Financial Statement Analysis

Note on pre-taxation reported data by UK FTSE-listed companies. A search for Benford’s laws compatibility ArXiv ID: 2509.09415 “View on arXiv” Authors: Marcel Ausloos, Probowo Erawan Sastroredjo, Polina Khrennikova Abstract Pre-taxation analysis plays a crucial role in ensuring the fairness of public revenue collection. It can also serve as a tool to reduce the risk of tax avoidance, one of the UK government’s concerns. Our report utilises pre-tax income ($PI$) and total assets ($TA$) data from 567 companies listed on the FTSE All-Share index, gathered from the Refinitiv EIKON database, covering 14 years, i.e., the period from 2009 to 2022. We also derive the $PI/TA$ ratio, and distinguish between positive and negative $PI$ cases. We test the conformity of such data to Benford’s Laws,- specifically studying the first significant digit ($Fd$), the second significant digit ($Sd$), and the first and second significant digits ($FSd$). We use and justify two pertinent tests, the $χ^2$ and the Mean Absolute Deviation (MAD). We find that both tests are not leading to conclusions in complete agreement with each other, - in particular the MAD test entirely rejects the Benford’s Laws conformity of the reported financial data. From the mere accounting point of view, we conclude that the findings not only cast some doubt on the reported financial data, but also suggest that many more investigations be envisaged on closely related matters. On the other hand, the study of a ratio, like $PI/TA$, of variables which are (or not) Benford’s Laws compliant add to the literature debating whether such indirect variables should (or not) be Benford’s Laws compliant. ...

Towards Competent AI for Fundamental Analysis in Finance: A Benchmark Dataset and Evaluation ArXiv ID: 2506.07315 “View on arXiv” Authors: Zonghan Wu, Congyuan Zou, Junlin Wang, Chenhan Wang, Hangjing Yang, Yilei Shao Abstract Generative AI, particularly large language models (LLMs), is beginning to transform the financial industry by automating tasks and helping to make sense of complex financial information. One especially promising use case is the automatic creation of fundamental analysis reports, which are essential for making informed investment decisions, evaluating credit risks, guiding corporate mergers, etc. While LLMs attempt to generate these reports from a single prompt, the risks of inaccuracy are significant. Poor analysis can lead to misguided investments, regulatory issues, and loss of trust. Existing financial benchmarks mainly evaluate how well LLMs answer financial questions but do not reflect performance in real-world tasks like generating financial analysis reports. In this paper, we propose FinAR-Bench, a solid benchmark dataset focusing on financial statement analysis, a core competence of fundamental analysis. To make the evaluation more precise and reliable, we break this task into three measurable steps: extracting key information, calculating financial indicators, and applying logical reasoning. This structured approach allows us to objectively assess how well LLMs perform each step of the process. Our findings offer a clear understanding of LLMs current strengths and limitations in fundamental analysis and provide a more practical way to benchmark their performance in real-world financial settings. ...

Corporate Fraud Detection in Rich-yet-Noisy Financial Graph ArXiv ID: 2502.19305 “View on arXiv” Authors: Unknown Abstract Corporate fraud detection aims to automatically recognize companies that conduct wrongful activities such as fraudulent financial statements or illegal insider trading. Previous learning-based methods fail to effectively integrate rich interactions in the company network. To close this gap, we collect 18-year financial records in China to form three graph datasets with fraud labels. We analyze the characteristics of the financial graphs, highlighting two pronounced issues: (1) information overload: the dominance of (noisy) non-company nodes over company nodes hinders the message-passing process in Graph Convolution Networks (GCN); and (2) hidden fraud: there exists a large percentage of possible undetected violations in the collected data. The hidden fraud problem will introduce noisy labels in the training dataset and compromise fraud detection results. To handle such challenges, we propose a novel graph-based method, namely, Knowledge-enhanced GCN with Robust Two-stage Learning (${"\rm KeGCN"}{“R”}$), which leverages Knowledge Graph Embeddings to mitigate the information overload and effectively learns rich representations. The proposed model adopts a two-stage learning method to enhance robustness against hidden frauds. Extensive experimental results not only confirm the importance of interactions but also show the superiority of ${"\rm KeGCN"}{“R”}$ over a number of strong baselines in terms of fraud detection effectiveness and robustness. ...

Financial Statement Analysis with Large Language Models ArXiv ID: 2407.17866 “View on arXiv” Authors: Unknown Abstract We investigate whether large language models (LLMs) can successfully perform financial statement analysis in a way similar to a professional human analyst. We provide standardized and anonymous financial statements to GPT4 and instruct the model to analyze them to determine the direction of firms’ future earnings. Even without narrative or industry-specific information, the LLM outperforms financial analysts in its ability to predict earnings changes directionally. The LLM exhibits a relative advantage over human analysts in situations when the analysts tend to struggle. Furthermore, we find that the prediction accuracy of the LLM is on par with a narrowly trained state-of-the-art ML model. LLM prediction does not stem from its training memory. Instead, we find that the LLM generates useful narrative insights about a company’s future performance. Lastly, our trading strategies based on GPT’s predictions yield a higher Sharpe ratio and alphas than strategies based on other models. Our results suggest that LLMs may take a central role in analysis and decision-making. ...

