Sector indices of S&P 500 Stock Index reflect only part of a company's financial situation

Investors and analysts often compare companies within the same sector. However, a study of all 500 firms in the S&P 500 shows that sector labels only partially capture the financial picture.

A team of researchers in Spain analyzed fiscal year 2022 financial statements using ratios that describe profitability, leverage, liquidity, efficiency, and cash generation. They first tested how strongly those ratios differed between sectors. Subsequently, they asked seven machine-learning models to predict each company's sector based on accounting data. The best model, K-nearest neighbors, achieved a validation accuracy of 49.3%—well above the 14.8% majority-class baseline but still too low to treat sector membership as a complete description of financial structure.

“Hence, we used unsupervised learning to group firms by financial similarity rather than by their existing labels,” shares corresponding author Ricardo Reier Forradellas from the Catholic University of Ávila.“This produced nine economically interpretable clusters.”

Each cluster included companies from more than one sector, and the clusters generally showed lower internal dispersion than conventional sectors across most ratios. Some familiar signatures remained; utilities, real estate, and financial firms were more readily identifiable than several other sectors.

“Our findings do not mean that sector classifications are obsolete," explains Forradellas. “They show that sectors tell only part of the story. When the aim is to compare companies by financial structure, accounting-based peer groups can provide a useful additional perspective.”

The team's analysis also compared cluster assignments across later annual reporting periods and found only moderate persistence. “This indicates that these peer groups should be updated rather than treated as fixed categories,” adds Forradellas. “Our approach complements sector taxonomies for benchmarking, peer comparison, and financial analysis.”

Contact the author:

Ricardo Reier Forradellas; Dekis Research Group, Catholic University of Ávila, Ávila, Spain; ricardo.reier@ucavila.es

Funder:

This research is part of the project financed by the EUROPEAN COMMISSION–Horizon Europe Framework Program under Grant 101159060 AIdesignTEX. The European Commission's support for this project does not constitute an endorsement of the contents, which reflect the views only of the authors

Conflict of interest:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

See the article:

Reier Forradellas, R., Sánchez Cabrera, D., Garay Gallastegui, L. M., & Náñez Alonso, S. L. (2026). Characterization of S&P 500 companies by sector using artificial intelligence: Statistical evidence and machine learning application. The Journal of Finance and Data Science, 12, 100193. https://doi.org/10.1016/j.jfds.2026.100193.