The latest earnings cycle continues to reward companies showing operating leverage instead of pure top-line acceleration. Across multiple sectors, a consistent pattern has started to emerge: EBITDA growth is compounding materially faster than revenue growth, signaling improving efficiency, stronger pricing power, or scaling cost structures beneath the surface.
This week's screen, built using the FMP's Income Statement API, surfaced five companies where that spread has become difficult to ignore. From infrastructure and cybersecurity to networking hardware and manufacturing services, the common thread is not sector momentum - it is margin expansion showing up persistently across multi-year financials.
In this article, we break down the companies identified by the screen, examine what the revenue-versus-EBITDA CAGR divergence is actually signaling, and walk through how the same framework can be replicated at scale using FMP's Income Statement data infrastructure.
Key Takeaways
- The screen identified five companies where EBITDA growth has materially outpaced revenue growth over a five-year period, suggesting improving operating leverage rather than simple cyclical expansion.
- The pattern appeared across unrelated sectors — including AI infrastructure, cybersecurity, consumer retail, and grid modernization — indicating the signal is tied more to operational efficiency than to a single macro narrative.
- Comparing revenue CAGR against EBITDA CAGR provided a clearer view into business quality and scalability than top-line growth alone, particularly in an environment where markets have become increasingly sensitive to margin durability and cash generation.
Multi-Year Operating Leverage Emerging Across Five Companies
Celestica Inc. (CLS)
5-Year Revenue CAGR: 14.79%
5-Year EBITDA CAGR: 40.58%
Celestica's spread between revenue growth and EBITDA growth is one of the clearest examples in this screen of operating leverage accelerating beneath a still-expanding top line. A near-15% revenue CAGR over five years is already substantial for a manufacturing and infrastructure solutions business, but EBITDA compounding above 40% changes the interpretation entirely. The signal is less about cyclical demand strength and more about mix evolution — particularly toward higher-value cloud, networking, and AI infrastructure programs where margins behave differently than traditional electronics manufacturing.
That distinction has become increasingly visible in recent quarters. The company recently reported first-quarter 2026 revenue growth of 53% year over year, while adjusted operating margin reached 8.0%, a company milestone. Management also raised full-year guidance alongside continued hyperscaler and AI infrastructure demand. The underlying takeaway is not simply “AI exposure,” which has become a crowded narrative across industrial and technology names, but the extent to which incremental revenue is now translating into disproportionate profitability expansion.
What makes the pattern notable is that the margin behavior has persisted across multiple reporting periods rather than appearing as a single-quarter anomaly. For readers building similar screens, this is where combining historical income statement data with segment-level reporting becomes useful. Revenue and EBITDA trends establish the operating leverage signal, while geographic and customer concentration data can help determine whether the improvement is broadening or tied to a narrower set of infrastructure spending cycles. Recent expansion activity tied to U.S.-based technology manufacturing capacity also suggests management is positioning around sustained enterprise infrastructure demand rather than short-duration order flow.
Deckers Outdoor (DECK)
5-Year Revenue CAGR: 18.60%
5-Year EBITDA CAGR: 25.63%
Deckers stands out in this screen because the margin expansion is occurring inside a consumer category where many peers have struggled to maintain pricing power consistently. Revenue growth approaching 19% annually over five years already reflects unusually strong brand execution for apparel and footwear, but EBITDA growth running materially faster suggests something more durable than simple volume growth. The data points toward disciplined inventory management, premium product positioning, and sustained full-price selling behavior across core brands.
Recent operating results reinforce that interpretation. Deckers raised annual sales and profit forecasts earlier this year after continued strength in HOKA footwear and resilient demand across international markets. HOKA's momentum remained a major contributor to revenue growth while gross margins benefited from reduced discounting pressure and strong direct-to-consumer execution. In practical terms, this matters because footwear margins tend to compress quickly once promotional activity accelerates. The fact that EBITDA has compounded faster than revenue over a multi-year period implies the company has, at least so far, avoided that dynamic better than much of the broader sector.
At the same time, the spread between revenue and EBITDA growth should not automatically be interpreted as linear or permanent. Some recent analyst commentary has focused on how promotional strategies, wholesale relationships, and category competition may influence future margin consistency. That makes Deckers a useful example of why pairing income statement analysis with channel mix data, inventory trends, and earnings call commentary can materially improve interpretation. The CAGR signal itself identifies operational efficiency expansion; understanding whether it remains structurally supported requires monitoring the underlying retail mechanics driving it.