Accounting statement analysis at industry level. A gentle introduction to the compositional approach ArXiv ID: 2305.16842 “View on arXiv” Authors: Unknown Abstract Compositional data are contemporarily defined as positive vectors, the ratios among whose elements are of interest to the researcher. Financial statement analysis by means of accounting ratios a.k.a. financial ratios fulfils this definition to the letter. Compositional data analysis solves the major problems in statistical analysis of standard financial ratios at industry level, such as skewness, non-normality, non-linearity, outliers, and dependence of the results on the choice of which accounting figure goes to the numerator and to the denominator of the ratio. Despite this, compositional applications to financial statement analysis are still rare. In this article, we present some transformations within compositional data analysis that are particularly useful for financial statement analysis. We show how to compute industry or sub-industry means of standard financial ratios from a compositional perspective by means of geometric means. We show how to visualise firms in an industry with a compositional principal-component-analysis biplot; how to classify them into homogeneous financial performance profiles with compositional cluster analysis; and how to introduce financial ratios as variables in a statistical model, for instance to relate financial performance and firm characteristics with compositional regression models. We show an application to the accounting statements of Spanish wineries using the decomposition of return on equity by means of DuPont analysis, and a step-by-step tutorial to the compositional freeware CoDaPack. ...

Theoretical Review of the Role of Financial Ratios ArXiv ID: ssrn-3472673 “View on arXiv” Authors: Unknown Abstract Purpose – Financial ratios are an instrumental tool in the world of finance and hence comprehensive knowledge of its various aspects is mandated for its user. T Keywords: Financial Ratios, Fundamental Analysis, Credit Risk, Financial Statement Analysis, Solvency, Fixed Income Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The paper is a qualitative literature review that discusses historical concepts and applications of financial ratios without presenting novel mathematical derivations, statistical models, or backtesting results. flowchart TD A["Research Goal:<br>Review Financial Ratios' Theoretical Role"] --> B["Key Methodology:<br>Theoretical Review & Analysis"] B --> C["Data/Inputs:<br>Finance Literature & Financial Statements"] C --> D["Computational Processes:<br>Ratio Calculation & Fundamental Analysis"] D --> E["Key Outcomes:<br>Credit Risk, Solvency & Fixed Income Assessment"]

Analiza Finansowa (Financial Analysis) ArXiv ID: ssrn-3207765 “View on arXiv” Authors: Unknown Abstract Polish Abstract: Podręcznik składa się z sześciu rozdziałów. W pierwszym omówiłem podstawowy system informacyjny przedsiębiorstwa, jakim jest rachunkowoś Keywords: Accounting information systems, Financial reporting, Management accounting, Business information systems, Financial statement analysis, Accounting/Financial Reporting Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The excerpt appears to be a textbook on basic financial analysis concepts like liquidity ratios and operational leverage, with minimal advanced mathematics, and no evidence of backtests or implementation data. flowchart TD A["Research Goal:<br>Analyze Accounting Systems & Reporting"] --> B{"Key Methodology"} B --> C["Qualitative Analysis<br>of Polish Abstract"] B --> D["Review of<br>6 Chapter Structure"] C --> E{"Computational Process:<br>Content Analysis"} D --> E E --> F["Key Findings Outcomes"] subgraph F [" "] G["Accounting IS<br>Core Enterprise System"] H["Financial Reporting<br>External Disclosure"] I["Management Accounting<br>Internal Decision Support"] J["Statement Analysis<br>Performance Evaluation"] end

Fraud Detection and Expected Returns ArXiv ID: ssrn-1998387 “View on arXiv” Authors: Unknown Abstract An accounting-based model has strong out-of-sample power not only to detect fraud, but also to predict cross-sectional returns. Firms with a higher probabilit Keywords: Accounting-Based Models, Fraud Detection, Cross-Sectional Returns, Predictive Analytics, Financial Statement Analysis, Equity Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses an accounting-based predictive model (high empirical data focus) with statistical validation and out-of-sample testing, but the mathematics described are primarily regression-based and do not involve advanced calculus or complex theoretical derivations. flowchart TD A["Research Goal: Does an accounting-based model<br>predict fraud AND future returns?"] --> B["Methodology: Predictive Analytics<br>Logistic Regression & Cross-Validation"] B --> C["Data Inputs:<br>Financial Statements & Stock Returns"] C --> D["Computational Process:<br>Estimate Prob(Fraud) using Accounting Ratios"] D --> E{"Key Findings"} E --> F["Strong Out-of-Sample Fraud Detection"] E --> G["Predict Cross-Sectional Returns"]

Financial Statement Analysis of Leverage and How it Informs About Profitability and Price-to-Book Ratios ArXiv ID: ssrn-292725 “View on arXiv” Authors: Unknown Abstract This paper presents a financial statement analysis that distinguishes leverage that arises in financing activities from leverage that arises in operations. The Keywords: financial statement analysis, leverage, operating leverage, financial leverage, Corporate Debt Complexity vs Empirical Score Math Complexity: 6.5/10 Empirical Rigor: 5.0/10 Quadrant: Holy Grail Why: The paper introduces formal leveraging equations and profitability decomposition (RNOA, net borrowing rate) requiring solid mathematical manipulation, but the core derivation is accounting-based rather than stochastic calculus. The empirical analysis uses cross-sectional regressions on market data to test hypotheses, indicating backtest-ready implementation and data dependency. flowchart TD A["Research Goal:<br>Identify if Operating vs.<br>Financial Leverage predicts<br>Profitability & P/B Ratios"] --> B["Methodology: Decomposition"] B --> C["Data Inputs:<br>Financial Statements<br>Balance Sheet & Income Statement"] C --> D["Computational Process:<br>1. Operating Leverage<br>2. Financial Leverage"] D --> E["Computational Process:<br>Regression Analysis:<br>Impact on ROE & Price-to-Book"] E --> F["Key Finding 1:<br>Operating Leverage positively<br>correlates with profitability"] E --> G["Key Finding 2:<br>Financial Leverage impact<br>on P/B is non-linear"]