This week's screen surfaced an interesting divergence that's starting to show up across multiple sectors: EBITDA growth is compounding materially faster than top-line revenue. That pattern tends to emerge when operating leverage is quietly improving—often ahead of sentiment shifts or narrative confirmation. Using the FMP's Income Statement API, this pass isolates companies where margin expansion isn't theoretical, but already embedded in the numbers.

In this article, we break down how that signal appeared across five names with sustained multi-year CAGR momentum, and walk through how the same FMP Income Statement API can be used to build a repeatable, discipline-driven screen that surfaces these inflections early rather than in hindsight.

5 Companies With Strong CAGR Momentum

AEM Agnico Eagle Mines Limited

5-Year Revenue CAGR: 28.16%
5-Year EBITDA CAGR: 40.86%

Agnico Eagle's spread between revenue and EBITDA growth is notable even within a sector that has benefited from favorable gold pricing over the last cycle. A five-year EBITDA CAGR north of 40% versus revenue growth in the high-20s points to more than just price tailwinds—it reflects sustained operating discipline following years of portfolio rationalization and asset consolidation. The merger with Kirkland Lake materially reshaped the company's cost structure, and that impact is now visible in multi-year operating leverage rather than one-off margin spikes.

What makes this signal worth attention is its durability. Mining margins are typically cyclical and volatile, yet AEM's EBITDA compounding suggests cost control and asset quality have held through varying commodity conditions. Tracking this dynamic forward is less about short-term gold moves and more about whether unit costs and sustaining capex remain contained. Income statement data paired with per-ounce cost disclosures and capital expenditure trends would be the most relevant datasets to monitor how persistent this margin structure proves to be.

AU AngloGold Ashanti plc

5-Year Revenue CAGR: 14.75%
5-Year EBITDA CAGR: 31.62%

AngloGold's growth profile shows a similar, though more measured, margin expansion story. Revenue growth in the mid-teens combined with EBITDA compounding at over twice that rate suggests incremental improvements in operating efficiency rather than aggressive top-line expansion. For a globally diversified miner with exposure across jurisdictions, that differential often reflects portfolio pruning, improved grade profiles, or reduced operational friction at mature assets.

The signal here lies in consistency rather than acceleration. AngloGold's EBITDA CAGR implies that profitability improvements have been spread across multiple years rather than concentrated in a single favorable pricing window. To contextualize this further, pairing income statement trends with geographic segment data and asset-level production costs would help clarify where margin resilience is being generated—and whether it's concentrated or broadly distributed across the portfolio.

DINO HF Sinclair Corporation

5-Year Revenue CAGR: 21.46%
5-Year EBITDA CAGR: 76.84%

HF Sinclair stands out sharply in this screen due to the magnitude of EBITDA compounding relative to revenue. A nearly 77% five-year EBITDA CAGR points to structural operating leverage rather than simple volume growth. This reflects the combined effects of refining margin expansion, integration benefits following the legacy HollyFrontier and Sinclair combination, and exposure to favorable midstream and renewables economics during parts of the cycle.

That said, refining is inherently cyclical, which makes the persistence of this growth rate the key analytical question rather than its historical strength. The data suggests a period where fixed costs were leveraged exceptionally well, but sustainability depends on throughput stability and margin normalization. Watching segment-level income statement data alongside crack spreads, utilization rates, and capital allocation disclosures provides the clearest framework for interpreting whether EBITDA growth remains structurally supported or begins to revert toward cycle averages.

BOOT Boot Barn Holdings, Inc.

5-Year Revenue CAGR: 20.05%
5-Year EBITDA CAGR: 38.18%

Boot Barn's numbers reflect a retail business that has translated store expansion and category demand into accelerating profitability. EBITDA growth nearly doubling revenue CAGR suggests scale efficiencies—from distribution, sourcing, and SG&A leverage—have compounded meaningfully as the store base expanded. Unlike many discretionary retailers, Boot Barn's growth profile has been supported by a relatively focused merchandising strategy rather than promotional intensity.

The analytical signal here centers on operating discipline during expansion. Margin expansion alongside unit growth often narrows as store bases mature, so the key is whether incremental stores continue to contribute at similar profitability levels. Income statement trends combined with store-level productivity metrics and inventory turnover data would help assess whether EBITDA growth is being driven by healthy demand dynamics or by temporary cost leverage that may normalize as growth moderates.

