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What Are The Potential Applications Of LLM In The Financial Sector? Verification Through 11 Tasks Including Financial Engineering, Market Forecasting, And Risk Management Using The GPT-4

What Are The Potential Applications Of LLM In The Financial Sector? Verification Through 11 Tasks Including Financial Engineering, Market Forecasting, And Risk Management Using The GPT-4

Large Language Models

3 main points
✔️ Potential applications of large-scale language models in the financial sector: large-scale language models, including GPT-4, have excellent text processing, sentiment analysis, and zero-shot learning capabilities, offering a wide range of potential applications in the financial industry, including analysis of market trends, risk assessment, and investment decision support.
✔️ Limitations and Directions for Improvement of Large-Scale Language Models: Limitations in direct computational tasks, optimization, and quantitative trading, but expect future improvements in the development of hybrid systems through improved expertise and integration with quantitative models.
✔️ Future Prospects and Technological Advancements: ample potential to revolutionize the way financial markets are analyzed and traded by merging large-scale linguistic models with quantitative models, improving the interpretability and reliability of outputs, and predicting market trends based on historical data and current events.

Revolutionizing Finance with LLMs: An Overview of Applications and Insights
written by Huaqin ZhaoZhengliang LiuZihao WuYiwei LiTianze YangPeng ShuShaochen XuHaixing DaiLin ZhaoGengchen MaiNinghao LiuTianming Liu
(Submitted on 22 Jan 2024)
Subjects: Computation and Language (cs.CL)


The images used in this article are from the paper, the introductory slides, or were created based on them.


In recent years, breakthroughs in AI technology related to natural language understanding and generation have been achieved through the remarkable development of OpenAI's GPT and other large-scale language models in the field of natural language processing. These models have demonstrated the ability to handle sophisticated tasks such as understanding complex contexts, answering questions, and generating content through advanced computational power and algorithms. In particular, their potential in the financial sector is becoming increasingly apparent.

The financial sector is a specialized and complex area requiring large amounts of data analysis, forecasting, and decision making. The ability of large-scale language models to process large volumes of textual data has a wide range of potential applications, from financial reporting, market news, investor communication analysis, market trend insights, risk assessment, and even investment decision support. The ability of large-scale language models to process natural language queries and provide immediate financial advice and support is also extremely useful in the financial services industry.

However, the application of large-scale language models to the financial sector faces multiple challenges, including understanding specialized and complex financial data and sophisticated model understanding of financial terminology, regulations, and market dynamics. In addition, high-risk financial decision making requires accuracy and reliability in forecasting.

To address these challenges, researchers and developers are refining algorithms for large-scale language models to improve knowledge understanding and processing power in specialized domains. The combination of expert systems and manual review is expected to improve the accuracy and reliability of model applications in the financial domain.

This paper focuses on the question of how to address the challenges unique to the financial sector and how to apply the success of large-scale language models in a wide range of areas to the financial industry. It provides a comprehensive survey of the latest advances in the areas of financial engineering, forecasting, risk management, and real-time question answering, and an overview of the technical approaches and potential that large-scale language models can fulfill in the financial sector. In addition, it evaluates the performance of GPT-4, summarizes research findings, addresses open questions, and provides insight into future directions in the field.

Financial Sector Tasks

The first is a task related to financial engineering. Quantitative trading, portfolio optimization, and robo-advisors are rapidly evolving areas in today's financial industry. They combine finance, mathematics, and computer science to create innovative financial strategies and products. And breakthroughs with large-scale language models are having a significant impact on these areas as well.

While traditional mathematical and statistical models have dominated quantitative trading to predict market movements, the advent of large-scale linguistic models has opened up new possibilities to leverage implicit sentiment information from unstructured data sources. This allows us to capture subtle nuances from analyst reports and market news and reflect them in our investment strategies. Large-scale language models offer a new paradigm in the investment decision process, allowing for contextual understanding and interpretation of complex financial jargon that is often overlooked in traditional quantitative analysis.

Portfolio Optimization utilizes large-scale linguistic models to analyze the wealth of unstructured data available from market reports and news articles to make risk assessments. This allows us to respond to geopolitical current events and market abruptness that traditional models can easily overlook, allowing us to take a more adaptive and informed asset allocation strategy.

Robo-advisors also make financial investing easy by combining large-scale language models with AI. They will be able to customize portfolios to the needs of individual users and respond quickly to market fluctuations. At the same time, the limitations of personalization and considerations of privacy and data security will be important considerations in future developments.

The second task is related to financial forecasting. From merger and acquisition forecasting to debt default prediction, advanced technologies such as natural language processing and large-scale language models are playing an important role. These technologies provide new means of analyzing a wide range of textual data and predicting complex movements in financial markets.