Fortinet, Inc. (FTNT)
5-Year Revenue CAGR: 21.85%
5-Year EBITDA CAGR: 33.84%
Fortinet's numbers highlight a pattern that has become increasingly important across enterprise software and cybersecurity: profitability scaling faster than customer acquisition costs. Revenue compounding above 21% annually over five years already places the company among the stronger large-scale cybersecurity operators, but EBITDA growth above 33% indicates the business has continued extracting operational efficiency even while competing in an aggressive spending environment.
That operating discipline has remained visible in recent results. Fortinet reported first-quarter 2026 revenue growth of 20% year over year, while non-GAAP operating margin reached 36% and free cash flow exceeded $1 billion. Importantly, the acceleration was not isolated to services alone — product revenue growth exceeded 40%, suggesting hardware demand and platform integration spending remain active despite broader enterprise budget scrutiny. In many software and security businesses, periods of elevated infrastructure investment can pressure margins temporarily. Here, the opposite dynamic appears to be occurring.
The broader signal is less about headline cybersecurity demand — which has remained structurally strong for years — and more about the company's ability to preserve margin quality while scaling its platform breadth. This is where combining income statement trends with billings growth, deferred revenue data, and free cash flow metrics becomes especially valuable. EBITDA acceleration without corresponding cash generation can sometimes reflect temporary accounting dynamics; Fortinet's recent reporting suggests the operating leverage is also translating into underlying cash efficiency. That relationship is often more informative than revenue growth alone when evaluating mature infrastructure software companies.
Arista Networks, Inc. (ANET)
5-Year Revenue CAGR: 29.77%
5-Year EBITDA CAGR: 37.26%
Arista's inclusion in this screen reflects a trend increasingly visible across AI-linked infrastructure spending: networking providers benefiting not just from higher deployment volumes, but from increasingly complex, higher-value architectures. Revenue compounding near 30% annually over five years already signals exceptional execution at scale. EBITDA growing even faster suggests the company has managed to expand profitability while participating in one of the most capital-intensive buildout cycles in technology infrastructure.
What makes the spread analytically important is the type of business Arista operates. High-growth hardware companies often experience margin compression during rapid expansion phases because supply chain costs, component pricing, and deployment scaling absorb operating gains. Arista's figures imply the opposite dynamic — that the company's software integration, cloud networking specialization, and hyperscaler exposure may be allowing incremental revenue to flow through more efficiently than many traditional hardware peers.
The key area to monitor going forward is concentration and infrastructure pacing. Arista remains closely tied to large cloud and AI infrastructure customers, meaning changes in hyperscaler capital expenditure cycles can materially influence growth cadence even if long-term demand remains healthy. For this reason, pairing income statement data with customer concentration disclosures, capex trends from major cloud operators, and segment-level networking demand can provide a more complete framework than revenue analysis alone. The EBITDA spread itself identifies operational strength; the surrounding infrastructure data helps determine how broad-based and durable that efficiency profile may be over time.
Quanta Services, Inc. (PWR)
5-Year Revenue CAGR: 18.62%
5-Year EBITDA CAGR: 22.01%
Quanta's appearance in this screen is notable because the operating leverage signal is emerging from infrastructure and utility services rather than software or high-margin technology verticals. In industries tied to grid modernization, energy systems, and large-scale engineering projects, revenue growth can often mask uneven profitability due to labor intensity and project variability. EBITDA consistently outpacing revenue over a five-year period suggests Quanta has been improving execution quality alongside expansion.
The interpretation here is closely tied to infrastructure complexity. As grid modernization, renewable integration, and data-center-related power demand have accelerated, large engineering and utility contractors have increasingly operated in environments where specialized capabilities matter more than pure scale alone. That shift can influence project mix, bidding discipline, and margin structure over time. In Quanta's case, the spread between revenue and EBITDA growth points toward a business capturing more operating efficiency from that environment rather than merely processing a larger backlog.
For analytical purposes, this type of signal becomes more meaningful when paired with backlog data, operating cash flow trends, and capital allocation metrics. Infrastructure businesses can report strong revenue growth while still facing execution pressure beneath the surface. Watching how EBITDA margins behave relative to backlog conversion and project mix can help distinguish between cyclical contract acceleration and structurally improving profitability. The current multi-year spread suggests the latter deserves closer examination, particularly as electrification and grid investment themes continue expanding across multiple industrial sectors.
