This week's income-statement scan surfaced an unusual cluster of names showing the same pattern: EBITDA growth compounding materially faster than top-line expansion. Using the FMP's Income Statement API as the source of record, the data points to operating leverage emerging in places the market doesn't typically group together — power semiconductors, contract manufacturing, and large-cap gold producers included.

This article breaks down how that signal showed up, why it matters in the current rotation toward profitability and cash efficiency, and how the FMP Income Statement API was used to isolate and validate the trend across five companies with clear multi-year CAGR momentum.

5 Companies With Strong CAGR Momentum

VICR Vicor Corporation

5-Year Revenue CAGR: 7.77%
5-Year EBITDA CAGR: 42.29%

Vicor's spread between revenue growth and EBITDA expansion is one of the widest in this group, and that gap is the point. A sub-8% revenue CAGR paired with EBITDA compounding above 40% indicates that margin structure — not demand acceleration — has been doing the heavy lifting. This pattern typically reflects a combination of pricing discipline, product mix improvement, and operating leverage in a business where fixed engineering and manufacturing costs matter.

What stands out is that this margin expansion has occurred while Vicor remains exposed to cyclical end markets like data center infrastructure and high-performance computing. The data suggests that internal efficiency gains have outweighed external volatility. For readers tracking durability rather than headline growth, income statement line items such as gross margin progression, R&D intensity, and EBITDA consistency across downcycles are the relevant datasets to monitor going forward.

FN Fabrinet

5-Year Revenue CAGR: 14.90%
5-Year EBITDA CAGR: 19.08%

Fabrinet shows a more compact, but still meaningful, divergence between revenue and EBITDA growth. In contract manufacturing, where margins are structurally thinner and customer concentration is high, sustaining EBITDA growth faster than revenue over multiple years signals execution quality rather than pricing power alone. The nearly 15% revenue CAGR confirms volume expansion, while EBITDA compounding near 19% suggests incremental profitability on that growth.

This matters because Fabrinet operates in optical communications and advanced manufacturing niches where cost control, yield optimization, and customer mix can materially shift margins. The signal here is not explosive leverage, but consistency. To contextualize this pattern, pairing income statement data with customer concentration disclosures and capex trends would help clarify whether efficiency gains are operationally embedded or dependent on favorable demand cycles.

AU AngloGold Ashanti plc

5-Year Revenue CAGR: 14.31%
5-Year EBITDA CAGR: 30.75%

AngloGold's numbers reflect more than just a rising gold price environment. A revenue CAGR in the mid-teens alongside EBITDA growth exceeding 30% implies that cost discipline and portfolio optimization have amplified commodity tailwinds. In mining, EBITDA sensitivity is often asymmetric — once fixed costs are covered, incremental revenue can disproportionately boost operating profit.

The data suggests AngloGold has benefited from a combination of asset quality improvements and cost normalization following prior restructuring. What makes this worth monitoring is whether EBITDA expansion remains supported if pricing conditions stabilize. Alongside income statement trends, all-in sustaining cost (AISC) disclosures, production guidance, and regional asset performance data are the datasets that best frame the durability of this margin profile.

CLS Celestica Inc.

5-Year Revenue CAGR: 12.16%
5-Year EBITDA CAGR: 35.40%

Celestica's CAGR profile points to a clear operational inflection over the measured period. Revenue growth in the low teens would not typically imply EBITDA compounding north of 35% unless the business mix is shifting meaningfully. This spread indicates a transition toward higher-value programs, better contract economics, or structurally lower cost intensity within its advanced manufacturing and supply chain solutions segments.

The implication is not simply growth, but a change in earnings quality. EBITDA expansion at this pace often reflects margin normalization after a weaker base period or a strategic repositioning. To evaluate whether this trend is holding, readers would benefit from tracking segment-level margins, backlog disclosures, and customer vertical exposure alongside standard income statement data.

NEM Newmont Corporation

5-Year Revenue CAGR: 15.65%
5-Year EBITDA CAGR: 31.17%

Newmont's figures reinforce a familiar theme in large-scale mining: scale combined with disciplined cost control can translate moderate revenue growth into outsized EBITDA gains. A revenue CAGR above 15% paired with EBITDA compounding just over 31% suggests strong operating leverage across its asset base, particularly as capital-intensive projects mature and unit costs stabilize.

