Profitability leadership has been quietly shifting beneath the surface of the market. While headline revenue growth continues to attract attention, a different signal has started appearing across select companies: EBITDA expanding at a materially faster rate than sales. That divergence often points to improving operating leverage, pricing power, cost discipline, or a combination of all three—factors that can reshape earnings trajectories long before they become obvious in consensus narratives.
This week's screen, built using FMP's Income Statement API, identified five companies from very different sectors that share this characteristic. Rather than focusing on absolute growth, the screen looks for businesses where EBITDA compounded meaningfully faster than revenue over a five-year period, highlighting cases where incremental sales translated into disproportionately stronger profitability. In this article, we examine the results and walk through how the same framework can be built using FMP's Income Statement API and scaled across a broader universe using its financial statement datasets.
Key Takeaways
- Five companies from unrelated sectors—media, industrials, cybersecurity, energy, and infrastructure—shared the same underlying characteristic: EBITDA growth significantly exceeded revenue growth over a five-year period.
- The gap between revenue CAGR and EBITDA CAGR can highlight improving operating leverage, helping identify businesses where profitability is scaling faster than sales.
- Revenue growth alone often masks important differences in business quality. Companies with similar top-line trajectories can produce very different earnings outcomes depending on margin expansion and operating efficiency.
- CAGR screens become more informative when combined with additional datasets such as cash flow trends, analyst revisions, insider activity, and ownership data, helping determine whether profitability improvements are supported by broader evidence.
Five Companies Showing a Clear Profitability Inflection
The Walt Disney Company (DIS)
5-Year Revenue CAGR: 7.92%
5-Year EBITDA CAGR: 18.98%
Disney's appearance on this screen is notable because the underlying revenue growth is relatively modest compared to many high-growth companies. A 7.92% revenue CAGR would not typically place a company at the top of a growth-focused ranking. What stands out is the nearly 19% EBITDA CAGR, suggesting that a significant portion of value creation over the past five years came from improving operating economics rather than simply expanding sales.
The signal becomes more interesting when viewed against Disney's ongoing business mix transformation. Over the past several years, management has been balancing declines in traditional television assets with profitability improvements in streaming, while continuing to leverage the scale of its parks, experiences, and licensing ecosystem. In recent earnings reports, Disney highlighted stronger streaming profitability and continued operating income growth across key segments, reinforcing the idea that earnings expansion has become increasingly driven by efficiency and portfolio optimization rather than pure top-line acceleration.
For investors studying the durability of this trend, the most useful datasets extend beyond revenue alone. Historical income statement data helps quantify the margin expansion already visible in the CAGR figures, while segment-level operating income trends provide additional context around where profitability gains are originating and whether they remain broadly distributed across the business.
Mueller Industries, Inc. (MLI)
5-Year Revenue CAGR: 14.02%
5-Year EBITDA CAGR: 37.78%
Mueller Industries generated one of the largest EBITDA growth differentials in this screen. Revenue compounded at a healthy 14.02% annually, but EBITDA expanded at nearly 38%, indicating that profit growth substantially outpaced sales growth over the same period.
That pattern often reflects more than cyclical demand alone. In Mueller's case, the result suggests a business that has been able to convert favorable market conditions into a disproportionately stronger earnings profile. Companies operating in industrial and manufacturing markets frequently experience revenue volatility tied to commodity prices, construction activity, and broader economic cycles. When EBITDA compounds materially faster than revenue across multiple years, it often points to operational improvements, product mix changes, pricing discipline, or capital allocation decisions that amplified the earnings impact of revenue growth.
The key question is not whether growth occurred, but how efficiently it was translated into profitability. Historical income statement data provides the clearest view of that progression, while cash flow and capital expenditure datasets can help determine whether margin expansion was supported by operational efficiency improvements or by temporary external factors that may not persist indefinitely.
Fortinet, Inc. (FTNT)
5-Year Revenue CAGR: 21.78%
5-Year EBITDA CAGR: 33.53%
Fortinet represents a different type of operating leverage story. Unlike mature businesses that improve margins through restructuring, Fortinet has maintained strong revenue expansion while simultaneously increasing profitability at an even faster rate. Revenue compounded at 21.78% annually over five years, while EBITDA grew at 33.53%.
