This week's screen surfaced an unusual divergence: EBITDA growth compounding materially faster than top-line revenue across a small cluster of names. In a market still debating whether margin expansion has run its course, that pattern stands out — not as a narrative call, but as an operating signal. The common thread wasn't sector hype or earnings surprises, but what showed up quietly in the financials when viewed across multiple cycles.

The scan was built using the FMP's Income Statement API, which allows multi-year revenue and EBITDA series to be pulled and normalized quickly. In this note, we walk through how that API was used, why the signal matters right now given current rotation and cost discipline trends, and how five companies emerged with sustained CAGR momentum that hasn't fully migrated into market consensus yet.

5 Companies With Strong CAGR Momentum

AMD Advanced Micro Devices, Inc.

5-Year Revenue CAGR: 33.56%
5-Year EBITDA CAGR: 53.45%

AMD's multi-year profile shows a widening gap between revenue growth and EBITDA expansion, with operating earnings compounding meaningfully faster than sales. That spread is not incidental. Over a five-year window that spans multiple product cycles and demand regimes, it suggests that incremental revenue has increasingly carried higher margin content — a structural rather than episodic shift. From a signal perspective, this is what operating leverage looks like before it becomes visible in headline profitability ratios.

What makes the pattern notable is the consistency of the EBITDA trajectory despite revenue volatility across end markets. This implies cost discipline and mix optimization have played a larger role than simple volume growth. For readers tracking this setup, the income statement time series is the anchor, but pairing it with segment-level disclosures and R&D intensity trends can help contextualize how margin expansion is being achieved and whether it aligns with capital allocation priorities reflected in cash flow statements.

FSM Fortuna Mining Corp.

5-Year Revenue CAGR: 43.80%
5-Year EBITDA CAGR: 56.48%

Fortuna's growth profile stands out even within the typically cyclical mining universe. Revenue expansion above 40% annually over five years points to asset growth and production scaling, but the faster EBITDA CAGR indicates that operating efficiency has improved alongside scale. In extractive industries, that combination is less common than it appears on the surface, as rising output often brings cost creep with it.

The signal here is less about commodity price direction and more about margin capture across cycles. Sustained EBITDA compounding above revenue suggests that cost per ounce dynamics, asset mix, or operational execution have trended favorably over time. To deepen the read, income statement data can be paired with production metrics, all-in sustaining cost disclosures, and capital expenditure patterns to assess whether margin gains are rooted in operational improvements rather than transient pricing effects.

HBM Hudbay Minerals Inc.

5-Year Revenue CAGR: 14.40%
5-Year EBITDA CAGR: 20.51%

Hudbay's CAGR profile is more measured, but the relationship between revenue and EBITDA growth remains instructive. EBITDA compounding at a materially higher rate than sales suggests that margin recovery or cost normalization has been an important contributor over the period. In capital-intensive resource businesses, even mid-teens revenue growth paired with stronger EBITDA expansion can materially change the earnings base.

Rather than signaling acceleration, this pattern points to stabilization and operating refinement. The data implies that incremental revenue has become more profitable than in prior cycles, a detail that can be overlooked when focusing solely on top-line growth rates. Monitoring unit economics through income statement trends alongside balance sheet leverage and mine-level disclosures can help determine whether this margin behavior is durable across different commodity environments.

EXEL Exelixis, Inc.

5-Year Revenue CAGR: 18.44%
5-Year EBITDA CAGR: 113.37%

Exelixis shows the most pronounced divergence in the group. While revenue growth has compounded at a solid but not extreme pace, EBITDA has expanded at a triple-digit CAGR over five years. That scale of difference typically reflects a business moving through a profitability inflection — where fixed costs flatten and incremental revenue drops disproportionately to operating income.

The analytical takeaway is not simply “strong margins,” but timing. EBITDA growth of this magnitude over multiple years indicates that the company transitioned from investment-heavy phases into a more harvest-oriented operating profile. To contextualize the signal, income statement trends should be read alongside R&D expense trajectories, collaboration revenue disclosures, and cash flow conversion. These datasets help determine whether the EBITDA expansion reflects sustainable operating leverage or a period-specific normalization effect.

