Some of the strongest operating stories in the market right now are not showing up in revenue growth alone. Across several industries, a more revealing pattern is beginning to emerge: EBITDA is compounding materially faster than sales, suggesting that scale, pricing power, and cost discipline are starting to work together beneath the surface.
This week's screen, built using the FMP's Income Statement API, surfaced five companies exhibiting that exact behavior. While the businesses operate in very different segments of the economy, the underlying signal is remarkably similar — incremental revenue is translating into profitability at an accelerating rate over a multi-year period.
In this article, we break down the companies that appeared on the screen, examine what the widening gap between revenue and EBITDA growth may be signaling, and walk through how the same analysis can be replicated using FMP's Income Statement API.
Key Takeaways
- Operating leverage—not revenue growth alone—was the defining signal across all five companies. Each company generated EBITDA growth that outpaced revenue growth over a five-year period, suggesting improving profitability dynamics beneath the top-line trend.
- The pattern appeared across unrelated sectors. Vertiv, EMCOR, KLA, Lam Research, and Texas Pacific Land operate in very different industries, indicating that the signal was driven by company-level operating performance rather than a single sector trend.
- Revenue screens and profitability screens can produce very different conclusions. Several of the companies highlighted here stand out not because they delivered the fastest sales growth, but because they converted that growth into operating earnings more efficiently over time.
- The most useful follow-up question is why EBITDA is scaling faster than revenue. Combining income statement data with cash flow, valuation, analyst estimate, and segment-level datasets can help determine whether the improvement reflects stronger business quality, changing economics, or temporary operating conditions.
Multi-Year Operating Leverage Emerging Across Five Companies
Vertiv Holdings Co (VRT)
5-Year Revenue CAGR: 17.62%
5-Year EBITDA CAGR: 43.80%
Among the names that surfaced in this screen, Vertiv shows the widest gaps between revenue growth and EBITDA growth. A 17.62% revenue CAGR is already notable on its own, but a 43.80% EBITDA CAGR suggests that the company's operating model has been scaling faster than top-line expansion would imply. That type of spread often points to a business moving through a period where pricing, product mix, manufacturing efficiency, or utilization rates are contributing meaningfully to profitability growth rather than revenue alone.
The broader backdrop helps explain why this signal has become more visible. Demand tied to AI infrastructure, data center power systems, thermal management, and high-density computing environments has accelerated across the industry. Recent reports highlighted continued expansion in AI-related data center spending, with infrastructure suppliers benefiting from large-scale capital deployment tied to compute capacity growth. Vertiv sits directly within that ecosystem through its exposure to cooling, power management, and critical infrastructure systems.
What stands out from a screening perspective is that EBITDA growth has compounded at more than double the pace of revenue over the last five years. That does not automatically imply future margin expansion, but it does indicate that incremental revenue has recently carried a different profitability profile than historical growth periods. For analysts attempting to validate whether the trend remains intact, combining income statement data with backlog, order activity, and segment-level reporting can often provide additional context around how operating leverage is developing beneath headline revenue figures.
EMCOR Group, Inc. (EME)
5-Year Revenue CAGR: 12.56%
5-Year EBITDA CAGR: 25.16%
EMCOR's appearance in the screen is notable because the company operates in a segment that is often viewed through a cyclical construction lens. Revenue has compounded at 12.56% annually over five years, while EBITDA has grown at 25.16%, indicating that profitability has expanded materially faster than the pace of business volume. In practical terms, that suggests project selection, execution quality, and contract mix may be contributing more heavily to earnings growth than simple backlog expansion.
Recent results reinforce part of that narrative. The company reported record revenue, earnings, and remaining performance obligations, with backlog continuing to grow across several end markets. The underlying signal is less about construction activity broadly and more about where EMCOR is positioned within that activity. Exposure to data centers, healthcare facilities, manufacturing projects, and infrastructure upgrades has increasingly shifted attention toward higher-complexity work where margins can behave differently than traditional volume-driven contracting businesses.
The key takeaway from the CAGR spread is not merely that earnings have grown faster than sales. Rather, it suggests that operational discipline has remained visible even while the company expanded its revenue base. Investors analyzing whether that pattern is continuing would likely benefit from tracking income statement trends alongside backlog metrics, project mix disclosures, and remaining performance obligations, which often provide a clearer view into the quality of future revenue than aggregate growth figures alone.
KLA Corporation (KLAC)
5-Year Revenue CAGR: 16.68%
5-Year EBITDA CAGR: 22.03%
KLA's signal is more subtle than some of the other names in the screen, but that may be part of what makes it interesting. Revenue has compounded at 16.68% annually over five years, while EBITDA has grown at 22.03%. The spread is narrower than what appears in Vertiv or EMCOR, yet it still reflects a consistent pattern of profitability scaling faster than sales.
Within semiconductor equipment, revenue growth alone can sometimes mask important differences in operating quality. KLA occupies a specialized position in process control, inspection, and yield management systems — areas that become increasingly important as semiconductor manufacturing complexity rises. As node transitions become more demanding and advanced packaging receives greater industry attention, spending often concentrates around technologies that help improve production efficiency rather than simply expand wafer capacity.
From a screening standpoint, the signal suggests that KLA's growth profile has not been driven solely by cyclical semiconductor demand. The EBITDA trajectory indicates that the company has maintained operating efficiency while participating in broader industry expansion. To assess whether that relationship remains stable, analysts would typically examine income statement data alongside segment disclosures, margin trends, and capital allocation metrics. Those datasets often reveal whether profitability gains are being supported by recurring operational improvements or by shorter-term cycle dynamics.