Within cybersecurity, scale can create meaningful economic advantages. As subscription and service revenue grows, fixed operating costs are spread across a larger customer base, allowing profitability to expand faster than sales. Recent company results continue to highlight this dynamic, with Fortinet reporting strong operating margins alongside ongoing revenue growth and increasing cash generation.
What makes this signal particularly useful is that it goes beyond the common narrative of cybersecurity demand remaining strong. The CAGR divergence suggests that revenue growth has been accompanied by increasing operating efficiency. For deeper analysis, investors would likely want to compare income statement trends with deferred revenue, billings, and cash flow datasets to evaluate whether profitability improvements are being supported by recurring customer relationships and expanding platform adoption.
Exxon Mobil Corporation (XOM)
5-Year Revenue CAGR: 13.63%
5-Year EBITDA CAGR: 41.24%
Exxon Mobil posted the widest spread between revenue and EBITDA growth among the companies in this screen. While revenue compounded at 13.63% annually, EBITDA expanded at more than 41%, creating one of the most pronounced profitability acceleration signals in the dataset.
Energy companies present a unique challenge when interpreting CAGR screens because commodity prices can influence both revenue and earnings. However, the magnitude of the EBITDA growth relative to revenue suggests that the story extends beyond oil price fluctuations alone. Over the last several years, Exxon has emphasized operational efficiency, portfolio optimization, production growth in higher-return assets, and disciplined capital allocation. Those initiatives have likely contributed to a greater share of incremental revenue reaching the EBITDA line.
For analysts, this is the type of signal that benefits from combining multiple datasets. Income statement data captures the earnings acceleration itself, but production metrics, capital expenditure trends, and free cash flow figures provide additional context regarding whether profitability improvements are being driven primarily by commodity cycles, operating execution, or a combination of both. The distinction matters because each carries different implications for how durable the observed margin profile may be over time.
Sterling Infrastructure, Inc. (STRL)
5-Year Revenue CAGR: 14.49%
5-Year EBITDA CAGR: 31.01%
Sterling Infrastructure's inclusion highlights a trend that has become increasingly visible across selected engineering and infrastructure-related businesses. Revenue compounded at 14.49% annually, while EBITDA expanded at more than double that pace, reaching a 31.01% CAGR.
The significance of that gap lies in what it implies about project quality and execution. Revenue growth alone can be achieved through larger contract volumes, acquisitions, or favorable market demand. EBITDA growth outpacing revenue by such a wide margin suggests that project selection, pricing discipline, and operational efficiency may have improved alongside business expansion. In other words, growth appears to have become increasingly profitable rather than merely larger.
This is also an example where backlog and project mix can be just as important as reported revenue figures. Historical income statement data identifies the profitability trend, but contract backlog disclosures, cash flow generation, and return metrics often provide additional insight into whether earnings growth is being supported by higher-quality business activity. The current data does not establish causation, but it does identify a pattern worth monitoring as the company continues to scale.
The Signal Beneath the Growth Numbers
What makes this screen interesting is not that all five companies grew. Revenue growth is common. What stands out is that across media, industrials, cybersecurity, energy, and infrastructure, EBITDA expanded substantially faster than sales over the same period. Different industries, different business models, different market environments—yet the same underlying pattern emerged.
Viewed collectively, the screen is less a growth screen than an operating leverage screen. In each case, incremental revenue appears to have translated into earnings at an increasingly efficient rate. Sometimes that can reflect cost discipline. In other cases, it may be driven by pricing power, favorable business mix shifts, scale advantages, asset optimization, or improvements in capital allocation. The cause varies from company to company, but the outcome is similar: profitability compounds faster than revenue.
That distinction matters because revenue alone often provides an incomplete picture of business quality. Two companies can produce identical top-line growth rates while generating very different economic outcomes underneath the surface. The spread between revenue CAGR and EBITDA CAGR can help identify where operating performance is strengthening independently of headline sales growth. It is not a conclusion in itself, but it can serve as a useful starting point for deeper investigation.
The more useful question is what additional evidence supports the signal. A profitability inflection becomes more meaningful when it can be cross-checked against cash generation, margin trends, analyst revisions, insider activity, or institutional positioning. This is where a broader financial dataset becomes valuable. Platforms such as FMP allow the same company to be examined through multiple lenses, helping separate simple earnings growth from a potentially broader improvement in business economics.