Interpreting the Spread: What the Data Is Actually Signaling
What stands out across these five companies is not simply that revenue is growing — plenty of companies can produce cyclical top-line expansion during favorable industry conditions. The more important signal is that EBITDA growth is consistently outrunning revenue growth across entirely different sectors, from cybersecurity and AI networking to industrial infrastructure and consumer retail. When that pattern appears repeatedly across unrelated business models, it usually points to something more structural than temporary demand acceleration.
In practical terms, the spread often reflects a company reaching a different stage of operational efficiency. Incremental revenue begins converting into profit at a faster rate because pricing power improves, fixed-cost absorption strengthens, product mix shifts upward, or operational complexity starts scaling more efficiently. That does not automatically mean margins will continue expanding indefinitely, but historically, sustained divergence between revenue CAGR and EBITDA CAGR has often coincided with periods where underlying business quality becomes more visible in the financials.
The distinction matters because raw revenue growth alone has become a less reliable signal in an environment where capital costs remain elevated and markets have become increasingly sensitive to efficiency metrics. A company growing revenue at 25% while EBITDA grows at 10% tells a very different story from one where EBITDA compounds at 35% against the same revenue trajectory. The latter suggests the underlying operating model may be strengthening as scale increases rather than becoming more expensive to maintain.
This is where combining multiple datasets inside Financial Modeling Prep becomes more useful than treating EBITDA expansion as a standalone signal. Revenue and EBITDA trends from the Income Statement API establish the initial operating leverage pattern, but the interpretation sharpens once those figures are evaluated against cash flow conversion, balance sheet efficiency, analyst expectation data, and insider activity over the same reporting periods. Comparing margin expansion against operating cash flow trends can help distinguish between businesses genuinely improving underlying economics and those benefiting from shorter-duration accounting or cycle-driven effects, particularly in sectors where infrastructure spending, AI demand, or consumer pricing strength can temporarily distort headline growth.
The picture becomes more nuanced once market expectations are introduced into the workflow. Analyst Estimates and Price Target datasets can provide context around whether operational improvement is occurring ahead of, in line with, or behind consensus assumptions, while Insider Trading data may help identify whether executive positioning is diverging from the improving financial trend. In sectors tied closely to infrastructure or cyclical spending, layering company-level performance against broader economic and industry datasets can also help separate firm-specific execution from favorable macro conditions.
What makes the current group notable is that the spread is not isolated to a single market narrative. AI infrastructure exposure appears in some names, consumer premiumization shows up in another, and grid modernization drives a different subset entirely. Yet the financial pattern remains similar: EBITDA compounding faster than revenue over multiple reporting periods. That consistency is often where screens become most useful — not as predictive tools, but as frameworks for identifying where operational leverage is already visible clearly enough in the numbers to justify deeper investigation.
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:
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:
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
Scaling this type of screen is less about adding complexity and more about holding the framework steady. The CAGR formula, the chosen financial line items, and the time horizon should remain unchanged — expansion should only affect how many companies are being evaluated, not how they're being measured. That's why the process starts narrow. Within the Basic plan, the Income Statement endpoints provide enough coverage to ensure reporting periods line up, missing data is handled consistently, and company-specific nuances aren't skewing results.
Once that baseline is validated, widening the universe becomes a straightforward extension. Moving into the Starter plan allows the same logic to be applied across a broader segment of the U.S. market without modifying the screen itself. The benefit here isn't just more data — it's context. With a larger sample, it becomes easier to distinguish between company-specific operating leverage and patterns that are common within certain sectors or market caps.
For deeper coverage — whether that means expanding internationally or extending the historical window — the Premium plan simply increases the dataset's reach. The methodology doesn't change. What evolves is the scope, not the structure. That continuity is what makes the screen repeatable: defined once, pressure-tested in a controlled set, and then applied more broadly without introducing inconsistencies into the analysis.
From Static Screens to a Continuous Operating Signal
The value of a screen like this is not in identifying a single quarter of strength — it is in tracking where operating leverage continues compounding across reporting cycles while broader narratives shift around it. Using structured financial data from the Financial Modeling Prep Income Statement API and Income Statement Bulk API makes that process repeatable, allowing the focus to stay on whether the underlying efficiency trend is persisting, broadening, or beginning to fade as new data comes in.
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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.