Lam Research Corporation (LRCX)
5-Year Revenue CAGR: 13.91%
5-Year EBITDA CAGR: 18.47%
Lam Research presents a similar, though distinct, operating leverage profile. Revenue compounded at 13.91% annually over five years, while EBITDA grew at 18.47%. The differential is not extreme, but it is persistent enough to stand out in a broad screen covering multiple industries and market capitalizations.
Unlike software businesses where operating leverage often emerges through low incremental distribution costs, semiconductor equipment manufacturers must balance research spending, manufacturing capacity, and cyclical customer investment patterns. That makes sustained EBITDA outperformance relative to revenue growth more meaningful because it suggests that profitability gains are occurring despite a business model that still carries significant operating requirements.
Another factor worth noting is the changing composition of semiconductor investment. Demand tied to advanced logic, memory upgrades, AI infrastructure buildouts, and increasingly complex manufacturing processes has altered spending priorities across parts of the semiconductor supply chain. The CAGR spread does not prove that these themes are driving profitability directly, but it does indicate that Lam's earnings profile has expanded more efficiently than revenue over an extended period. Income statement trends combined with capital expenditure data, wafer fabrication equipment spending metrics, and customer concentration disclosures can help provide additional context around whether that relationship is strengthening, stabilizing, or becoming more cyclical.
Texas Pacific Land Corporation (TPL)
5-Year Revenue CAGR: 24.01%
5-Year EBITDA CAGR: 26.64%
Texas Pacific Land stands apart from the rest of the group because its operating model is fundamentally different. Revenue has compounded at 24.01% annually over five years, while EBITDA has grown at 26.64%. The gap between the two figures is relatively modest, but that is precisely what makes the result noteworthy. Maintaining EBITDA growth ahead of revenue while already operating from an exceptionally high-margin base is often more difficult than expanding margins from a lower starting point.
The company's asset structure plays a central role in that dynamic. Rather than functioning as a traditional exploration and production company, Texas Pacific Land generates revenue through royalty interests, land ownership, water services, and energy-related activity across the Permian Basin. That framework creates a financial profile where incremental revenue frequently requires less capital intensity than many conventional operators, allowing a larger portion of growth to translate into operating earnings.
The screen identifies TPL not because margins are suddenly expanding, but because the relationship between revenue and EBITDA has remained consistently favorable over time. For analytical purposes, that distinction matters. In businesses with royalty-driven economics, tracking income statement data alongside production activity, royalty revenue composition, water segment performance, and land-related disclosures can often provide a clearer understanding of how profitability is evolving than revenue growth alone. The current spread suggests that operating efficiency has remained intact even as the business has scaled materially over the last five years.
Interpreting the Spread: What the Data Is Actually Signaling
The common thread across these five companies is not sector exposure, market capitalization, or even revenue growth. Vertiv, EMCOR, KLA, Lam Research, and Texas Pacific Land operate in entirely different parts of the economy, yet each produced the same underlying result: EBITDA compounded faster than revenue over a multi-year period.
That distinction matters because revenue growth alone often tells only part of the story. A company can grow sales through favorable industry conditions, acquisitions, pricing actions, or simple market expansion. When EBITDA consistently outpaces revenue over several years, the signal shifts from growth to efficiency. It suggests that management is extracting more operating profit from each incremental dollar of business than it did previously. The source can vary—improved cost structures, higher-value product mix, better utilization, pricing discipline, or scale advantages—but the pattern itself is often worth investigating.
Viewed collectively, these companies also highlight an important market dynamic. Many of the strongest operating stories today are not necessarily the fastest-growing revenue stories. Instead, they are businesses where profitability metrics are accelerating beneath the surface while headline growth remains relatively ordinary. That is often where purely top-line screens begin to lose explanatory power. Revenue identifies expansion; operating leverage helps explain the quality of that expansion.
This is also where combining multiple datasets becomes useful. The initial screen was built using FMP's Income Statement API, but the more interesting work begins after the candidates are identified. When operating trends from income statements are examined alongside cash flow data, enterprise value metrics, analyst estimates, and segment-level disclosures available across the broader Financial Modeling Prep platform, it becomes easier to distinguish simple growth from genuine operating improvement. The objective is not just to find companies growing faster, but to understand whether profitability, cash generation, and market expectations are moving in the same direction.
For example, a company showing EBITDA growth well ahead of revenue may look compelling on an income statement alone. However, reviewing Cash Flow Statement data can help determine whether those gains are translating into cash generation, while Enterprise Value and valuation metrics provide context around how the market has priced that improvement. Analyst Estimates and Price Target datasets add another layer by showing whether consensus expectations have already adjusted to the company's changing operating profile. Together, those datasets create a more complete picture than any single financial metric can provide.
In practice, the most valuable screens rarely produce answers. They produce questions. Why is profitability scaling faster than revenue? Is the improvement broad-based or concentrated in one segment? Is cash flow following earnings? Are analyst expectations already reflecting the change? The screen identifies where the pattern exists. The next layer of data helps explain why.
That is ultimately what makes this type of signal useful. It shifts attention away from isolated growth figures and toward the relationship between growth and profitability. Across all five companies, the numbers point to the same underlying observation: operating performance improved at a faster rate than sales over an extended period. The interpretation may differ from one business to another, but the analytical starting point remains the same. For analysts, that often marks the transition from screening for growth to studying the quality and durability of that growth.
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
The value of a screen like this is not the individual names it surfaces, but the operating behavior it helps identify over time. Using data from the Financial Modeling Prep Income Statement API and Income Statement Bulk API, the framework becomes less about finding one-off outperformers and more about tracking where profitability is consistently compounding faster than revenue—a signal that often deserves a closer look as new financial data is reported.
If you found this useful, you might also like: Weekly Signals Desk | Five Dividend Increases Flagged by the FMP API (May 18-22)
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.
