Analysis of the Application of Large Language Models in Three Major Fields: Trading and Portfolio Management, Financial Risk Management, and Financial Text Mining

Abstract

With the iteration of artificial intelligence, large language models, with their powerful capabilities in natural language understanding, logical reasoning and structured output, are influencing various fields in the financial ecosystem. This article will focus on the application of analytical large language models in three major fields: trading and portfolio management, financial risk management, and financial text mining. In trading and portfolio management, large language models understand market sentiment during the trading and investment process by building different models and can comprehensively and rationally judge whether it is suitable for trading. Through training large language models, future applications are more likely to be a "human-machine combination" model. In terms of financial risk management, different types of large language models have different performance differences. By comparing large language models, more accurate risk analysis can be obtained, and it can help people complete risk management more accurately. In the field of financial text mining, large language models, with their massive structured and unstructured data and their ability to quickly read texts, can obtain more reliable and detailed text reports. This article will systematically review the applications of large language models in three major fields.