A fresh screen run through the Financial Modeling Prep Income Statement API is starting to pick up a pattern that's easy to miss in headline growth numbers: operating performance is accelerating faster than top-line expansion across a small cluster of names. It's not sector-specific, and it's not tied to a single macro narrative — but the consistency is notable.
This note breaks down five companies where multi-year revenue growth is being outpaced by even stronger EBITDA compounding, using data pulled directly from the FMP Income Statement API. More importantly, it walks through how that signal is constructed — and why it's beginning to show up more frequently in recent screens.
5 Companies With Strong CAGR Momentum
ACLS Axcelis Technologies, Inc.
5-Year Revenue CAGR: 18.75%
5-Year EBITDA CAGR: 39.48%%
Axcelis stands out for the magnitude of the spread between revenue growth and EBITDA expansion. A near-40% EBITDA CAGR against sub-20% revenue growth points to sustained operating leverage rather than a one-off margin event. In practical terms, this suggests that incremental revenue has been translating into disproportionately higher operating income — typically a function of pricing strength, product mix improvement, or scale efficiencies in manufacturing and service.
That dynamic aligns with what has been observable across parts of the semiconductor equipment space, where demand tied to power devices and mature-node capacity has supported profitability even as broader semiconductor cycles fluctuate. The key signal here is not just growth, but efficiency of growth. Reviewing segment-level detail from the income statement — particularly gross margin progression and operating expense discipline — would help clarify whether this leverage is being driven by structural improvements or cyclical tailwinds. Monitoring backlog and order cadence through company disclosures can further contextualize whether this margin profile is being maintained under current demand conditions.
CDE Coeur Mining, Inc.
5-Year Revenue CAGR: 17.79%
5-Year EBITDA CAGR: 82.70%
Coeur Mining presents one of the more extreme divergences in the screen, with EBITDA compounding at more than four times the pace of revenue. In commodity-linked businesses, this type of expansion often reflects a combination of cost normalization, operational turnaround, and favorable realized pricing environments rather than pure volume growth. The magnitude here suggests that prior periods of margin compression have been reversed meaningfully.
From an analytical standpoint, this is a classic example of operating leverage embedded in cyclical industries — where relatively modest revenue gains can translate into sharp EBITDA acceleration once fixed costs are absorbed. However, this also makes the signal more sensitive to external variables such as metal prices and input costs. To better understand durability, it would be useful to pair income statement data with realized price disclosures and cost-per-ounce metrics typically found in company filings. Tracking these alongside commodity price benchmarks can help determine whether the observed CAGR reflects structural improvement or a favorable pricing window.
CLS Celestica Inc.
5-Year Revenue CAGR: 14.08%
5-Year EBITDA CAGR: 39.32%
Celestica's profile reflects a steady top-line expansion paired with a significantly faster rate of EBITDA growth — a pattern often associated with mix shift rather than broad-based demand acceleration. In the context of electronics manufacturing services, this can point to increased exposure to higher-margin segments such as aerospace, defense, or specialized hardware, where pricing and contract structure differ materially from traditional volume manufacturing.
The signal here is less about absolute growth and more about quality of revenue. A widening gap between revenue and EBITDA growth suggests that the company has been reallocating resources toward more profitable end markets or improving operational execution within existing segments. Segment reporting within the income statement, combined with margin breakdowns across business lines, would provide additional clarity. Supplementing this with customer concentration data or backlog disclosures can help assess whether this shift is broad-based or driven by a smaller set of higher-value programs.
HBM Hudbay Minerals Inc.
5-Year Revenue CAGR: 15.20%
5-Year EBITDA CAGR: 22.38%
Hudbay's growth profile is more balanced relative to others in the group, with EBITDA expansion moderately outpacing revenue growth. This suggests a degree of operating leverage, but not to the extent seen in more extreme cases like Coeur Mining. In mining, this type of spread often reflects incremental efficiency gains, cost discipline, or portfolio optimization rather than a full-cycle margin reset.