In predicting merger and acquisition activity, large language models analyze trends and changes in strategy through financial reports and news articles that suggest signs of upcoming M&A activity. Sentiment analysis of market commentary and financial reports can detect changes in market sentiment toward a particular company or sector, providing valuable insights that can foreshadow potential M&A activity. Speculative information on social media and changes in public sentiment can also be analyzed as early indicators of M&A trends.

In predicting default, the language model assesses a company's financial health from a variety of textual sources. Financial disclosures, news articles, and even statements by corporate leaders are analyzed to detect early signs of financial crisis. Complementing traditional numerical modeling, early signs of a company's deteriorating financial condition are detected through detailed analysis of market sentiment and tone.

In addition, there are many examples of the use of the GPT-4, which has attracted much attention in recent years, reporting excellent results in financial forecasting. In particular, forecasting stock price trends using the GPT-4 is a complex process that requires comprehensive analysis of diverse data sources.

Until now, econometric models such as ARIMA and machine learning algorithms have dominated stock price forecasting in academic research and the financial industry, but these methods have had challenges in quickly responding to rapid changes in the market and in providing a clear basis for forecasts. Market movements are difficult to predict due to their stochastic nature and multiple influencing factors, and conventional quantitative models have difficulty capturing rapid changes in market sentiment and the global economy.

The GPT-4 is also gaining attention in market forecasting. Through the processing and interpretation of a variety of textual data, including financial news, economic indicators, and social media trends, deep insights into market sentiment and trends can be gained. Below are some examples of GPT-4 applications and benefits in market forecasting

  • Examples of GPT-4 applications in market forecasting
    • Analyze financial news and reports: Quickly analyze financial news and reports to gain a comprehensive picture of market conditions and potential trends.
    • Social media sentiment analysis: analyzing the sentiment of posts and tweets provides an important indicator of market trends by gauging public opinion and investor sentiment.
    • Interpretation of economic indicators: Interpret textual data related to economic indicators that influence market forecasts, such as inflation rates and GDP growth.
    • Scenario simulation: simulates various market conditions and outcomes based on historical data to support risk assessment and decision-making.
    • Real-time data processing: Responds to rapid market changes and provides timely information needed for forecasting.
  • Advantages of GPT-4 in Market Analysis
    • Enhanced Forecasting Capabilities: Analyzes diverse data sources to provide more accurate forecasts than traditional methods.
    • Deeper understanding of the market: Through analysis of text data, we can capture what numerical data alone cannot.
    • Understand market dynamics.
    • Rapid Adaptation to Market Changes: The AI-driven nature of GPT-4 allows it to respond quickly to new information and market changes.
    • Customizable analysis: Focus on specific sectors, geographies, or data types.
    • Reducing human bias: Data-driven insights provide more objective and reliable market forecasts.

The third task is related to financial risk management. These include credit scoring, ESG scoring, fraud detection, and compliance checks. These are important processes to maintain financial stability, evaluate investments and sustainability, and protect against criminal activity.

Credit scoring is an extremely important credit and risk assessment of individuals and firms in the financial sector. Previous evaluation methods have relied on rule-based and machine learning algorithms, but these methods are specialized for specific purposes and are difficult to generalize. The introduction of large-scale language models has opened up new possibilities in this area.

Environmental, Social, and Governance (ESG) scoring is an important tool in corporate sustainability assessment. It is essential for investors to assess the extent to which a company is fulfilling its social and environmental responsibilities. Large-scale linguistic models enable these assessments to be more accurate and objective.

In addition, as digital wallet technology evolves, the detection of fraudulent activity becomes increasingly important. Large-scale linguistic models play an important role in efficiently identifying suspicious transactions and protecting against financial crime.

In addition, compliance checks are a major challenge in the financial industry, as regulations are constantly changing. With its zero-shot learning capability, the L-Large Language Model has the potential to quickly adapt to new standards and assist in processes such as auditing, trade monitoring, and financial reporting. This will enable financial institutions to efficiently meet the latest regulatory requirements.

The fourth task is related to financial real-time question and answer. This task is a particularly important area in financial education, and the GPT-4 has the potential to significantly improve the quality of education in this area.

With its advanced natural language processing capabilities, GPT-4 can explain complex financial concepts in an easy-to-understand manner, providing learners with a customized learning experience and stimulating user interaction. Complex terms such as financial market securities and risk management can be explained in terms that even beginning learners can easily understand. In addition, the contents of the material can be adjusted according to the learner's progress, facilitating practical learning through interactive Q&A and simulations. On the other hand, the information provided by GPT-4 relies on an existing knowledge base, which limits its ability to immediately respond to the latest financial trends. Ethical and compliance considerations must also be taken into account to ensure the accuracy and transparency of the financial information provided.

