For decades, the sector label has been the first thing an analyst reaches for when sizing up a company: technology versus energy, healthcare versus financials, utilities versus consumer discretionary. A new study of every one of the 500 firms in the S&P 500 suggests that habit captures far less than investors assume. When researchers in Spain trained artificial intelligence models to recognize a company’s sector using nothing but its accounting numbers, the best-performing model — an algorithm known as K-nearest neighbors — succeeded only 49.3 percent of the time: well above blind guessing, but wrong more often than right. And when the team let an unsupervised algorithm group the firms purely by financial similarity, ignoring official labels altogether, it uncovered nine distinct financial families, every one of which cut across sector boundaries. The findings, published in The Journal of Finance and Data Science, stop short of declaring sector classifications obsolete. But they make a striking case that the labels organizing the world’s most closely watched stock index describe only part of each company’s financial anatomy — and that data-driven peer groups can reveal the rest.

Comparing companies within a sector is one of the oldest rituals in finance. Analysts benchmark profit margins against industry rivals, screen stocks by sector to diversify portfolios, and build risk models on the premise that firms facing similar markets should resemble one another on the balance sheet. Entire index families and research disciplines are organized around that assumption. Yet it is, at bottom, an empirical claim — one that had rarely been tested at the scale of an entire flagship index. A research team at the Catholic University of Ávila in Spain, working within its Dekis Research Group and led by corresponding author Ricardo Reier Forradellas, decided to put the claim to a formal test. Their question was deceptively simple: if you strip away a company’s name, its industry narrative and its stock chart, and hand a machine only its financial ratios, how often does the machine land on the same sector label that humans assigned? The answer, according to the new paper, is not nearly often enough for sectors to be treated as complete financial descriptions.

The study began with a comprehensive snapshot of the U.S. large-cap market: the fiscal year 2022 financial statements of all 500 constituents of the S&P 500. From each statement, the researchers distilled a battery of accounting ratios spanning the dimensions that fundamental analysts track most closely — profitability, which measures how efficiently a firm converts sales and assets into earnings; leverage, which captures its reliance on borrowed money; liquidity, which gauges its ability to meet short-term obligations; efficiency, which reflects how productively it deploys its resources; and cash generation, which reveals whether reported earnings are backed by real cash flow. Ratios like these compress thousands of line items into comparable numbers, making them the raw material of fundamental analysis. The team’s first step was classical rather than computational: a statistical examination of how strongly those ratios actually differed from sector to sector. The verdict was nuanced rather than clean. The ratios did vary across sectors — the labels are not arbitrary — but those differences accounted for only part of the financial variation among the 500 firms.

Then came the artificial intelligence. The researchers trained seven different supervised machine-learning models on a single task: given a company’s accounting ratios, predict its sector. Supervised learning of this kind works by letting an algorithm study examples whose answers are known — here, firms carrying official sector labels — and internalize the patterns connecting inputs to outputs. Among the seven contenders, the strongest performer was K-nearest neighbors, a deceptively simple method that makes predictions by analogy. Rather than deriving an explicit formula, the algorithm stores the training companies as points in a multidimensional space of financial ratios and classifies each new company by finding its closest neighbors and adopting whatever label dominates among them. In effect, the model asks: which established sector residents does this firm most resemble on paper? Performance was measured on validation data withheld from training — a safeguard that prevents the algorithm from simply memorizing answers it has already seen — and K-nearest neighbors reached a validation accuracy of 49.3 percent, the highest figure any of the seven approaches achieved.

To judge whether 49.3 percent is impressive or damning, one must consult the study’s baseline. Because the S&P 500’s sectors are unevenly populated, a lazy classifier that always guessed the most common sector — the majority class — would have been correct 14.8 percent of the time. Viewed against that yardstick, the machine-learning result is more than three times better, confirming that accounting ratios do carry a genuine sector signal: utilities genuinely do look different from banks on a balance sheet. But the same number carries a more provocative message. Even the best model misidentified a company’s sector more often than it identified it correctly. In practical terms, most of the index’s members behave as financial hybrids, their ratio profiles confusable with those of firms from entirely different industries. If sector membership were a full description of financial structure, a well-trained classifier should approach near-perfect accuracy. Instead, the evidence indicates that a company’s industry tells you something real about its finances — but far from everything.

