Markets often reward revenue growth first, but some of the more interesting operating stories emerge when profitability begins accelerating faster than sales. This week's screen surfaced five companies where five-year EBITDA CAGR has pulled meaningfully ahead of revenue growth—a pattern that can point to improving operating leverage, stronger cost discipline, or a business model reaching a more efficient stage of growth.
The screen was built using FMP's Income Statement API, which makes it possible to calculate and compare long-term growth rates across standardized financial statements. Beyond highlighting this week's five companies, we'll also walk through the API workflow used to build the screen, explain how the CAGR calculations are performed, and show how the same methodology can be scaled across a broader universe using FMP's Income Statement data.
Key Takeaways
- Five companies surfaced where EBITDA has compounded materially faster than revenue over the past five years, highlighting businesses that have strengthened operating performance beyond top-line growth alone.
- The screen demonstrates how multi-year CAGR analysis can uncover potential operating leverage, helping distinguish margin expansion from simple revenue acceleration across different sectors.
- The article breaks down the analytical workflow behind the screen using FMP's Income Statement API, showing how standardized historical financial data can be transformed into a repeatable screening methodology.
Five Companies Showing a Clear Profitability Inflection
Carlisle Companies Incorporated (CSL)
5-Year Revenue CAGR: 11.21%
5-Year EBITDA CAGR: 16.27%
Carlisle's five-year growth profile shows a notable widening between top-line expansion and EBITDA growth. Revenue compounded at 11.21% annually, while EBITDA advanced at 16.27%, indicating that profitability improved at a meaningfully faster pace than sales. That type of divergence often reflects more than cyclical demand—it suggests that pricing, product mix, operational efficiency, or disciplined capital allocation have had an increasingly important influence on earnings quality.
That interpretation aligns with Carlisle's longer-term operating strategy. In recent reporting, management has continued emphasizing its Vision 2030 framework, disciplined pricing, productivity initiatives through the Carlisle Operating System, and balanced capital deployment despite a slower construction environment. The company has also reaffirmed its 2026 outlook while acknowledging continued macro headwinds in certain end markets.
For investors, the key question is not whether revenue continues growing at the same pace, but whether operating leverage remains durable as demand normalizes across commercial construction. Income statement data provides the foundation for identifying this margin expansion, while future quarters can be monitored alongside segment-level profitability and capital allocation metrics to determine whether the efficiency trend remains intact.
Boot Barn Holdings, Inc. (BOOT)
5-Year Revenue CAGR: 21.74%
5-Year EBITDA CAGR: 41.21%
Boot Barn stands out as the strongest operating leverage story in this week's screen. Revenue increased at an already impressive 21.74% compound annual rate, yet EBITDA expanded at nearly twice that pace, reaching a 41.21% CAGR. When earnings compound substantially faster than sales over several years, it generally indicates that scale is beginning to translate into stronger operating economics rather than growth simply requiring proportionally higher spending.
The company has spent recent years expanding its store footprint while simultaneously strengthening private-label penetration and merchandise margins. More recently, management has continued reporting resilient demand despite a retail environment that remains selective across discretionary categories, suggesting execution has remained disciplined even as consumer spending patterns have become more uneven. Rather than relying solely on revenue acceleration, Boot Barn's financial profile increasingly reflects improvements in margin structure alongside expansion.
To understand whether this trend continues, income statement data remains the primary reference point, but investors may also find value in tracking comparable-store sales, gross margin progression, and analyst estimate revisions over time. Together, those datasets help distinguish whether future EBITDA growth continues to be driven primarily by operating efficiency, sales productivity, or changes in consumer demand.
RTX Corporation (RTX)
5-Year Revenue CAGR: 10.56%
5-Year EBITDA CAGR: 22.98%
RTX presents a different type of profitability inflection. Revenue grew at a solid 10.56% annual rate, while EBITDA compounded at 22.98%, implying that earnings expanded substantially faster than overall sales. For large industrial and aerospace companies, this type of pattern often reflects improvements in program mix, operational execution, and manufacturing efficiency rather than rapid top-line acceleration alone.
Over the past year, attention has increasingly shifted toward commercial aerospace production, defense demand, and the company's progress following prior supply-chain and engine-related challenges. Those developments have kept investors focused not only on revenue recovery but also on the pace at which profitability improves as production normalizes across multiple business segments.
Going forward, income statement trends provide the clearest view of operating leverage, while backlog data, segment reporting, and analyst estimates help place those financial results into broader context. Monitoring how margins evolve relative to revenue growth may offer a more informative signal than revenue alone for a business with long-cycle contracts and diversified end markets.
