 to early 2025.
  • To categorize LLM applications by financial tasks and technical methodologies, and to analyze datasets and model types used.
  • To identify key research trends, strengths, gaps, and propose future research directions for AI-driven financial strategies.

Main Methods:

  • Systematic literature review of 84 research studies.
  • Dual-layered categorization of applications (stock price forecasting, sentiment analysis, portfolio management, algorithmic trading) and methodologies (prompting, fine-tuning, multi-agent frameworks, reinforcement learning, custom architectures).
  • Consolidation and comparison of datasets (financial statements, multimodal data) and LLM types (general-purpose vs. finance-specialized).

Main Results:

  • LLM applications in equity investing span forecasting, sentiment analysis, portfolio management, and algorithmic trading.
  • Research utilizes diverse datasets from financial statements to multimodal data, employing various LLM techniques like fine-tuning and reinforcement learning.
  • Key strengths include improved sentiment analysis and market feedback integration, while gaps exist in scalability, interpretability, and real-world validation.

Conclusions:

  • LLMs offer powerful tools for equity investing, with ongoing research exploring diverse applications and methodologies.
  • Future research should focus on hybrid models, advanced architectures with large context windows, and robust evaluation frameworks.
  • This review provides a foundational roadmap for advancing AI-driven financial strategies and practical implementation in the financial sector.