This week's screen surfaced five companies where EBITDA growth is materially outpacing revenue growth, a pattern that points to improving operating leverage rather than top-line expansion alone. Autodesk, Newmont, EMCOR, KLA, and Arista Networks each show a widening gap between five-year revenue CAGR and EBITDA CAGR, suggesting that profitability is scaling faster than sales.
In this article, we break down that signal and show how to replicate the screen using FMP's Income Statement API. We will also look at how the same methodology can be expanded across a broader universe with FMP's bulk income statement data.
Key Takeaways
- All five companies show five-year EBITDA growth outpacing revenue growth, signaling stronger operating leverage rather than top-line expansion alone.
- The signal is not uniform across sectors: software, mining, contracting, semiconductor equipment, and networking each require different supporting data to explain the margin improvement.
- The strongest cases are those where EBITDA growth is also supported by cash conversion, balance-sheet stability, and consistent forward estimates.
Five Companies Showing a Clear Profitability Inflection
Autodesk, Inc. (ADSK)
5-Year Revenue CAGR: 34.76%
5-Year EBITDA CAGR: 82.73%
Autodesk shows the widest spread in this group, with five-year EBITDA growth exceeding revenue growth by nearly 48 percentage points. That gap indicates that the company has converted a growing share of incremental revenue into operating profit over the period. For a subscription-based software business, this is an important distinction. Revenue growth reflects customer demand and pricing, while substantially faster EBITDA growth can also capture the benefits of recurring revenue, delivery efficiency, product consolidation, and tighter control over operating expenses.
Recent results provide additional context for the longer-term signal. Autodesk reported fiscal 2026 revenue of $7.21 billion, an increase of 18%, while its non-GAAP operating margin reached 38%, up two percentage points. Operating cash flow and free cash flow increased by more than 50%. Those figures are not directly interchangeable with EBITDA CAGR, but they reinforce the underlying observation that profitability and cash generation have been expanding faster than sales.
The next area to monitor is whether this operating leverage remains visible after normalizing for restructuring charges, stock-based compensation, acquisition activity, and billing-cycle changes. FMP's income statement dataset can track revenue, operating income, EBITDA, and expense ratios across reporting periods, while cash flow statement data can show whether the earnings improvement is being matched by stronger cash conversion. Analyst-estimate and target datasets would add another layer by showing whether consensus assumptions already reflect the recent margin expansion.
Newmont Corporation (NEM)
5-Year Revenue CAGR: 16.50%
5-Year EBITDA CAGR: 34.42%
Newmont's EBITDA has grown at more than twice the rate of revenue over the five-year period. In mining, that relationship requires a different interpretation than it does in software. Revenue is heavily influenced by realized commodity prices and production volumes, while EBITDA also reflects operating costs, asset quality, portfolio changes, royalties, and the fixed-cost leverage inherent in large-scale mining operations. The spread therefore signals stronger profitability, but it should not automatically be treated as a permanent structural margin shift.
The company's recent financial position illustrates both sides of that analysis. Newmont ended the first quarter of 2026 with $8.8 billion in cash and a net cash balance of $3.2 billion, following a period of debt reduction and portfolio optimization. At the same time, management expects 2026 unit costs to increase because of lower production volumes, higher royalties, production taxes, and commodity-price assumptions, even as direct operating costs are expected to remain broadly stable through productivity initiatives.
For Newmont, the durability of the signal is best evaluated alongside realized gold prices, production volumes, all-in sustaining costs, depreciation, and capital expenditure. Income statement data establishes the revenue-to-EBITDA relationship, but cash flow and commodity-sensitive operating metrics are necessary to separate price-driven leverage from improvements in mine-level execution. Historical earnings estimates can also help show how closely reported profitability has tracked consensus expectations during changing gold-price environments.
EMCOR Group, Inc. (EME)
5-Year Revenue CAGR: 13.17%
5-Year EBITDA CAGR: 25.44%
EMCOR's five-year EBITDA CAGR is almost double its revenue CAGR, pointing to a meaningful improvement in the profitability of its project and service mix. For a specialty contractor, this matters because revenue growth alone can be misleading. Large projects can add substantial sales without generating attractive returns if labor productivity, procurement, scheduling, or contract pricing deteriorate. EBITDA growing faster than revenue suggests that the company has, over the measured period, expanded without allowing those execution pressures to absorb all of the incremental value.
The latest operating data remains consistent with that broader pattern. EMCOR reported first-quarter 2026 revenue of $4.63 billion, up 19.7% year over year, while diluted earnings per share increased 30%. Operating margin rose to 8.7% from 8.2%, and remaining performance obligations reached a record $15.62 billion, an increase of 32.9%. The backlog figure is particularly relevant because it provides visibility into future activity, although the eventual margin contribution still depends on project mix, labor availability, and cost discipline.
The analytical focus should remain on whether margin expansion is broad-based across EMCOR's segments or concentrated in specific project categories. Income statement data can track gross profit, operating income, and EBITDA progression, while segment disclosures and revenue-by-product datasets would help distinguish organic execution from acquisition-related growth. Earnings-call transcripts and estimate revisions are also useful for monitoring management commentary around data centers, advanced manufacturing, healthcare facilities, labor costs, and the conversion of remaining performance obligations into reported revenue.