What stands out here is consistency. The differential between revenue and EBITDA growth indicates that profitability has improved alongside expansion, but without the volatility typically associated with sharp commodity swings. To evaluate this further, examining unit cost trends, production volumes, and realized pricing from company disclosures would be informative. Income statement data alone highlights the presence of leverage, but pairing it with operational metrics — such as cost per pound or ounce — helps determine whether this reflects sustained efficiency improvements or simply stable execution within a supportive pricing environment.
STRL Sterling Infrastructure, Inc.
5-Year Revenue CAGR: 14.28%
5-Year EBITDA CAGR: 32.17%
Sterling Infrastructure's growth pattern points toward expanding margins within a sector not typically associated with significant operating leverage. A doubling of EBITDA growth relative to revenue suggests that project selection, pricing discipline, or segment mix has shifted meaningfully over the observed period. In construction and infrastructure services, this can often be tied to a move toward higher-margin specialty services or improved execution on complex projects.
The analytical signal here lies in margin durability. Infrastructure businesses can exhibit cyclical revenue tied to project timing, but sustained EBITDA outperformance indicates that underlying economics may be improving. Reviewing segment-level income statement data — particularly gross margins across different project types — would help isolate where this leverage is being generated. Additionally, backlog composition and contract mix disclosures can provide context on whether current profitability reflects a temporary project cycle or a broader repositioning of the business.
Reading the Signal Beneath the Surface
Across all five names, the common thread isn't simply growth — it's the shape of that growth. EBITDA is compounding materially faster than revenue, pointing to a shared dynamic: improving efficiency in how incremental dollars convert into operating profit. Whether driven by pricing in semiconductors, cost normalization in mining, mix shift in manufacturing, or execution in infrastructure, the signal is the same — operating leverage is starting to show up in the numbers.
This pattern sits one layer below what most screens capture. Revenue growth alone doesn't distinguish between scaling efficiently and simply expanding volume. When EBITDA consistently outpaces revenue across unrelated sectors, it shifts the focus toward company-level execution — where operating structures are becoming more favorable relative to recent history.
To deepen that read, the income statement is only a starting point. A more complete view comes from layering in cash flow, balance sheet structure, and forward estimates — the kind of multi-endpoint workflow supported across datasets available through the Financial Modeling Prep platform. When those layers align — for example, margin expansion alongside improving cash generation — the signal strengthens; when they don't, it often points directly to where further scrutiny is needed.
How to Build a Clean CAGR Workflow Using FMP Data
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.
Expanding the Screen Into Full-Market Coverage
Scaling a screen like this isn't about changing the model — it's about preserving it. The CAGR formula, the selected line items, and the time horizon need to remain locked as the universe expands. The only variable that should change is coverage. That's why the process typically begins in a contained environment, where the focus is on data integrity rather than breadth. Using the Basic plan, the Income Statement endpoints provide enough depth to validate that fiscal periods align, gaps are handled consistently, and company-specific reporting differences aren't distorting the output.
Once that foundation is stable, expansion becomes mechanical. Shifting to the Starter plan allows the same framework to run across a much wider slice of the U.S. market without altering the logic itself. The advantage here isn't just scale — it's perspective. A larger sample makes it easier to contextualize whether a revenue-to-EBITDA growth spread is truly differentiated or simply reflective of broader patterns within certain sectors or size cohorts.
For broader applications — including international coverage or deeper historical runs — the Premium plan extends both the dataset and time horizon. The underlying methodology stays intact; only the scope expands. That consistency is what turns a one-time screen into a durable process: built once, tested under controlled conditions, and then applied across larger datasets without introducing analytical drift.
From Periodic Screens to an Ongoing Operating Read
What begins as a periodic screen can evolve into a steady read on how operating performance is shifting beneath the surface. Re-running this framework through the FMP Income Statement API and Income Statement Bulk API keeps the signal current — not as a one-off snapshot, but as a repeatable check on where efficiency and growth are starting to diverge.
If you found this useful, you might also like: Weekly Signals Desk | Price-Target Gaps Identified via the FMP API (March 30 - April 3)
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.
