A routine growth screen run through the FMP's Income Statement API turned up a consistent signal this week: in several cases, operating profit is compounding materially faster than revenue. That kind of divergence usually points to something deeper than simple top-line expansion — it often reflects improving cost structure, scale advantages, or disciplined capital allocation showing up in the numbers.
Five companies surfaced in the scan, spanning different corners of the market but displaying the same pattern: EBITDA growth running well ahead of revenue growth over a five-year horizon. When that spread appears repeatedly across unrelated sectors, it suggests a structural operating shift rather than a one-off earnings cycle.
In the sections below, we break down the companies highlighted by the screen and walk through how the Financial Modeling Prep Income Statement API can be used to build a clean, repeatable CAGR workflow across multiple tickers.
5 Companies With Strong CAGR Momentum
AEM Agnico Eagle Mines Limited
5-Year Revenue CAGR: 28.56%
5-Year EBITDA CAGR: 41.94%
Agnico Eagle's five-year growth profile reflects a pattern that has become increasingly visible across high-quality precious-metals producers: operational scale improving faster than the underlying commodity cycle. Revenue expanding at 28.56% annually over five years is already notable for a large-cap mining operator, but the 41.94% EBITDA CAGR suggests that margin expansion and operational leverage have been doing much of the heavy lifting beneath the surface. That spread between revenue and EBITDA growth is often a sign that a miner has moved past the capital-intensive development phase of certain assets and is beginning to extract more operating efficiency from existing production platforms.
For analysts evaluating whether the profitability expansion reflected in the CAGR data is structural or cyclical, the most informative dataset typically comes from the income statement and cash-flow series—particularly trends in operating margins, sustaining capital intensity, and free-cash-flow conversion across reporting periods.
FSM Fortuna Mining Corp.
5-Year Revenue CAGR: 45.62%
5-Year EBITDA CAGR: 59.09%
Fortuna Mining stands out in this screen for the magnitude of its top-line expansion. A 45.62% five-year revenue CAGR places the company among the fastest-growing mid-tier precious-metals producers in the dataset. Even more telling is the 59.09% EBITDA CAGR, which indicates that operating profitability has compounded at a meaningfully faster rate than sales. In mining, that kind of divergence frequently reflects two overlapping dynamics: a portfolio shift toward higher-margin production and a ramp-up of recently commissioned assets reaching steady-state output.
For smaller multi-asset producers, this pattern can emerge when development projects transition from capital-intensive build phases into revenue-generating operations. Once that inflection occurs, incremental production volumes often carry higher marginal profitability, particularly if the company has already absorbed the bulk of its development costs. The resulting financial signature — revenue rising quickly while EBITDA accelerates even faster — is exactly the type of operating leverage this screen is designed to surface.
To evaluate how durable this growth pattern is, analysts typically examine segment-level income statement data and production metrics over time. Datasets showing mine-level output, operating costs per ounce, and EBITDA margin trends provide context for whether the growth differential is driven primarily by commodity pricing, new production capacity, or structural cost improvements.
DINO HF Sinclair Corporation
5-Year Revenue CAGR: 22.07%
5-Year EBITDA CAGR: 81.82%
HF Sinclair's numbers represent one of the more dramatic divergences in the screen. While revenue expanded at a 22.07% annual rate, EBITDA grew at 81.82% over the same five-year window, indicating that the company's operating profitability has increased far faster than its top line. In refining businesses, such a gap often reflects the cyclical but powerful influence of refining margins — when crack spreads widen, profitability can expand significantly even if refined product volumes grow only modestly.
Recent results illustrate how sensitive refiners can be to margin dynamics. HF Sinclair has reported periods where refining profitability surged alongside stronger fuel margins, contributing to significantly higher segment income despite relatively stable throughput levels. The refining business is inherently capital-intensive but operationally leveraged; once facilities are running at capacity, incremental margin improvements can translate into disproportionately large EBITDA gains.
For deeper analysis, the most revealing datasets typically include income statement detail combined with refining margin indicators—particularly gross refining margins, segment EBITDA, and throughput volumes. Tracking these alongside broader energy market data helps clarify whether the EBITDA acceleration is being driven primarily by industry margin cycles or by company-specific operational improvements.
SCCO Southern Copper Corporation
5-Year Revenue CAGR: 11.49%
5-Year EBITDA CAGR: 19.49%
Southern Copper's profile in the screen reflects a different type of growth signal: steady expansion accompanied by gradual margin improvement rather than rapid top-line acceleration. Revenue increased at an 11.49% CAGR over the last five years, while EBITDA grew at 19.49%, indicating that operational profitability has expanded nearly twice as quickly as sales. For a large, established mining operator, this kind of pattern often reflects incremental cost discipline and improved operational efficiency rather than dramatic production growth.
