Large Language Models Revolutionize Financial Sentiment Analysis with Improved Accuracy

The rapid advancement of large language models (LLMs) has led to a paradigm shift in designing intelligent systems. However, this shift has not been fully realized in financial sentiment analysis (FSA), where the discriminative nature of the task and lack of prescriptive knowledge on leveraging existing generative models have hindered progress. This study investigates the effectiveness of using LLMs without fine-tuning for FSA, proposing a design framework with heterogeneous LLM agents that yields better accuracies compared to alternative multi-LLM agent settings. The findings have significant implications for business and management in terms of analyzing large amounts of text data and providing insights into market sentiment.

Can Large Language Models Revolutionize Financial Sentiment Analysis?

The rapid advancement of large language models (LLMs) has led to a paradigm shift in designing intelligent systems, shifting the focus from massive data acquisition and new model training to human alignment and strategic elicitation of existing pre-trained models. However, this paradigm shift is not fully realized in financial sentiment analysis (FSA), due to the discriminative nature of this task and a lack of prescriptive knowledge on how to leverage existing generative models in such a context.

Designing Heterogeneous LLM Agents for Financial Sentiment Analysis

This study investigates the effectiveness of using LLMs without fine-tuning for FSA. Rooted in Minsky’s theory of mind and emotions, a design framework with heterogeneous LLM agents is proposed and applied to FSA. The framework instantiates specialized agents using prior guiding knowledge from both linguistics and finance. Then, a summative agent reasons on the aggregated agent discussions.

Comprehensive evaluations using six FSA datasets show that the framework yields better accuracies compared to many alternative multi-LLM agent settings, especially when the discussion contents are substantial. This study contributes to the design foundations and paves new avenues for LLM-based FSA and potentially other tasks. Lastly, implications for business and management have also been discussed.

The Importance of Financial Sentiment Analysis

Financial sentiment analysis is a prototypical task in that category and is becoming increasingly important as financial service processes and our social behavior digitalize. Companies disclose electronic versions of their annual reports, earnings calls, and announcements, and investors join online communities, discussion forums, and social media to interact with others.

The recent GameStop Saga and the popularity of a spectrum of market sentiment indexes, such as MarketPsych, have shown clear evidence that sentiment is a useful analytic tool for financial decision-making, forecasting short-term returns and volatilities, detecting fake news and fraud, and predicting risk.

The Role of Large Language Models in Financial Sentiment Analysis

Large language models have the potential to revolutionize financial sentiment analysis by providing a new paradigm for designing intelligent systems. By leveraging existing pre-trained models without fine-tuning, LLMs can be used to analyze large amounts of text data and provide insights into market sentiment.

However, there are challenges to overcome in using LLMs for FSA. For example, the discriminative nature of this task requires a deep understanding of financial concepts and terminology, which may not be fully captured by existing LLMs. Additionally, the lack of prescriptive knowledge on how to leverage existing generative models in such a context is a significant challenge.

The Design Framework for Heterogeneous LLM Agents

The design framework proposed in this study instantiates specialized agents using prior guiding knowledge from both linguistics and finance. This framework is rooted in Minsky’s theory of mind and emotions, which provides a foundation for understanding the complex interactions between human emotions and financial decision-making.

The framework consists of three main components: (1) linguistic agents that analyze text data and extract relevant information, (2) financial agents that provide domain-specific knowledge and insights, and (3) summative agents that reason on the aggregated agent discussions. This framework has been applied to FSA and shown promising results in terms of accuracy and effectiveness.

Evaluating the Design Framework

Comprehensive evaluations using six FSA datasets show that the design framework yields better accuracies compared to many alternative multi-LLM agent settings, especially when the discussion contents are substantial. The results demonstrate the potential of LLM-based FSA for providing insights into market sentiment and supporting financial decision-making.

Implications for Business and Management

The study’s findings have implications for business and management in terms of using LLMs to analyze large amounts of text data and provide insights into market sentiment. By leveraging existing pre-trained models without fine-tuning, companies can gain a competitive advantage by analyzing large amounts of text data and providing insights into market sentiment.

Additionally, the study’s findings highlight the potential for LLM-based FSA to support financial decision-making, forecasting short-term returns and volatilities, detecting fake news and fraud, and predicting risk.