The use of financial and sustainability ratios to map a sector. An approach using compositional data

ArXiv ID: 2509.06468 “View on arXiv”

Authors: Elena Rondós-Casas, Germà Coenders, Miquel Carreras-Simó, Núria Arimany-Serrat

Abstract

Purpose: The article aims to visualise in a single graph fish and meat processing company groups in Spain with respect to long-term solvency, energy, waste and water intensity and gender employment gap. Design/methodology/approach: The selected financial, environmental and social indicators are ratios, which require specific statistical analysis methods to prevent severe skewness and outliers. We use the compositional data methodology and the principal-component analysis biplot. Findings: Fish-processing companies have more homogeneous financial, environmental and social performance than their meat-processing counterparts. Specific company groups in both sectors can be identified as poor performers in some of the indicators. Firms with higher solvency tend to be less efficient in energy and water use. Two clusters of company groups with similar performances are identified. Research limitations/implications: As of now, few firms publish reports according to the EU Corporate Sustainability Reporting Directive. In future research larger samples will be available. Social Implications: Firm groups can visually see their areas of improvement in their financial, environmental and social performance compared to their competitors in the sector. Originality/value: This is the first time in which visualization tools have combined financial, environmental and social indicators. All individual firms can be visually ordered along all indicators simultaneously.

Keywords: Compositional Data Analysis, ESG Integration, PCA Biplot, Solvency Ratios, Corporate Sustainability Reporting Directive

Complexity vs Empirical Score

  • Math Complexity: 5.0/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail
  • Why: The paper uses advanced compositional data methodology (CoDa) and PCA biplots, which requires specialized statistical knowledge (math complexity ~5). However, it is based on real-world sustainability and financial ratio data from a specific sector, adhering to EU reporting standards and demonstrating practical visualization for firms, indicating substantial empirical grounding (rigor ~6.5).
  flowchart TD
A["Research Goal<br>Map sector using financial<br>and sustainability ratios"] --> B["Data & Inputs<br>Financial, energy, waste,<br>water, gender ratios"]
B --> C["Methodology<br>Compositional Data Analysis<br>+ PCA Biplot"]
C --> D["Computational Process<br>Transform ratios →<br>Statistical analysis →<br>Biplot visualization"]
D --> E["Key Findings & Outcomes<br>1. Fish: homogeneous performance<br>2. Meat: varied performance<br>3. Trade-off: High solvency vs.<br>Energy/Water inefficiency<br>4. Two distinct clusters identified"]