Faced with that ceiling, the team changed tactics. Instead of asking the data to reproduce the human-made labels, they asked it to ignore the labels entirely. “Hence, we used unsupervised learning to group firms by financial similarity rather than by their existing labels,” explains corresponding author Ricardo Reier Forradellas of the Catholic University of Ávila. “This produced nine economically interpretable clusters.” Unsupervised learning is the branch of machine learning that finds structure without a teacher: the algorithm receives no answers, only measurements, and must discover on its own which companies naturally bunch together in ratio space. The result was not a mirror of the official taxonomy but an alternative map of the U.S. corporate economy, drawn exclusively in the currency of profitability, leverage, liquidity, efficiency and cash flow. Crucially, the clusters were not statistical noise. Each of the nine could be described in plain financial language, giving the researchers confidence that the algorithm had surfaced economically meaningful structure rather than accidental groupings.

The clearest evidence for that meaningfulness lay in how tightly knit the new groups were. Every one of the nine clusters contained companies drawn from more than one official sector, confirming that financial similarity respects no industry border. Yet the clusters were generally more internally coherent than the sectors themselves: across most of the accounting ratios, firms inside a data-driven group showed lower internal dispersion — a smaller statistical spread around the group’s typical value — than firms sharing a sector label. In other words, a company’s closest financial peers were more likely to be found inside its algorithmic cluster than inside its sector. Some familiar signatures did survive the analysis. Utilities, real estate companies and financial firms proved more readily identifiable than several other sectors, a reflection of business models that imprint themselves unmistakably on the accounts: capital-intensive networks, property-heavy balance sheets and debt-fueled intermediation leave deep accounting fingerprints. Other kinds of firms, by contrast, proved harder to pin down, slipping quietly across sector lines on the machine’s map.

Forradellas is careful to frame the result as an addition to financial practice rather than a demolition of it. “Our findings do not mean that sector classifications are obsolete,” he says. “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 distinction matters for anyone who relies on comparisons professionally. For questions about regulation, industry competition or supply chains, sector membership remains the natural organizing principle. But for questions about valuation, credit risk or benchmarking financial performance — questions that turn on how a company actually funds itself, generates cash and manages its short-term obligations — the study suggests that peers defined by accounting similarity may be the more honest reference group. The two lenses answer different questions, and neither one alone captures the whole financial creature.

The research also carries a warning for anyone tempted to enshrine the new clusters as a permanent replacement taxonomy. When the team compared cluster assignments across later annual reporting periods, they found only moderate persistence: companies did not stay put in their financial families from one reporting period to the next. “This indicates that these peer groups should be updated rather than treated as fixed categories,” Forradellas adds. “Our approach complements sector taxonomies for benchmarking, peer comparison, and financial analysis.” That drift is not a flaw in the method so much as a feature of corporate life. Firms alter their capital structures, pivot their strategies, acquire rivals and ride macroeconomic cycles, and their ratio profiles shift accordingly. A company can migrate from a cash-rich cluster to a heavily leveraged one without ever changing its ticker symbol or its industry. Any financial map built from accounting data, the authors imply, must be redrawn periodically — a living taxonomy rather than a carved-in-stone one.

The findings arrive as machine learning steadily permeates quantitative finance, and they offer a template for how data-driven classification might sit alongside traditional taxonomies rather than clash with them. For index providers, the results hint at complementary ways to construct peer sets for benchmarking; for analysts and portfolio managers, they suggest that screening by algorithmic financial similarity could surface valuation signals and risks that sector screens miss. The study is also a sober reminder of the limits of AI: even the best of seven supervised models fell well short of the accuracy that would make sectors predictable from accounts alone, and unsupervised groupings still demand expert interpretation before they become economically meaningful. Published open access by KeAi, a publishing venture of Elsevier and China Science Publishing & Media Ltd, the paper — “Characterization of S&P 500 companies by sector using artificial intelligence: Statistical evidence and machine learning application” — reexamines a tool so familiar that few thought to question it. The sector, the study concludes in effect, is where a company works. The balance sheet is who it is. Investors reading only the first are seeing half the picture.