ADSK Autodesk, Inc.

5-Year Revenue CAGR: 13.39%
5-Year EBITDA CAGR: 24.52%

Autodesk's spread between revenue and EBITDA growth reflects a more familiar software pattern: steady top-line expansion paired with improving margin efficiency as subscription models mature. The transition to recurring revenue has allowed operating expenses to scale more predictably, and the resulting EBITDA CAGR indicates that margin gains have been gradual but persistent rather than episodic.

What stands out is not rapid acceleration, but stability. Autodesk's EBITDA compounding suggests that cost structure optimization and pricing discipline have progressed alongside revenue growth, even as the company continues to invest in platform development. Monitoring income statement trends in tandem with deferred revenue, remaining performance obligations, and customer retention metrics offers the clearest lens into whether this margin profile remains durable as growth rates normalize.

Reading the Signal Beneath the Surface

Taken together, these five companies illustrate a shared structural dynamic spanning commodities, energy, retail, and software: EBITDA is compounding faster than revenue because cost structures are tightening beneath relatively steady demand. This type of divergence rarely surfaces in single-period views. It emerges when multi-year operating data is normalized and examined across cycles—exactly the kind of longitudinal context datasets aggregated through Financial Modeling Prep are designed to make observable. The implication is less about sector leadership and more about efficiency quietly reasserting itself after years of inflationary pressure and balance-sheet adjustment.

What stands out is how consistently this pattern appears across very different operating models. In capital-intensive businesses such as mining and refining, it reflects asset rationalization and cost containment. In consumer and software companies, it shows up through scale efficiencies and disciplined expense growth. The common thread is not macro exposure, but execution. When EBITDA CAGR persistently outpaces revenue CAGR over multiple cycles, it has historically aligned with businesses where incremental revenue dollars are becoming structurally more profitable—distinct from short-lived margin expansion driven by external pricing.

Interpreting that signal with confidence requires more than a single dataset. Income statements establish the baseline, but cash flow data helps separate accounting leverage from real cash generation, while balance sheet trends provide context on how those gains are being absorbed or deployed. Analyst expectations then serve as a reference point—not to project outcomes, but to assess whether operating leverage visible in the data is already embedded in consensus views.

Viewed through this lens, CAGR functions less as a growth headline and more as an organizing framework. When refreshed consistently using bulk historical data and paired with complementary financial statements, it allows efficiency trends to be tracked as they form—often well before they harden into narrative.

How to Build a Clean CAGR Workflow Using FMP Data

A reliable CAGR screen comes from consistency, not complexity. The key is to control for inputs and methodology so that every company is evaluated on the same footing. When the process is standardized, the resulting growth figures become something you can refresh, compare, and expand over time—rather than a static output tied to a single run. Below is a practical workflow that analysts commonly use when working with FMP's Income Statement data.

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 CAGR screen is most effective when the same discipline used in the initial build is carried forward. The process typically begins with a small, controlled set of symbols to confirm that the growth logic holds across different reporting histories and business models. At this stage, the Basic plan is sufficient, providing access to the Income Statement endpoints needed to validate calculations, handle data gaps, and resolve edge cases without introducing unnecessary data volume.

Once the outputs are behaving consistently, stepping up to the Starter tier allows the identical framework to be applied across the broader U.S. equity universe. The benefit here is less about increasing the number of tickers and more about improving signal context. With a wider comparison set, relative growth profiles become easier to interpret, and CAGR-based filters start revealing patterns that are difficult to detect in smaller samples.

For coverage beyond U.S. equities or for deeper historical analysis, the Premium plan extends the same workflow to global exchanges and longer reporting histories. Crucially, the methodology does not need to be reworked. The screening logic remains unchanged; only the dataset expands, allowing the process to transition from a limited validation exercise into a scalable, market-wide research input.

From Periodic Screens to an Ongoing Operating Read

When refreshed on a consistent cadence, data pulled from the FMP Income Statement API and Income Statement Bulk API shifts CAGR from a static metric into a live operating reference. Running the same framework repeatedly makes margin inflections and efficiency drift easier to contextualize as they develop, rather than after they've already been absorbed into narrative. At that point, the value is less about the math and more about maintaining continuity in how operating performance is observed over time.

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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.