Over the past several quarters, a quieter rotation has been taking shape beneath the surface of the market — not necessarily toward the fastest revenue growers, but toward companies where incremental growth is converting into disproportionately stronger operating performance. In this week's screen using the FMP's Income Statement API, five companies from very different sectors surfaced with the same underlying characteristic: EBITDA compounding materially faster than revenue over a multi-year period.
That divergence matters. When profitability growth begins to consistently outpace top-line expansion, the signal often points to improving operating leverage, pricing strength, mix optimization, or scale efficiencies starting to show up more clearly in the financials. In this article, we'll break down the names that screened highest under that framework and walk through how the same methodology can be structured and scaled using FMP's Income Statement API and Bulk Income Statement API.
Key Takeaways
- EBITDA growth materially outpaced revenue growth across all five companies, suggesting operating leverage and margin efficiency improved faster than headline sales trends alone would indicate.
- The screen surfaced similar profitability patterns across unrelated sectors — including restaurants, industrials, software, building products, and mining — highlighting how operational quality can emerge independently of broader sector narratives.
- Comparing CAGR spreads alongside supporting datasets such as free cash flow, analyst revisions, deferred revenue, and insider activity provides a more complete framework than relying on revenue growth screens in isolation.
Multi-Year Operating Leverage Emerging Across Five Companies
Chipotle Mexican Grill, Inc. (CMG)
5-Year Revenue CAGR: 15.17%
5-Year EBITDA CAGR: 35.18%
Chipotle's spread between revenue growth and EBITDA expansion stands out because the business already operates at considerable scale. A 15.17% revenue CAGR over five years is strong on its own, but EBITDA compounding at 35.18% suggests the underlying operating model has been improving materially faster than sales growth alone would imply. That usually reflects a combination of throughput efficiency, pricing architecture, digital mix, and tighter restaurant-level economics rather than simple unit expansion.
What makes the signal more interesting is that the margin profile has continued evolving even as the broader restaurant space has faced periodic traffic softness and input inflation. Recent quarterly results showed traffic recovery tied to value-oriented menu initiatives and operational adjustments, even while labor and commodity costs remained elevated. That divergence matters because it suggests management has been able to preserve a degree of operating discipline during a more uneven consumer environment instead of relying exclusively on pricing to sustain margins.
From a data perspective, the Income Statement endpoint helps quantify how restaurant-level economics have changed over time, but pairing it with segment-level margin trends and same-store sales data adds important context. Monitoring operating margin stability relative to transaction growth can help determine whether EBITDA expansion is still being driven by scalable efficiencies or increasingly dependent on pricing leverage alone.
Parker-Hannifin Corporation (PH)
5-Year Revenue CAGR: 8.13%
5-Year EBITDA CAGR: 16.19%
Parker-Hannifin's numbers reflect a very different type of operating leverage story. Revenue growth has been comparatively moderate at 8.13% annually, yet EBITDA has compounded at nearly double that pace. In industrial businesses, that kind of spread often points to mix improvement, acquisition integration discipline, and stronger exposure to higher-margin aftermarket or aerospace segments rather than cyclical top-line acceleration.
That pattern appears increasingly visible in Parker's recent positioning. Aerospace demand and aftermarket activity have remained resilient, supporting both earnings strength and upgraded guidance in recent quarters. The company also announced a $2.55 billion acquisition of Circor Aerospace, further increasing exposure to higher-margin aerospace systems. The significance of that move is less about headline expansion and more about reinforcing the portions of the portfolio already contributing disproportionate profitability.
This is the type of setup where cash flow and segment reporting become especially important alongside the core Income Statement dataset. Revenue growth alone may understate what is actually happening operationally if the company is systematically shifting toward businesses with structurally higher returns and recurring aftermarket demand. Watching how EBITDA margins behave relative to order backlog and aerospace exposure may provide a cleaner read on whether the operating leverage trend is broadening or becoming concentrated in a smaller subset of the portfolio.
Carlisle Companies Incorporated (CSL)
5-Year Revenue CAGR: 11.00%
5-Year EBITDA CAGR: 16.05%
Carlisle's spread between revenue and EBITDA growth is notable because it reflects a quieter form of operating improvement than many higher-profile industrial names. The company's 11.00% revenue CAGR is solid, but EBITDA compounding at 16.05% indicates profitability has been scaling faster than sales through a period that included shifting construction demand, inflationary pressure, and uneven industrial activity.
In Carlisle's case, the signal appears tied less to cyclical acceleration and more to portfolio refinement and pricing discipline. The company has spent several years simplifying operations around higher-margin building products and roofing-related businesses while reducing exposure to less strategic segments. When EBITDA compounds materially faster than revenue in that type of transition, it often suggests the business mix itself is improving rather than simply benefiting from temporary volume conditions.
The Income Statement data captures the margin expansion trend, but balance sheet and cash flow datasets help complete the picture because much of Carlisle's transformation has involved capital allocation decisions rather than headline revenue growth alone. Tracking free cash flow conversion alongside margin stability may help determine whether the operating leverage pattern reflects durable structural changes or simply favorable conditions within specific end markets.
Cadence Design Systems, Inc. (CDNS)
5-Year Revenue CAGR: 15.46%
5-Year EBITDA CAGR: 21.24%
Cadence continues to show one of the cleaner software-style operating leverage profiles in the broader semiconductor ecosystem. Revenue compounded at 15.46% annually over five years, while EBITDA expanded at 21.24%, implying that incremental demand has been translating into profitability with relatively limited cost drag. In highly specialized software infrastructure businesses, that type of spread often reflects pricing power, sticky enterprise adoption, and expanding workflow dependence rather than cyclical demand alone.
