This week's screen surfaced five companies where EBITDA growth is outpacing revenue growth over a five-year period, pointing to a stronger profitability trend beneath the headline sales numbers. Wingstop, Lennox International, Disney, Parker-Hannifin, and Vertiv each show a widening spread between top-line expansion and operating earnings growth, a signal that margins, cost discipline, or business mix may be improving.
The analysis uses FMP's Income Statement API to compare multi-year revenue and EBITDA growth across the group. This article breaks down the five names, the signal behind the screen, and how to use the same API framework to build a repeatable CAGR-based profitability scan.
Key Takeaways
- Wingstop, Lennox, Disney, Parker-Hannifin, and Vertiv all show five-year EBITDA growth exceeding revenue growth, signaling stronger operating leverage across different business models.
- The spread between revenue CAGR and EBITDA CAGR matters most when it is supported by margin expansion, cash generation, and repeatable operating improvements rather than temporary cost effects.
- Combining FMP income statement, cash flow, analyst target, market price, and insider-trading data provides a more complete test of whether the signal is durable and already reflected in expectations.
Five Companies Showing a Clear Profitability Inflection
Wingstop Inc. (WING)
5-Year Revenue CAGR: 23.64%
5-Year EBITDA CAGR: 28.49%
Wingstop's EBITDA has compounded nearly five percentage points faster than revenue over the five-year period. That spread matters because the company operates an asset-light franchise model, where additional system sales, restaurant openings, royalty income, and vendor-related revenue can produce earnings growth without requiring corporate costs to rise at the same rate. The figures suggest that Wingstop has generated operating leverage alongside its rapid expansion, rather than relying on unit growth alone.
Recent results also show why the underlying components of that leverage need to be separated. In the first quarter of 2026, total revenue rose to $183.7 million from $171.1 million, supported in part by net new franchise development and higher vendor rebates. At the same time, domestic same-store sales declined 8.7%, primarily because of lower transaction volumes and continued pressure on consumer spending. The contrast between network expansion and weaker comparable-store demand makes restaurant count, system-wide sales, royalty revenue, and transaction trends especially important when assessing whether EBITDA growth remains broad-based.
The income statement dataset captures the widening gap between revenue and EBITDA, but a fuller reading would pair it with store-level operating data and segment disclosures. New-unit openings can support royalty growth even during a softer traffic period, while prolonged transaction weakness would place greater weight on pricing, franchise development, and cost control. For this name, the most useful follow-up screen would combine historical income statements with quarterly same-store sales, restaurant openings, and cash-flow data.
Lennox International Inc. (LII)
5-Year Revenue CAGR: 7.74%
5-Year EBITDA CAGR: 16.53%
Lennox presents one of the clearest examples in the screen of earnings growth separating from the top line. Its five-year EBITDA CAGR is more than twice its revenue CAGR, indicating that profitability has expanded through a combination of pricing, productivity, portfolio discipline, and operating efficiency. For an HVAC manufacturer exposed to residential replacement cycles, weather patterns, channel inventory, and construction activity, that distinction is significant. The signal is not simply that Lennox sold more equipment, but that it generated substantially more EBITDA from each incremental layer of revenue over the measurement period.
The latest quarter illustrates why a multi-year result should still be tested against current margin behavior. Lennox reported first-quarter 2026 revenue of $1.1 billion, up 6%, while segment profit declined 3% and segment margin contracted 130 basis points to 14.4%. Management has also outlined longer-term targets that include segment profit margins of 22% to 23% by 2030, providing a useful benchmark against which future operating performance can be assessed. The near-term contraction does not invalidate the five-year EBITDA pattern, but it shows that the path has not been linear and remains sensitive to volume, mix, costs, and seasonal demand.
For Lennox, the key analytical task is to distinguish structural margin improvement from cyclical benefit. Historical income statement data establishes the long-run spread, while segment margins, unit volumes, pricing commentary, inventory levels, and free-cash-flow conversion help explain how durable that spread has been. Analyst estimate revisions can also show whether expectations are adjusting to current margin pressure or remaining anchored to the company's longer-term profitability framework.
The Walt Disney Company (DIS)
5-Year Revenue CAGR: 8.55%
5-Year EBITDA CAGR: 20.25%
Disney's EBITDA CAGR exceeds its revenue CAGR by almost 12 percentage points, but the interpretation is more complex than it is for a single-segment industrial or restaurant operator. Disney's earnings base reflects several businesses with different capital requirements and economic drivers, including streaming, film and television, sports, and theme parks. The five-year spread therefore points to a broader reshaping of the earnings mix, including cost actions, changes in streaming economics, and the recovery and expansion of the Experiences segment.
The company's fiscal 2025 results help show where some of that operating strength has been concentrated. Full-year Entertainment segment operating income increased 19% to $4.7 billion, while Experiences produced record operating income of $10.0 billion. However, Disney's fiscal first quarter of 2026 was less uniform: revenue increased 5% to $26.0 billion, while total segment operating income declined 9% to $4.6 billion. That divergence reinforces the importance of examining segment composition rather than treating consolidated EBITDA growth as a single operating trend.
The most informative dataset here is a combination of consolidated income statements and segment-level operating results. Streaming profitability, subscriber economics, content spending, park attendance, per-capita guest spending, and sports rights costs can move in different directions within the same quarter. Disney is also scheduled to report fiscal third-quarter 2026 results on August 5, 2026, making the next segment disclosure a relevant checkpoint for determining whether the long-term EBITDA spread continues to be supported by multiple businesses or remains concentrated in a smaller group of earnings drivers.