MLI Mueller Industries, Inc.

5-Year Revenue CAGR: 13.19%
5-Year EBITDA CAGR: 38.03%

Mueller Industries presents a classic operating leverage case in a more mature industrial setting. Revenue growth in the low-teens would not typically attract attention on its own, but EBITDA compounding at nearly three times that rate reframes the picture. The data indicates that efficiency gains, pricing discipline, or product mix shifts have materially altered the earnings profile without requiring outsized top-line expansion.

This type of setup is often easier to miss because it lacks dramatic revenue acceleration. The signal instead resides in margin structure and cost absorption over time. Tracking income statement margins alongside input cost exposure, inventory behavior, and capital expenditure trends can help readers assess how much of the EBITDA growth stems from structural efficiency versus cyclical tailwinds.

Reading the Signal Beneath the Surface

Viewed together, the five names above are less about sector alignment and more about process alignment. They operate across semiconductors, mining, biotech, and industrial manufacturing, yet they share a common financial behavior: EBITDA compounding materially faster than revenue over a full multi-year window. That pattern is rarely accidental. It tends to surface when businesses move from growth-by-expansion to growth-by-efficiency — where incremental dollars of revenue carry meaningfully higher economic value than they did earlier in the cycle.

What stands out is that this signal often appears well before it registers in headline narratives or single-period margins. Multi-year CAGR compresses noise and forces the question of durability. When EBITDA growth consistently outpaces revenue across different operating environments, it points to internal operating leverage, mix discipline, or cost structure changes that are persisting rather than reverting. In that sense, this type of screen functions more as an early diagnostic than a definitive conclusion.

To deepen the read, income statement data alone is rarely sufficient. Analysts typically layer multiple datasets to triangulate whether the signal is structural or transient, starting with normalized operating histories and then widening the lens. Using consolidated financial datasets from platforms such as FMP allows EBITDA trends to be examined alongside cash flow generation, balance sheet movement, and consensus expectations within a single analytical framework.

At a strategy level, the takeaway is not that these companies share a common outcome, but that they share a common setup. The data points to businesses where operating performance is evolving faster than surface-level growth metrics imply. In markets where capital rotates quickly and narratives lag fundamentals, that gap — consistently observed and repeatedly measured — is often where the most useful questions begin.

How to Build a Clean CAGR Workflow Using FMP Data

A repeatable CAGR screen is less about clever formulas and more about controlling inputs. When every company is evaluated using the same data structure and time horizon, growth metrics become comparable, refreshable, and analytically useful. Below is a straightforward workflow that analysts commonly use when working with FMP's Income Statement data, designed to scale from a single name to a full universe without changing the underlying logic.

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 works best when the discipline used in the initial build is preserved as coverage expands. The process typically starts with a small, controlled group of symbols, not to find conclusions, but to confirm that the growth logic behaves consistently 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, address data gaps, and pressure-test edge cases without introducing unnecessary breadth.

Once the outputs are stable, moving to the Starter tier allows the same framework to be applied across a broader slice of the U.S. equity universe. The value here is less about volume and more about context. With a larger comparison set, relative growth profiles become easier to interpret, and CAGR-based filters begin to surface patterns that are difficult to detect in smaller samples.

For analysts extending coverage beyond U.S. equities or requiring deeper historical depth, the Premium plan broadens the dataset without requiring any changes to methodology. The screening logic remains intact; only the scope expands. That continuity is the point — allowing a workflow that begins as a focused validation exercise to scale into a durable, 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 turns CAGR from a static snapshot into a running operating read. Reapplying the same framework over time makes shifts in efficiency and margin behavior easier to contextualize as they form, rather than after they've been absorbed into consensus. At that point, the value is less about the calculation itself and more about maintaining continuity in how operating performance is observed.

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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.