The backdrop around AI infrastructure spending adds another layer to the story. Cadence recently raised its full-year revenue outlook as demand for AI chip-design tools and complex system-on-chip development continued accelerating across major hyperscalers and semiconductor firms. What matters operationally is not just that revenue is growing, but that the company remains embedded in increasingly critical stages of semiconductor design complexity. That tends to support recurring usage and higher-value software workflows over time.
For this type of business, the Income Statement endpoint explains only part of the operating leverage trend. Analyst estimate revisions, deferred revenue trends, and R&D intensity are also useful datasets because they help distinguish between temporary AI enthusiasm and sustained infrastructure demand. Monitoring how margins evolve alongside acquisition-related integration costs and enterprise spending patterns may offer a more grounded interpretation of whether the current profitability expansion remains broad-based.
Southern Copper Corporation (SCCO)
5-Year Revenue CAGR: 11.70%
5-Year EBITDA CAGR: 19.66%
Southern Copper's EBITDA CAGR materially outpacing revenue growth is especially significant given the inherently cyclical nature of commodity markets. Revenue grew at an annualized rate of 11.70% over five years, while EBITDA compounded at 19.66%, suggesting the company captured more operating efficiency and margin expansion from commodity cycles than revenue figures alone immediately reveal.
In mining businesses, this type of spread often reflects a combination of cost discipline, production mix, and favorable realized pricing environments rather than simply higher output volumes. Copper producers have also increasingly become tied to broader infrastructure and electrification narratives, which has shifted market attention toward long-duration supply dynamics instead of purely near-term pricing fluctuations. That context matters because profitability sensitivity in mining companies can widen considerably once production costs stabilize while realized commodity prices remain supportive.
The Income Statement dataset highlights the operating leverage trend clearly, but commodity-linked businesses benefit from being viewed alongside production metrics, realized pricing data, and capital expenditure trends. Watching how EBITDA margins behave relative to copper price volatility and project investment cycles may help determine whether the recent spread reflects temporary commodity conditions or a more sustained improvement in operational efficiency.
Interpreting the Spread: What the Data Is Actually Signaling
What stands out across these five companies is not simply that EBITDA growth exceeded revenue growth — it's that the pattern appeared across entirely different operating environments. Restaurants, industrials, enterprise software, building products, and copper mining rarely move in sync fundamentally. Yet each surfaced through the same framework: profitability compounding materially faster than top-line expansion over a sustained period.
That matters because screens built purely around revenue acceleration often miss where operational quality is quietly improving underneath the surface. In many cases, the more important signal is not how fast a company is growing, but how efficiently incremental revenue is being converted into earnings over time. When that spread widens consistently across multiple reporting periods, it can point toward deeper structural changes inside the business — pricing discipline strengthening, fixed costs scaling more efficiently, higher-margin segments contributing a larger share of earnings, or capital allocation becoming increasingly selective.
The distinction is especially relevant in the current market backdrop, where index leadership has remained concentrated in a relatively narrow group of high-visibility themes while many operational improvements have developed more quietly across less crowded areas of the market. Looking at EBITDA CAGR relative to revenue CAGR offers a way to identify businesses where operating leverage may already be strengthening before that transition becomes fully reflected in broader market narratives.
This is also where the screen becomes more useful as part of a broader analytical workflow rather than a standalone ranking exercise. Revenue and EBITDA trends from the Income Statement API establish the initial signal, but the interpretation sharpens once those figures are cross-referenced against additional datasets available through Financial Modeling Prep. Comparing margin expansion against Free Cash Flow trends, analyst estimate revisions, insider transaction data, or valuation dispersion can help determine whether improving operating leverage is being reinforced by broader financial behavior — or whether the signal remains isolated to the income statement itself.
In cyclical businesses like Southern Copper or Parker-Hannifin, combining Income Statement data with segment exposure and macro-sensitive operating metrics can help separate temporary commodity or industrial tailwinds from more durable efficiency improvements. For software-oriented companies like Cadence, deferred revenue trends, recurring revenue visibility, and R&D intensity often provide a cleaner read on whether margin expansion is being supported by durable enterprise demand rather than short-cycle spending bursts. Meanwhile, consumer-facing businesses like Chipotle benefit from layering transaction growth, comparable sales trends, and restaurant-level margin data alongside profitability metrics to understand whether operating leverage is being driven primarily by traffic, pricing, or mix.
The broader takeaway is that CAGR spreads are most useful when treated as a starting signal rather than a conclusion. On their own, they identify where operational leverage appears to be emerging. Combined with supporting datasets — cash flow, analyst revisions, insider activity, segment reporting, valuation multiples, and balance sheet trends — they become a more effective framework for identifying where underlying business quality may be evolving faster than surface-level growth metrics initially suggest.
Structuring a Repeatable CAGR Screening Workflow with FMP
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
What begins as a simple CAGR screen often evolves into something more useful: a repeatable way to monitor where operating efficiency is improving beneath the surface of headline growth. Using the FMP Income Statement API and Income Statement Bulk API as the foundation makes it possible to revisit the same framework over time, track how those spreads change across reporting cycles, and separate temporary momentum from operational trends that continue compounding underneath the market narrative.
If you found this useful, you might also like: Weekly Signals Desk | Concentrated Analyst Revisions via the FMP API (May 11-15)
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.
