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

TL;DR

This survey reviews how Large Language Models are transforming financial decision-making by processing vast data, improving analysis, and enabling real-time insights across various investment and market analysis tasks.

Contribution

It categorizes recent research on LLM applications in finance into four frameworks, providing a structured overview of methods, challenges, and future directions.

Findings

LLMs enhance stock selection and risk assessment.

Hybrid methods improve financial forecasting accuracy.

Llm integration faces challenges like data quality and model interpretability.

Abstract

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.