Financial Ratios

Financial resilience of agricultural and food production companies in Spain: A compositional cluster analysis of the impact of the Ukraine-Russia war (2021-2023) ArXiv ID: 2504.05912 “View on arXiv” Authors: Unknown Abstract This study analyzes the financial resilience of agricultural and food production companies in Spain amid the Ukraine-Russia war using cluster analysis based on financial ratios. This research utilizes centered log-ratios to transform financial ratios for compositional data analysis. The dataset comprises financial information from 1197 firms in Spain’s agricultural and food sectors over the period 2021-2023. The analysis reveals distinct clusters of firms with varying financial performance, characterized by metrics of solvency and profitability. The results highlight an increase in resilient firms by 2023, underscoring sectoral adaptation to the conflict’s economic challenges. These findings together provide insights for stakeholders and policymakers to improve sectorial stability and strategic planning. ...

Decoding Financial Health in Kenyas’ Medical Insurance Sector: A Data-Driven Cluster Analysis ArXiv ID: 2502.17072 “View on arXiv” Authors: Unknown Abstract This study examines insurance companies’ financial performance and reporting trends within the medical sector using advanced clustering techniques to identify distinct patterns. Four clusters were identified by analyzing financial ratios and time series data, each representing unique financial performance and reporting consistency combinations. Dynamic Time Warping (DTW) and KMeans clustering were employed to capture temporal variations and uncover key insights into company behaviors. The findings reveal that resilient performers consistently report and have financial stability, making them reliable options for policyholders. In contrast, clusters of underperforming companies and those with reporting gaps highlight operational challenges and issues related to data consistency. These insights emphasize the importance of transparency and timely reporting to ensure the sector’s resilience. This study contributes to the literature by integrating time series analysis into financial clustering, offering practical recommendations for improving data governance and financial stability in the insurance sector. Future research could further investigate non-financial indicators and explore alternative clustering methods to provide a deeper understanding of performance dynamics. ...

Considerations on the use of financial ratios in the study of family businesses ArXiv ID: 2501.16793 “View on arXiv” Authors: Unknown Abstract Most empirical works that study the financing decisions of family businesses use financial ratios. These data present asymmetry, non-normality, non-linearity and even dependence on the results of the choice of which accounting figure goes to the numerator and denominator of the ratio. This article uses compositional data analysis (CoDa) as well as classical analysis strategies to compare the structure of balance sheet liabilities between family and non-family businesses, showing the sensitivity of the results to the methodology used. The results prove the need to use appropriate methodologies to advance the academic discipline. ...

Leveraging Fundamental Analysis for Stock Trend Prediction for Profit ArXiv ID: 2410.03913 “View on arXiv” Authors: Unknown Abstract This paper investigates the application of machine learning models, Long Short-Term Memory (LSTM), one-dimensional Convolutional Neural Networks (1D CNN), and Logistic Regression (LR), for predicting stock trends based on fundamental analysis. Unlike most existing studies that predominantly utilize technical or sentiment analysis, we emphasize the use of a company’s financial statements and intrinsic value for trend forecasting. Using a dataset of 269 data points from publicly traded companies across various sectors from 2019 to 2023, we employ key financial ratios and the Discounted Cash Flow (DCF) model to formulate two prediction tasks: Annual Stock Price Difference (ASPD) and Difference between Current Stock Price and Intrinsic Value (DCSPIV). These tasks assess the likelihood of annual profit and current profitability, respectively. Our results demonstrate that LR models outperform CNN and LSTM models, achieving an average test accuracy of 74.66% for ASPD and 72.85% for DCSPIV. This study contributes to the limited literature on integrating fundamental analysis into machine learning for stock prediction, offering valuable insights for both academic research and practical investment strategies. By leveraging fundamental data, our approach highlights the potential for long-term stock trend prediction, supporting portfolio managers in their decision-making processes. ...

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"]

Distressed Firm and Bankruptcy Prediction in an International Context: A Review and Empirical Analysis of Altman’s Z-Score Model ArXiv ID: ssrn-2536340 “View on arXiv” Authors: Unknown Abstract The purpose of this paper is firstly to review the literature on the efficacy and importance of the Altman Z-Score bankruptcy prediction model globally and its Keywords: Altman Z-Score, Bankruptcy Prediction, Credit Risk Modeling, Financial Ratios, Distress Analysis, Equity/Fixed Income Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper applies a well-established linear model (Z-Score) with basic statistical metrics, showing low math complexity, but uses a large international dataset, cross-country validation, and AUC analysis, indicating high empirical rigor. flowchart TD A["Research Goal<br>Evaluate global efficacy of Altman Z-Score<br>in distressed firm & bankruptcy prediction"] --> B["Methodology & Data<br>Literature review & empirical analysis<br>of international financial data"] B --> C["Input Variables<br>Financial Ratios:<br>Working Capital/Total Assets<br>Retained Earnings/Total Assets<br>EBIT/Total Assets<br>Market Value/Book Value<br>Sales/Total Assets"] C --> D["Computational Process<br>Calculate Altman Z-Score:<br>Z = 1.2A + 1.4B + 3.3C + 0.6D + 1.0E<br>Apply Thresholds: Z < 1.8 (Distress)"] D --> E["Key Findings<br>Model demonstrates moderate predictive power<br>Contextual limitations in global markets<br>Recommendations for sector/region adjustments"]