KLA Corporation (KLAC)
5-Year Revenue CAGR: 16.63%
5-Year EBITDA CAGR: 21.70%
KLA's profitability spread is narrower than those of Autodesk, Newmont, or EMCOR, but it remains notable because the company already operates with comparatively high margins. A five-year EBITDA CAGR of 21.70%, against revenue growth of 16.63%, indicates that earnings capacity has continued to scale from an elevated base. That pattern is consistent with the economics of semiconductor process control, where increasingly complex chip architectures require more inspection, metrology, and yield-management activity.
Recent results also show why the signal should be assessed against expectations rather than in isolation. KLA reported fiscal third-quarter 2026 revenue of $3.42 billion and adjusted earnings of $9.40 per share, both above consensus estimates. The company guided to approximately $3.58 billion in fiscal fourth-quarter revenue, supported by AI-related semiconductor investment, but the market response remained cautious because expectations were already high and management noted memory-related margin pressure.
For KLA, the most informative follow-up data includes gross margin, service revenue, research and development spending, customer concentration, and geographic exposure. The income statement endpoint can establish whether EBITDA continues to outgrow sales, while segment and geographic revenue datasets can show where that leverage is originating. Analyst-estimate histories are especially relevant here because semiconductor-equipment valuations often respond not only to reported growth, but also to the difference between actual results, guidance, and already elevated consensus assumptions.
Arista Networks, Inc. (ANET)
5-Year Revenue CAGR: 30.61%
5-Year EBITDA CAGR: 38.38%
Arista combines one of the strongest revenue growth rates in the screen with an even faster rate of EBITDA expansion. That combination is analytically different from a low-growth cost-cutting story. The figures suggest that the company has maintained substantial top-line momentum while also increasing the amount of operating profit generated from its expanding cloud and data-center networking business.
The current operating environment adds an important qualification. Arista reported first-quarter 2026 revenue of $2.71 billion, up 35.1% year over year, as demand for AI networking infrastructure remained strong. However, management also identified supply constraints and higher component costs as pressures on near-term margins. The company had previously raised its 2026 revenue-growth outlook to 25% and increased its AI-networking revenue expectation to $3.25 billion, while maintaining a gross-margin framework in the low-to-mid 60% range.
The key question is whether EBITDA continues to outpace revenue when supply-chain costs, customer concentration, and product mix are incorporated into the comparison. Income statement data can track gross margin, operating expenses, and EBITDA, while customer-concentration disclosures and geographic revenue data would clarify the dependence on major cloud customers. Deferred-revenue figures, analyst estimates, and earnings-call transcripts can also help evaluate whether current demand is translating into recognized revenue and sustained profitability at the same pace.
The Signal Beneath the Growth Numbers
Taken together, these five companies point to a broader distinction that a revenue screen alone would miss. Autodesk, Newmont, EMCOR, KLA, and Arista operate in very different industries, yet each has produced faster EBITDA growth than revenue over the measured period. The common thread is not sector exposure. It is the ability to convert expansion into profit at a progressively stronger rate.
That makes the gap between revenue CAGR and EBITDA CAGR more useful as a diagnostic signal than as a standalone ranking. A wide spread can reflect durable operating leverage, but it can also result from commodity prices, acquisition effects, restructuring, an unusually favorable project mix, or recovery from a depressed earnings base. The next stage of the analysis is therefore to determine what is driving the spread and whether the improvement is also visible in cash generation, balance-sheet quality, and forward expectations.
A deeper screen starts by treating FMP as a connected financial dataset rather than relying on a single output. The Income Statement API can identify the EBITDA-to-revenue spread, while the Cash Flow Statement API tests whether that improvement is translating into operating and free cash flow. Balance-sheet data adds a further control for debt, liquidity, and working-capital pressure, and TTM Key Metrics helps compare valuation and capital efficiency across the five companies.
Expectations matter as well. Comparing the historical operating signal with FMP's Financial Estimates API helps identify whether analysts expect the revenue-to-profitability spread to persist, narrow, or reverse. Price Target Summary and Historical Grades data can show whether consensus sentiment has adjusted alongside the fundamental improvement, while earnings-surprise data provides evidence of how consistently reported results have exceeded or fallen short of expectations.
The practical takeaway is that EBITDA outgrowing revenue should be treated as the start of the research process, not the conclusion. The strongest cases are those where margin expansion is supported by cash conversion, a stable balance sheet, and operating results that remain consistent after accounting for industry-specific factors. Where those supporting signals diverge, the CAGR gap still has value, but primarily as a prompt for closer investigation rather than evidence of a uniformly improving business.
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 this screen is not in a single snapshot, but in tracking whether the gap between revenue and EBITDA growth persists as new results arrive. Re-running the same framework through FMP's Income Statement API and Income Statement Bulk API turns a one-time observation into a repeatable operating signal.
If you found this useful, you might also like: Weekly Signals Desk | Price-Target Gaps Identified via the FMP API (July 6-10)
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.