What emerges from this process is not a prediction but a framework. The screen identifies companies where earnings power has expanded more rapidly than revenue over time. From there, the objective becomes understanding the source of that divergence, validating it across multiple datasets, and determining whether the pattern reflects a temporary condition or a more durable shift in business economics. The CAGR calculation surfaces the signal. The broader dataset helps explain it.
Building a Consistent CAGR Screening Framework
Building a useful CAGR screen is less about the formula itself and more about maintaining discipline in the underlying dataset. The calculation is straightforward; what determines whether the result is meaningful is data consistency. Every company needs to be evaluated using the same reporting periods, identical financial line items, and the same time horizon. Once those inputs are standardized, growth rates become comparable across industries, capital structures, and business models. The workflow below shows how to structure that process using FMP's Income Statement data — starting with a single company and then scaling the exact same logic across a broader universe.
Step 1: Pull Income Statement Data
Begin with a single symbol to establish the baseline. Query the standard Income Statement API to retrieve the full set of historical reporting periods needed for the calculation.
As long as your API key is active, one request gives you the raw time series you'll be working with. For example:
Endpoint:
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https://financialmodelingprep.com/stable/income-statement?symbol=AAPL&apikey=YOUR_API_KEY |
Step 2: Gather Historical Figures
From the JSON output, select the specific metric you want to analyze — revenue, EBITDA, EPS, or another line item. Arrange the values in proper chronological order before doing any math. This step is easy to overlook, but it's critical: CAGR only makes sense when the starting and ending points are clearly defined and consistently ordered.
Step 3: Calculate CAGR
Once the first and last data points are set, calculate CAGR using the standard formula:
CAGR = (Ending Value / Beginning Value)^(1 / Years) - 1
This reduces several years of performance into a single annualized figure, making it easier to compare growth profiles across companies without getting lost in interim volatility.
Step 4: Scale Screening with Bulk API
After validating the method on one symbol, broaden the workflow using the Income Statement Bulk API:
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https://financialmodelingprep.com/stable/income-statement-bulk?year=2025&period=FY&apikey=YOUR_API_KEY |
Running the same calculation at scale lets you build filters — for instance, highlighting companies that clear a five-year revenue CAGR threshold — while ensuring every ticker is processed under the same ruleset. Once the bulk pull is in place, updating or rerunning the screen is effectively a single action.
Scaling the Framework Without Changing the Methodology
The strength of this type of screen comes from consistency, not complexity. Once the formula, reporting periods, and financial line items are defined, the objective is to keep the methodology fixed while gradually widening the universe being tested. Expanding coverage should not require changing the framework itself — only the number of companies moving through it.
That's why the workflow is easiest to validate in a smaller environment first. Within the Basic plan, the Income Statement endpoints provide enough historical coverage to align reporting periods properly, normalize the selected metrics, and verify that the CAGR calculations are producing comparable outputs across companies. At this stage, the emphasis is less about scale and more about making sure inconsistencies in filings or missing data are not distorting the screen.
From there, expanding into the Starter plan simply broadens the sample size. The screening logic remains identical, but the larger universe makes sector-level comparisons more useful. Patterns that initially appear company-specific can then be evaluated against peers, industries, or market-cap cohorts to determine whether the operating leverage signal is isolated or part of a broader trend developing within a segment of the market.
The Premium plan extends that same structure further by increasing historical depth and geographic coverage. The underlying process still does not change. What changes is the scope of observation — allowing the same framework to be applied across wider datasets without introducing new assumptions or altering the screening criteria midstream. That continuity is what makes the process repeatable over time rather than dependent on one-off observations or isolated market conditions.
From Static Screens to a Continuous Operating Signal
The value of a screen like this is not in identifying a one-time result, but in creating a repeatable process for tracking how operating performance evolves over time. By regularly monitoring profitability trends through the Income Statement API and Income Statement Bulk API, the focus shifts from isolated observations to a more consistent view of where business economics are strengthening, stabilizing, or beginning to change beneath the surface.
If you found this useful, you might also like: Weekly Signals Desk | Concentrated Analyst Revisions via the FMP API (June 1-5)
Disclosure: Signals Desk content is provided for informational and analytical purposes only and does not constitute investment advice or trade recommendations. The analysis reflects interpretation of market data and publicly disclosed or third-party information, including data accessed via Financial Modeling Prep APIs, at the time of publication. Signals discussed are probabilistic, can be wrong, and may change as market conditions and consensus data evolve. This content should be considered alongside broader research, individual objectives, and risk assessment.