What makes Newmont notable is the consistency of this spread across a multi-year window that included commodity price volatility and portfolio adjustments. The signal here is not short-term margin expansion, but structural earnings leverage. Monitoring free cash flow conversion, sustaining capex levels, and mine-level cost disclosures — in addition to income statement trends — provides the clearest view into whether this EBITDA trajectory remains supported by underlying operations.

Reading the Signal Beneath the Surface

Viewed together, these five companies don't point to a single sector call — they point to a structural theme. Across semiconductors, contract manufacturing, and large-cap mining, EBITDA has been compounding materially faster than revenue, suggesting that operating leverage and internal efficiency have mattered more than headline demand growth. This is not a momentum screen in the traditional sense; it's a margin-resilience signal surfacing across otherwise unrelated businesses.

What ties the group together is the persistence of that spread over time. In each case, revenue growth alone would undersell the operating story. The divergence reflects cost structures that have already been tightened, product or asset mix shifts that favor profitability, or scale effects that are only now showing up in reported earnings. From a strategist's perspective, this kind of pattern tends to emerge during periods when capital quietly reallocates toward earnings durability rather than pure volume growth — often before that preference is obvious in price action.

This is also where relying on a single metric becomes limiting. Income statement data establishes the signal, but its strength depends on confirmation elsewhere. Comparing EBITDA growth against free cash flow trends — particularly through the lens of how cash generation actually works at the business level — helps separate accounting leverage from operational substance, a distinction outlined well in FMP's breakdown of cash flow mechanics and interpretation. When balance sheet data is layered on top, it becomes clearer whether margin expansion is being funded internally or accompanied by rising financial leverage.

Additional context comes from sentiment and positioning. Analyst targets and estimate revisions, viewed alongside realized margin expansion, help frame whether profitability gains are already reflected in expectations or still underappreciated. Insider activity can further sharpen that read when it aligns with operational improvement. Individually, none of these datasets are decisive. Combined — as part of a broader analytical workflow grounded in the data infrastructure available through Financial Modeling Prep — they turn a simple CAGR spread into a more complete operating narrative.

The broader takeaway is that EBITDA acceleration without aggressive revenue growth is not incidental here; it's the common thread. That observation doesn't amount to a forecast, but it does define a category of companies where efficiency has been the primary driver of results — and where future shifts in margins, rather than sales growth alone, are likely to carry the most informational weight.

How to Build a Clean CAGR Workflow Using FMP Data

A solid CAGR screen is more about discipline than complexity. The goal is to pull consistent historical data from the right endpoints and apply the same logic every time. The workflow below mirrors how many analysts approach multi-year growth checks using FMP's Income Statement data — straightforward, repeatable, and easy to scale once validated.

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 the screen is most effective when it follows the same discipline as the initial build. Start with a limited set of symbols and confirm that the CAGR logic behaves as expected. For this validation phase, the Basic plan provides enough access to the Income Statement endpoints to pressure-test the methodology without pulling unnecessary volume.

Once the results are consistent across that initial sample, expanding to the Starter tier allows the workflow to run across the full U.S. equity universe. At that scale, relative comparisons become more meaningful, and CAGR-based filters begin to surface patterns that aren't visible in smaller datasets.

When coverage needs extend further — whether into non-U.S. listings or longer historical series — the Premium plan introduces global exchanges and deeper reporting history. The key point is that the workflow itself doesn't change. The same CAGR framework simply operates over a broader and more comprehensive dataset, shifting the screen from a pilot tool into a full-market research input.

From Periodic Screens to an Ongoing Operating Read

When refreshed on a regular cadence, pulls from the FMP Income Statement API and Income Statement Bulk API shift CAGR from a backward-looking statistic into a live operating signal. Re-running the same framework over time makes margin inflections and efficiency drift easier to spot early, before they become obvious in headline narratives. At that point, the value isn't the calculation itself — it's the continuity of the read.

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