Introduction

In the current landscape of corporate analysis, annual reports and other public information serve as foundational resources for understanding a company's financial health, operational activities, and strategic direction. These documents, mandated by regulatory bodies like the U.S. Securities and Exchange Commission (SEC), provide stakeholders with comprehensive insights into a firm's performance over a fiscal year. With the rise of Large Language Models (LLMs), such as GPT-4, there is growing interest in leveraging these AI tools to automate and enhance the analysis of such reports. However, LLMs face significant challenges in the financial domain, particularly due to the complexity of financial terminology, the need for numerical accuracy, and compliance with data privacy regulations. This survey note explores the use of annual reports for company analysis, current issues with LLMs in financial report analysis, provides sample queries, and examines how structured data and effective chunking strategies can address these challenges, offering a holistic view for stakeholders and researchers.

Using Annual Reports for Company Analysis

Annual reports are critical documents that offer a detailed overview of a company's activities throughout the preceding year. They are intended for shareholders, potential investors, employees, and other stakeholders to evaluate the firm's financial performance and operational strategies. The structure of annual reports, while varying by company, typically includes several key components:

• Management's Discussion and Analysis (MD&A): This section provides insights into the company's performance, risks, and strategic initiatives, offering a narrative explanation of financial results and future outlook.

• Financial Statements: These include the balance sheet, income statement, and cash flow statement, presenting a numerical snapshot of the company's financial position and performance. For instance, the income statement details revenues, expenses, and net income, while the balance sheet shows assets, liabilities, and equity.

• Notes to Financial Statements: These provide additional context and explanations for the numbers, such as accounting policies, contingent liabilities, and related party transactions, enhancing the understanding of financial data.

• Auditor's Report: This confirms the accuracy and fairness of the financial statements, typically issued by an independent auditor, ensuring compliance with generally accepted accounting principles (GAAP) or International Financial Reporting Standards (IFRS).

• Corporate Governance Information: This includes details about the board of directors, executive compensation, and governance practices, crucial for assessing the company's ethical standards and decision-making processes.

  
  

Current Issues with LLMs in Financial Report Analysis

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LLMs, powered by advanced natural language processing, have shown promise in various applications, including text summarization, question answering, and information extraction. In the financial sector, they can process large volumes of textual data from annual reports, market news, and investor communications to provide insights into market trends, perform risk assessments, and assist in investment decisions. However, their application in financial report analysis faces several challenges:

• Specialized and Complex Data: Financial reports contain domain-specific language and are governed by strict accounting standards and regulations. LLMs, primarily trained on general internet text, may struggle to interpret specialized financial terminology, such as "earnings before interest and taxes (EBIT)" or "non-GAAP measures," without fine-tuning on domain-specific datasets. Research suggests that this requires additional training on financial corpora to enhance comprehension, as noted in a study on LLMs in finance (Revolutionizing Finance with LLMs: An Overview of Applications and Insights).

• Numerical Data Handling: LLMs are designed for text processing and may not handle numerical data as effectively as specialized tools. For instance, calculating a company's revenue growth rate requires extracting and comparing figures from financial statements, a task where LLMs can make errors, especially in complex reports with footnotes and adjustments. This limitation was highlighted in a study on LLMs for financial statement analysis, which found challenges in basic arithmetic

• Accuracy and Reliability: In the financial sector, precision is paramount, as inaccurate analysis can lead to significant decision-making errors. LLMs can sometimes produce incorrect or biased outputs, particularly when dealing with ambiguous or nuanced financial narratives. This issue is critical, as evidenced by research showing the need for specialized fine-tuning to ensure accuracy in finance-specific applications

• Data Privacy and Compliance: Financial reports contain sensitive information, such as executive compensation and strategic plans, requiring adherence to data protection regulations like GDPR and industry standards. Using LLMs must ensure compliance, which can be challenging, especially with cloud-based models. Many organizations adopt hybrid approaches, combining LLMs with retrieval-augmented generation (RAG) systems to incorporate domain-specific data securely, as discussed in the same practical guide.

These challenges limit the widespread integration of LLMs in financial analysis, necessitating innovative solutions to enhance their effectiveness.

Structured Data and Chunking

To address the challenges faced by LLMs, integrating structured data and employing an effective chunking strategy can significantly enhance their performance in financial report analysis. These approaches leverage the strengths of both data organization and AI processing, offering a robust framework for analysis.

Structured Data: Financial statements within annual reports are inherently structured, with data organized in tables, such as the balance sheet, income statement, and cash flow statement. This structured data can be directly used for calculations and comparisons, ensuring numerical accuracy. For instance, to calculate the revenue growth rate, one can extract revenue figures from the income statements of consecutive years and compute the percentage change, a task more suited to data processing tools than LLMs alone. By integrating structured data with LLM text analysis, we can cross-verify information and provide more comprehensive insights. For example, if an LLM summarizes that revenue increased by 10%, structured data can confirm this by comparing the actual figures, enhancing reliability. This approach is supported by research on LLMs in finance, which emphasizes the importance of combining textual and numerical analysis for accurate financial insights (Financial Statement Analysis with Large Language Models).

Chunking Strategy: Chunking involves breaking down the annual report into smaller, manageable parts based on their sections or elements, aligning with the LLM's context window limitations. This strategy improves the relevance and efficiency of information processing by focusing the model on specific parts of the report. The structure of annual reports, as identified in various sources, typically includes:

Dividing the report into these sections allows for targeted analysis. For instance, a query about revenue would direct the LLM to the financial statements chunk, while a query about strategy would focus on the MD&A. Labeling each chunk with its section type provides additional context, enabling the LLM to better understand and utilize the information. Research on financial report chunking for retrieval-augmented generation (RAG) supports that element-type-based chunking improves RAG results, enhancing accuracy and context in question-answering tasks. This approach ensures that the LLM processes relevant data efficiently, mitigating the risk of exceeding its context window and improving response quality.

The integration of annual reports and LLMs offers a promising avenue for automating and enhancing company analysis, providing stakeholders with timely and insightful information. However, challenges such as handling specialized financial data, ensuring numerical accuracy, and complying with privacy regulations must be addressed. By leveraging structured data from financial statements for calculations and employing a chunking strategy that divides reports into logical sections, we can overcome these hurdles. This combined approach not only enhances the capabilities of LLMs but also ensures that the analysis is reliable, compliant, and efficient, paving the way for advanced financial analysis tools in the future.