GPT-4 Task Evaluation of the Financial Sector

This paper presents methods using one-shot learning and zero-shot prompting to evaluate the performance of GPT-4 in the financial sector.

Six diverse datasets have been selected to assess the broad capabilities of the GPT-4 in the financial sector. These include a wide range of text types, including news articles, analytical reports, and social media posts such as tweets. In addition, we incorporate time-series data, tabular data, and textual content to build practical financial tasks that reflect real-world financial scenarios.

We evaluate the task of identifying sentiment in financial news. Critical to financial analysis, we follow the FLUE framework and use the Financial Phrase Bank (FPB) dataset and FiQA-SA. the FPB dataset is a collection of financial news excerpts, each annotated by experts in the field with positive, annotated with positive, negative, and neutral sentiment categories by experts in the field. FiQA-SA, on the other hand, is an extensive dataset used primarily to quantify the sentiment of English-language financial reporting and microblog content using a sentiment intensity scale ranging from -1 to 1.

The figure below shows an example of sentiment analysis performed on 970 data points from the FiQASA task set, achieving 79 % accuracy by using GPT-4.

Next, we evaluate the task of identifying named entities in finance. This task aims to identify important financial entities, such as individuals, organizations, and places. These entities are critical to the development of the financial knowledge graph; the NER dataset consists of financial agreement statements filed with the U.S. Securities and Exchange Commission and includes entities classified as LOCATION, ORGANISATION, and PERSON.

We are also evaluating the task of financial question answering. This task automatically responds to financial queries based on the data provided. Two datasets are used for this: FinQA and ConvFinQA, which provide question and answer pairs annotated by experts related to earnings reports of S&P 500 companies, and ConvFinQA, which contains multi-turn dialogues about these earnings reports ConvFinQA includes a multi-turn dialogue on these earnings reports.

We evaluate the task of forecasting stock price trends. Forecasting stock price trends is an important financial task that can be of great value in developing investment strategies. This task is tackled as a binary classification assignment that predicts stock price trends based on historical prices and related tweets. The widely used BigData22 dataset is used for this analysis.

The figure below shows an example of stock price forecasting performed on 1,470 data points from the BigData task set. 51% accuracy was achieved by using GPT-4.

In addition, in evaluating financial tasks utilizing the GPT-4, this paper tests a wide variety of prompting strategies, including vanilla zero shot prompting, chain-of-sort (CoT) enhanced zero shot prompting, and one-shot prompting. We are analyzing how these strategies affect GPT performance in financial tasks. The formulation of prompts is important for effective interaction with large language models.

Experimental results

The table below shows the zero-shot and few-shot performance of various large-scale language models on the specified data sets. The experimental results clearly show that the large-scale language models have the ability to perform precisely on the validated financial tasks. Based on the data collected, it is shown that the large-scale language model demonstrates remarkable ability in zero-shot learning, mathematical reasoning ability, and in its strong suit, sentiment analysis of language. The effectiveness of the financial task has been quantitatively evaluated by comparing the results to actual financial data and historical market performance, showing useful and practical results in areas such as financial engineering, risk assessment, and market trend analysis. It demonstrates that large-scale language models have great potential for application in the field of finance.


This paper examines the potential and limitations of large-scale language models in 11 different financial tasks using the GPT-4. It is clear that large-scale language models have remarkable capabilities in text processing, sentiment analysis, and zero-shot learning capabilities. The ability of large-scale language models to efficiently analyze and interpret a wide range of textual data will play an essential role in deciphering market dynamics and investor sentiment.

On the other hand, it is also important to recognize the limitations of large-scale language models in direct computational tasks, especially in optimization and quantitative trading. These models are expected to play only a supplementary role and work in a way that contributes to existing models that deal with quantitative variables through sentiment analysis. It is expected that their usefulness will be further demonstrated by linking them with functional tools, which is a recent trend. Currently, large-scale language models are not an independent solution for computational finance tasks and are a powerful tool that aims to enhance existing models.

Future improvements could be made by integrating large-scale language models with advanced quantitative models. The development of hybrid systems that combine the text processing power of large-scale language models with sophisticated quantitative trading algorithms seems promising. Another important challenge is to improve the interpretability and reliability of the output of large-scale language models in a financial context to ensure that the insights generated are accurate and actionable. Furthermore, the application of large-scale language models to forecast market trends based on historical data and current events can open up new areas of financial forecasting. In the future, the integration of qualitative and quantitative analysis could revolutionize the way financial markets are analyzed and traded.

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