Can Large language model analyze financial statements well? (2025)

Abstract: Since GPT-3.5’s release, large language models (LLMs) have made significant advancements, including in financial analysis. However, their effectiveness in financial calculations and predictions is still uncertain. This study examines LLMs’ ability to analyze financial reports, focusing on three questions: their accuracy in calculating financial ratios, the use of these metrics in DuPont analysis and the Z-score model for bankruptcy prediction, and their effectiveness in predicting financial indicators with limited knowledge. We used various methods, including zero-shot and few-shot learning, retrieval-augmented generation (RAG), and fine-tuning, in three advanced LLMs and compared their outputs to ground truth and expert predictions to assess their calculation and predictive abilities. The results highlight both the potential and limitations of LLMs in processing numerical data and performing complex financial analyses.

## introduction financial reporting analysis plays an important role in a company's analysis of financial health, operational efficiency, and potential risks. traditionally, this process has relied on skilled financial analysts to manually compute and interpret financial ratios derived from financial statements. established methods such as dupont analysis @xcite and the altman z-score model (altman, 1968) have been developed and refined over decades to accurately estimate profitability, financial leverage, and risk of bankruptcy. however, these techniques are time-intensive, costly, and susceptible to human error, limiting their scalability and efficiency, particularly when real-time analysis of large datasets is required. with the advent of models like gpt-3.5, large language models (llms) have shown remarkable potential to automate document analysis across domains @xcite . advanced llms, such as gpt-4 and llama, excel in natural language understanding, solving complex tasks, and generating contextual insights. their robust text processing abilities offer an opportunity to transform traditional financial analysis by offering faster and more accessible insights to analysts and decision makers @xcite . despite this promise, significant challenges persist in applying llms to quantitative tasks. studies have noted that while llms handle languagebased tasks effectively, they often struggle with precise numerical reasoning @xcite . recent advances, including fine-tuning on math datasets @xcite and using hybrid approaches that combine llms with symbolic computation tools @xcite , have improved numerical reasoning to some extent. however, their applicability to realworld financial contexts remains uncertain @xcite , as financial analysis demands not only linguistic comprehension but also accurate numerical computation from both structured and unstructured data @xcite . given these challenges, it is crucial to assess whether llms can accurately analyze financial data, especially numerical data in financial statements, to support decision-making processes in finance. this study investigates the feasibility of using llms to automate three essential tasks in financial statement analysis: (1) calculating financial ratios, (2) utilizing these ratios in established models such as dupont analysis and altman's z-score for bankruptcy prediction, and (3) forecasting critical indicators such as ebitda and sales. each task requires precise numerical computation, logical reasoning, and contextual understanding, making them ideal benchmarks for evaluating llms in financial statement analysis. by comparing the performance of various approaches (zero-shot, few-shot, retrieval-augmented generation (rag), and fine-tuning) with expert predictions and ground truth, this research aims to identify both the strengths and limitations of llms in financial tasks. in summary, this study provides a comprehensive evaluation of llms in financial statement analysis, providing insights into their strengths, limitations, and areas of improvement. the primary contributions of this study are: ## related work financial analysis is a cornerstone of corporate finance, supporting decision-making in areas such as investment, risk management, and corporate governance. traditional approaches rely on financial metrics derived from balance sheets, income statements, and cash flow statements, with ratios such as profitability, liquidity, leverage, and efficiency serving as essential indicators @xcite . these ratios form the basis for advanced analytical frameworks like dupont analysis and the altman z-score model. dupont analysis decomposes return on equity (roe) into three components: profit margin, asset turnover, and financial leverage, allowing analysts to identify sources of financial performance @xcite . similarly, the altman z-score model predicts bankruptcy risk through a weighted combination of financial ratios @xcite ). however, these methods are labor-intensive, prone to human error, and constrained in their ability to process large datasets or deliver real-time insights. advances in artificial intelligence (ai) and machine learning (ml) offer opportunities to automate financial analysis. while these methods improve efficiency and consistency, they often focus on pure numerical predictions @xcite or textual sentiment analysis @xcite , falling short of replicating traditional frameworks like dupont and z-score @xcite . large language models (llms) represent a transformative technology in this space, demonstrating exceptional abilities in natural language understanding and complex problem-solving @xcite . by mastering complex linguistic patterns, llms excel in various domains, including customer support automation, content generation, and coding assistance @xcite . in financial contexts, however, llms face unique challenges. financial documents often contain jargon, numerical data, and intricate relationships that demand both linguistic and mathematical precision @xcite . while llms like gpt-3.5 and gpt-4 have shown promise in tasks such as sentiment analysis @xcite , their numerical reasoning abilities are limited, particularly in multi-step calculations or exact numerical tasks @xcite . studies highlight that even state-of-the-art llms often miscalculate or misinterpret numerical contexts, leading to inaccurate financial projections @xcite . this limitation underscores the critical importance of precise numerical reasoning in financial decision-making, where even minor errors can lead to flawed conclusions. efforts to enhance llms' numerical reasoning have explored hybrid approaches, such as retrieval-augmented generation (rag), which integrates external databases for improved factual accuracy @xcite . fine-tuning on domain-specific datasets @xcite and techniques like chain-of-thought prompting have also been proposed to improve performance on complex financial tasks @xcite . these methods have demonstrated the potential to bridge gaps between llm capabilities and traditional financial analysis. for instance, gpt-4 has been shown to outperform human analysts in predicting earnings changes @xcite , while few-shot learning has proven effective for text classification in finance with minimal labeled data @xcite . despite these advances, no consensus exists on the optimal strategies for enhancing llms in numerical and domain-specific tasks. this paper seeks to address this gap by systematically benchmarking various methods, including zero-shot, fewshot, rag, and fine-tuning, to evaluate their efficacy in financial applications. the findings aim to establish a clearer framework for leveraging llms in finance and identify trade-offs between performance and computational efficiency. ## problem formulation the core objective of this study is to assess the effectiveness of llms in analyzing financial statements and making financial projections compared to traditional methods and experts' forecasts. building on previous research (section 2), which highlights the potential and limitations of llms in financial statements analysis and numerical reasoning, this study aims to identify the most effective models and methodologies for financial analysis tasks. to achieve this, we address the following research questions: rq1: how accurately can llms compute financial ratios based on provided financial statement data? rq2: how effectively can llms predict bankruptcy risks using methodologies such as the altman z-score model and dupont analysis? rq3: how capable are llms in forecasting critical financial indicators? rq4: what is the optimal combination of models and approaches balancing efficiency and effectiveness? to better study these questions, we prepared a special dataset to simulate a qualified and experienced financial analyst, allowing llms to acquire knowledge from this dataset through rag or finetuning. ## conclusion the study demonstrates clear performance and resource trade-offs across llama 3.2, llama 3.1, and mistral. llama 3.1 achieves the highest accuracy, particularly with fine-tuning and rag combined with few-shot learning, although it requires higher gpu memory ( 30% more than llama 3.2). this makes llama 3.1 ideal for accuracy-critical tasks where computational resources are sufficient. llama 3.2 balances performance and resource efficiency well, showing lower gpu and cpu usage, especially in fine-tuning and rag. it offers a cost-effective alternative for large-scale or resourceconstrained deployments, achieving competitive results with 20%-30% less gpu memory usage than llama 3.1. mistral shows mixed performance, excelling in retrieval-intensive tasks but underperforming in others, particularly with zero-shot or fine-tuning optimizations. its architecture suits tasks requiring efficiency but limits its effectiveness in generalpurpose financial applications. in summary, llama 3.1 is best for high-accuracy tasks, particularly in rag and few-shot setups, while llama 3.2 is a more resource-efficient choice. mistral performs well in retrieval-heavy tasks but struggles with accuracy in other areas. these results emphasize the need for model and optimization strategy selection based on task requirements and resource constraints, with future research focusing on hybrid approaches to further balance performance and resource usage.