Parker-Hannifin Corporation (PH)
5-Year Revenue CAGR: 8.48%
5-Year EBITDA CAGR: 16.20%
Parker-Hannifin's EBITDA has grown at nearly twice the rate of revenue, a pattern consistent with stronger operating leverage across an increasingly diversified industrial portfolio. For Parker, the significance of the signal lies in its ability to translate mid-single-digit and high-single-digit sales growth into materially faster earnings expansion. Acquisitions, portfolio changes, pricing, productivity, and a greater share of higher-margin engineered products and aftermarket activity can all contribute to that result, so the CAGR spread should be viewed as the output of several operating levers rather than a single margin initiative.
Current results provide additional support for the margin component of the screen. In Parker's fiscal third quarter of 2026, sales increased 11% to a record $5.5 billion and organic sales rose 6.5%. Segment operating margin reached 23.4%, up 20 basis points, while adjusted segment operating margin increased 40 basis points to 26.7%. The company also reported 16% growth in adjusted net income. These figures indicate that recent earnings growth has continued to exceed the pace of underlying organic sales, although segment differences and acquisition effects remain relevant to the comparison.
Parker's story is best illustrated by joining the income statement history with segment-margin and acquisition datasets. Organic growth reveals what the existing business is producing, while reported revenue includes currency, acquisitions, and divestitures. Order trends, aerospace and industrial segment performance, backlog, and cash conversion can then show whether the EBITDA advantage reflects recurring execution or temporary mix benefits. Tracking those measures together helps prevent acquisition-driven revenue changes from being mistaken for purely organic operating leverage.
Vertiv Holdings Co (VRT)
5-Year Revenue CAGR: 18.25%
5-Year EBITDA CAGR: 43.11%
Vertiv has the widest revenue-to-EBITDA growth spread in the group. EBITDA compounded at 43.11% over five years, compared with an already strong 18.25% revenue CAGR. That difference reflects more than demand growth in data-center infrastructure. It indicates a substantial change in the earnings generated from that demand, supported by volume leverage, pricing, procurement, manufacturing productivity, and a more favorable operating structure. Among the five companies, Vertiv offers the most pronounced example of a high-growth business simultaneously improving profitability.
The latest reported quarter reinforces the scale of the operating shift. First-quarter 2026 net sales rose 30% to $2.65 billion, including 23% organic growth, while adjusted operating profit increased 64% to $551 million. Adjusted operating margin expanded 430 basis points to 20.8%, with the company attributing the improvement to higher-volume operating leverage and positive price-cost performance, including tariff mitigation. Demand was especially strong in the Americas, where organic sales grew 44% amid continued data-center investment.
The central issue for readers is not whether demand is currently strong, but how efficiently Vertiv converts a rapidly expanding order book into revenue, profit, and cash. Income statement data shows the historical EBITDA acceleration, while orders, backlog, book-to-bill, regional margins, free cash flow, and capital expenditure provide a more complete operating picture. The company ended 2025 with a $15.0 billion backlog and a fourth-quarter book-to-bill ratio of approximately 2.9 times, so future disclosures should be read for execution capacity, geographic mix, tariff effects, and the relationship between backlog conversion and margin performance.
The Signal Beneath the Growth Numbers
Taken together, these five companies point to a broader operating pattern: revenue growth is only the first layer of the story. Wingstop and Vertiv are converting rapid expansion into even faster EBITDA growth, while Lennox, Disney, and Parker-Hannifin show that meaningful earnings leverage can also emerge from slower top-line growth when pricing, productivity, portfolio mix, and cost structure move in the same direction. The common signal is not simply higher profitability. It is the widening distance between what the businesses are selling and what they are retaining at the operating level.
That spread deserves attention because it can reveal a change in business quality before the headline growth rate fully explains it. A rising EBITDA CAGR relative to revenue may reflect stronger incremental margins, but the source matters. Franchise economics at Wingstop are different from manufacturing productivity at Lennox or Parker-Hannifin, just as Disney's segment mix differs from Vertiv's exposure to data-center infrastructure. The screen identifies the outcome; the next stage of research has to determine whether it came from recurring operating improvements, acquisition effects, pricing, temporary cost relief, or a favorable shift in business mix.
A stronger workflow therefore connects the Income Statement Bulk API with FMP's Cash Flow Statement API. EBITDA growth that is also visible in operating cash flow and free cash flow carries a different analytical weight from earnings growth accompanied by heavier working-capital requirements or capital expenditure. Viewed within the broader financial and market datasets available through the FMP platform, the initial profitability screen becomes a starting point for testing whether reported operating leverage is also appearing in cash generation, valuation expectations, and market behavior.
Market expectations add another layer. Comparing the fundamental spread with FMP's Price Target Summary or Price Target Consensus data can show whether analysts already reflect the margin improvement in their published assumptions. Historical price and volume data can then place the signal in the context of market behavior, while insider-trading statistics provide a separate view of transactions reported by executives and directors. None of these datasets confirms the durability of an EBITDA trend on its own. Used together, they help distinguish an operating inflection that is supported by cash conversion and broader evidence from one that remains visible only in the income statement.
The practical takeaway is that EBITDA growing faster than revenue is best treated as a research trigger, not a conclusion. Across these five names, the pattern signals improving operating leverage, but the underlying mechanism differs by company. The most useful follow-up is to identify where the earnings are coming from, test whether they are converting into cash, and assess how much of that improvement is already embedded in analyst expectations and market pricing.
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
Used consistently, the FMP Income Statement API and Income Statement Bulk API turns a one-time CAGR screen into a repeatable way to track whether operating leverage is strengthening, fading, or shifting across companies. The value is not in finding the signal once, but in observing how it changes as new financial data arrives.
If you found this useful, you might also like: Weekly Signals Desk | Five Dividend Increases Flagged by the FMP API (July 13-17)
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.