Hudbay Minerals Inc. (HBM)
5-Year Revenue CAGR: 15.99%
5-Year EBITDA CAGR: 23.61%
Hudbay's financial profile illustrates how operating performance can strengthen even within a cyclical commodity business. Revenue compounded at 15.99% annually over five years, while EBITDA increased at 23.61%, indicating that profitability improved at a materially faster rate than sales. For mining companies, this often reflects a combination of stronger realized pricing, production efficiency, cost discipline, and asset optimization rather than volume growth alone.
The broader mining sector has recently benefited from continued attention on copper's role in electrification and infrastructure investment, while precious metals have remained supported by macroeconomic uncertainty. Within that backdrop, investors continue evaluating producers based on cost performance and cash generation rather than commodity prices alone, making EBITDA expansion an especially relevant operating signal.
Income statement data highlights the improvement captured by this screen, but production reports, operating cost metrics, and realized metal prices provide important context for understanding how much of that profitability stems from company execution versus broader commodity market conditions. Tracking those datasets together offers a more complete picture of whether margin strength remains supported across future reporting periods.
AngloGold Ashanti plc (AU)
5-Year Revenue CAGR: 18.74%
5-Year EBITDA CAGR: 38.17%
AngloGold Ashanti recorded one of the widest gaps in this week's screen. Revenue compounded at 18.74% annually, while EBITDA advanced at 38.17%, suggesting that operating earnings expanded at more than twice the pace of sales. Such a divergence often signals improving cost performance and stronger operating margins, particularly in an industry where profitability can fluctuate significantly alongside commodity prices.
Gold producers have recently benefited from a supportive bullion price environment, but differences in profitability increasingly depend on operational execution rather than metal prices alone. Factors such as production consistency, sustaining costs, portfolio optimization, and capital discipline frequently determine which companies convert higher revenues into proportionally stronger cash generation. AngloGold's multi-year CAGR profile suggests that those operating dynamics warrant closer examination alongside commodity trends.
Income statement analysis identifies the profitability acceleration visible in this screen, while production updates, all-in sustaining cost (AISC) disclosures, and reserve reporting provide additional context for evaluating whether the underlying operating improvements continue over time. Taken together, those datasets help separate commodity-driven earnings gains from company-specific operational performance.
The Signal Beneath the Growth Numbers
Although the five companies in this screen operate in very different industries—from aerospace and mining to specialty retail and building products—they share the same underlying characteristic: EBITDA has compounded materially faster than revenue over a multi-year period. That doesn't automatically identify the "best" businesses, nor does it imply a future outcome. What it does highlight is a consistent financial pattern that often deserves closer examination. When operating earnings accelerate more quickly than sales over an extended period, it suggests that something inside the business has become more efficient, whether through pricing discipline, cost control, portfolio optimization, or improved operating leverage.
Viewed in isolation, however, EBITDA CAGR is only the starting point. The next step is determining why profitability expanded. One company may be benefiting from structural margin improvement, while another may simply be cycling through unusually favorable commodity prices or demand conditions. Separating those drivers requires moving beyond a single financial statement and building a workflow that connects multiple datasets.
That broader perspective is where a platform like Financial Modeling Prep becomes useful—not because it produces the conclusion, but because it makes it possible to examine the same company through multiple financial lenses using standardized datasets. Income Statement data can be evaluated alongside Cash Flow Statement and Balance Sheet data to determine whether stronger EBITDA is translating into healthier cash generation, improving capital efficiency, or a more resilient financial position. Looking at those relationships often provides a more complete explanation of margin expansion than revenue growth alone.
Market expectations add another layer of context. Comparing multi-year operating improvements with Analyst Estimates and Price Target Summary data helps determine whether consensus expectations have evolved alongside the company's fundamentals or whether the financial profile has improved faster than analyst assumptions. Likewise, Insider Trading activity can provide additional perspective on how management has behaved during periods of expanding profitability—not as a predictive signal, but as another piece of evidence within a broader research framework.
Ultimately, this week's screen is less about identifying winners than identifying companies where the relationship between revenue and profitability has materially changed over time. That narrowing focus creates a stronger starting point for deeper research, allowing investors to spend less time searching for candidates and more time understanding the operational drivers behind sustained improvements in business performance.
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 isn't the list itself—it's the repeatable process behind it. By rebuilding the same analysis with each reporting cycle using the FMP's Income Statement API and Income Statement Bulk API, recurring changes in operating performance become easier to identify, compare, and place into context as new financial data is released.
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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.