Recent financial disclosures reinforce that narrative. Southern Copper reported record net sales and strong EBITDA performance in 2025, supported by higher metal prices and improved output of several by-products such as molybdenum and zinc. Even when copper production fluctuates slightly year to year, diversified metal output and cost management can still lift overall profitability — a dynamic that frequently shows up in EBITDA growth rates before it becomes obvious in revenue figures.
For analysts examining whether this margin expansion is structural, the most relevant datasets typically include income statement margin trends and cost-per-pound production metrics, as well as production breakdowns by metal. These indicators help determine whether profitability gains stem primarily from commodity pricing, operational efficiency, or shifts in the company's production mix.
DY Dycom Industries, Inc.
5-Year Revenue CAGR: 8.99%
5-Year EBITDA CAGR: 17.75%
Dycom's appearance in the screen highlights a different operating dynamic: infrastructure-driven growth with gradually expanding margins. Revenue increased at a modest 8.99% CAGR, while EBITDA grew at 17.75%, suggesting that operating efficiency and contract mix have improved over time. For a specialty construction and telecom infrastructure contractor, this type of spread often reflects higher utilization of crews, improved project pricing, or scale efficiencies across multi-year network buildouts.
Dycom operates primarily within the communications infrastructure ecosystem, providing engineering, construction, and maintenance services for broadband and fiber deployments. In that environment, revenue growth is often tied to the timing of carrier capital expenditures, while EBITDA expansion tends to come from operational efficiency and improved contract economics as projects scale.
To assess how persistent that profitability trend may be, analysts typically look at income statement margin progression alongside backlog and contract data. Datasets tracking operating margin trends, project mix, and capital spending by major telecom clients can help determine whether the EBITDA growth differential reflects a temporary project cycle or a longer-term shift in operating leverage.
Reading the Signal Beneath the Surface
Viewed together, the five companies in this screen — a gold major, a mid-tier miner, a refiner, a copper producer, and a telecom infrastructure contractor — don't share an obvious sector narrative. What they do share is a financial signature: EBITDA compounding materially faster than revenue. That divergence typically appears when businesses reach a stage where operational scale begins translating into stronger margins, whether through cost discipline, asset maturity, or improved pricing dynamics.
Signals like this tend to emerge when consistent financial data is examined across multiple reporting cycles. When multi-year income statement series are pulled from datasets such as those available through Financial Modeling Prep, it becomes easier to identify when profitability begins widening relative to top-line growth — a pattern that often develops quietly before it becomes part of the broader earnings narrative.
The takeaway from this screen is therefore less about any single company and more about the structure of the signal itself. Revenue growth confirms expansion, but when EBITDA accelerates faster, it suggests something inside the operating model is improving. Identifying that spread — and tracking how consistently it persists — is often where the more meaningful analytical insight begins.
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
A screening framework only remains reliable as it grows if the underlying rules stay fixed. The CAGR formula, the financial line items being measured, and the time horizon all need to remain identical as the universe expands. What changes is simply the number of companies passing through the filter. That's why most workflows start in a controlled testing environment. At this stage, the Basic plan provides sufficient access to the Income Statement endpoints to confirm that fiscal years line up correctly, that missing fields are handled consistently, and that reporting variations across companies don't distort the calculation.
Once the process is working cleanly, scaling becomes a question of coverage rather than methodology. Moving to the Starter plan allows the same screening logic to run across a much broader portion of the U.S. equity market. The benefit isn't only the larger sample size — it's the additional context that comes with it. With more companies flowing through the model, it becomes easier to see whether the spread between revenue CAGR and EBITDA CAGR is an outlier signal or simply a common pattern within certain sectors or market-cap ranges.
For analysts looking to extend the screen further — across international equities or longer historical datasets — the Premium plan expands both geographic coverage and time depth without requiring any adjustments to the model itself. The formula, filters, and logic remain unchanged; only the dataset widens. That continuity is what ultimately transforms a one-off screen into a repeatable research tool — designed once, validated carefully, and then applied at scale without compromising analytical consistency.
From Periodic Screens to an Ongoing Operating Read
Screens like this are most useful when they move from a one-time exercise to a repeatable monitoring process. Running the same CAGR framework through the Financial Modeling Prep Income Statement API and Income Statement Bulk API on a regular cadence makes it possible to track when operating leverage begins widening beneath the surface — often before it becomes obvious in headline narratives. Over time, that consistency turns a simple growth filter into a practical way of keeping a live read on how corporate profitability is evolving across the market.
If you found this useful, you might also like: Signals Desk Weekly Take via FMP API | Five Companies With Persistent Earnings Beats (March 2-6)
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.
