Integrating Large Language Models in Financial Investments and Market Analysis: A Survey
It addresses the problem of enhancing financial decision-making for investors and analysts by integrating LLMs, but it is incremental as it synthesizes existing literature rather than presenting new findings.
This survey reviews recent research on applying Large Language Models (LLMs) to financial investments and market analysis, categorizing contributions into frameworks like LLM-based pipelines and hybrid methods, and highlighting capabilities in areas such as stock selection and risk assessment.
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.