This week's data scan flagged an unusual but consistent pattern across five otherwise unrelated companies: EBITDA is compounding materially faster than top-line revenue over a full five-year window. That kind of divergence rarely shows up by accident — it tends to emerge when operating leverage, pricing power, or cost structure improvements are taking hold beneath the surface.

The signal came from a standardized pull of multi-year financials using the FMP's Income Statement API, which allows the same growth math to be applied cleanly across sectors, market caps, and reporting histories. In this piece, we break down what surfaced in that scan, why the EBITDA-over-revenue spread matters right now, and how the same FMP Income Statement API workflow can be used to systematically identify similar operating momentum as it develops — not after it's already consensus.

5 Companies With Strong CAGR Momentum

ANET Arista Networks, Inc.

5-Year Revenue CAGR: 27.87%
5-Year EBITDA CAGR: 34.92%

Arista's five-year profile shows EBITDA growing materially faster than revenue — a classic operating-leverage signal in networking hardware/software. With revenue compounding near ~28% annually while EBITDA is approaching ~35% CAGR, this suggests margin expansion driven by either product mix shifts (higher-margin software/recurring revenue) or fixed cost absorption as scale increases.

In recent quarters, Arista's top-line beats relative to consensus — including a modest revenue beat and EPS above estimates — indicate execution against enterprise and cloud provider demand. This aligns with sustained demand for high-bandwidth switching and routing platforms underpinning data center traffic growth. Monitoring changes in the mix of software subscription/licensing revenue versus pure hardware sales via the income statement and revenue segmentation endpoints would make the narrative around margin expansion more precise.

The EBITDA-vs-revenue spread matters because it often presages structural improvements in profitability that aren't visible from revenue alone. In Arista's case, the divergence reflects both improving cost efficiencies and, increasingly, higher-value software attached to its switching portfolio — a nuance that only detailed operating expense trends and gross margin data can quantify.

VICR Vicor Corporation

5-Year Revenue CAGR: 8.38%
5-Year EBITDA CAGR: 48.52%

Vicor's growth profile is notable not for top-line acceleration, but for a disproportionately high EBITDA CAGR versus revenue — implying that each incremental dollar of sales is yielding much larger increments of EBITDA. An EBITDA CAGR near ~49% against sub-10% revenue growth suggests steep margin expansion, likely from pricing, product mix, and non-linear cost absorption. This pattern is consistent with a business scaling critical components where fixed R&D and manufacturing costs are spread over a growing base of high-margin products.

Public filings and third-quarter results show sequential net profit and revenue growth after prior losses, reflecting improved operational execution and licensing income streams. The signal here isn't simply that EBITDA is rising — it's that this company has crossed a structural break where operational efficiency and IP monetization are manifesting materially in results, rather than being a byproduct of cyclical revenue swings.

To dissect this further, analysts would benefit from gross margin breakdowns, licensing revenue segments, and backlog or book-to-bill data. Those endpoints clarify whether the EBITDA lift is sustainable or if it's a function of one-off settlements/orders — essential context when revenue growth is not accelerating at a comparable pace.

GFI Gold Fields Limited

5-Year Revenue CAGR: 13.94%
5-Year EBITDA CAGR: 20.17%

Gold Fields shows a more traditional mining growth profile: EBITDA outpacing revenue, but both growing in the low-to-mid-teens range annually. The differential suggests that operational efficiencies or commodity price realizations have amplified profitability faster than sales growth. In gold mining, EBITDA is often sensitive to realized commodity prices and production volumes; a rising gold price or improved cost management can inflate EBITDA without a proportionate revenue jump.

Recent market data highlight gold reaching multi-year highs, lifting mining equities broadly and underpinning profit growth in H1 2025. The interim dividend increase amid tripling profits underscores how commodity and production dynamics interlink with the compound trend captured in your CAGR analysis.

For readers, the signal here is that resource exposure plus operational execution can lift profitability independently of pure revenue acceleration. Supplementing this with production volume trends, realized commodity prices, and cost per ounce metrics would better anchor the interpretation, especially in a sector where cycle and input price dynamics matter as much as scale.

TALO Talos Energy Inc.

5-Year Revenue CAGR: 26.47%
5-Year EBITDA CAGR: 100.36%

Talos Energy's data stand out: EBITDA compounding at ~100% annualized over five years versus ~26% revenue growth. That magnitude of differential is often seen in businesses transitioning from capital-intensive build phases to cash generation, where fixed costs have been largely absorbed and incremental production or contract gains flow directly to EBITDA.

In the context of upstream energy companies, this pattern can align with asset ramp-ups, higher realised commodity prices, or favorable hedge positions that sharply boost operating profit margins. While I didn't find specific near-term press in the recent news crawl, the EBITDA trend reflected here suggests a structural shift toward more profitable operations, potentially driven by new field developments or broader oil/natural gas price environments.

In interpreting this signal, endpoints such as realised price per barrel, lifting costs, and field production volumes would add clarity on the mechanics driving the EBITDA uplift versus revenue. Those data help distinguish whether the CAGR momentum reflects sustainable operating leverage or temporary price environments.

MRK Merck & Co., Inc.

5-Year Revenue CAGR: 10.44%
5-Year EBITDA CAGR: 14.02%

Merck's five-year profile — with EBITDA growth moderately outpacing revenue — suggests a steadier, more diversified margin expansion compared with the more extreme differentials above. In large pharmaceuticals, EBITDA gains often reflect lifecycle management of major products, controlled R&D expenses, and successful commercialization of new drugs that contribute higher profitability.

Merck's pipeline and recent regulatory developments (outside this immediate search) are commonly tracked via clinical trial and product launch news, while financials benefit from segmental reporting — for example, performance in immunology and oncology franchises. In this backdrop, the CAGR spread indicates incremental operating efficiencies and portfolio evolution.

To deepen this story, analysts would look at R&D spend trends, gross/operating margin dynamics, and product-level revenue breakdowns — especially for key drugs contributing disproportionate profit. These endpoints naturalize the CAGR signals within broader product and cost architecture rather than leaving them as abstract percentages.

Reading the Signal Beneath the Surface

Across all five companies, the common thread isn't sector exposure or market cap — it's the persistent gap between revenue growth and EBITDA growth. When that spread holds over a full five-year window, it typically reflects more than cyclical tailwinds. It points to operating structures that are improving faster than demand alone would imply: cost bases flattening, pricing power firming, or capital intensity declining as scale is reached. Importantly, this pattern is appearing simultaneously in networking hardware, power semiconductors, mining, energy, and large-cap pharmaceuticals — a reminder that operating leverage is usually a company-level phenomenon, not a macro one.

Stepping back, this type of signal tends to surface before it becomes obvious in narrative form. Revenue growth is widely tracked and quickly contextualized; EBITDA efficiency far less so, particularly when improvements accrue gradually rather than through discrete step-changes. That's where standardized, multi-year comparisons matter. When EBITDA compounds materially faster than revenue across multiple reporting cycles, the data suggests management execution is reshaping the earnings engine itself, not merely benefiting from a favorable year.

To pressure-test and deepen this interpretation, the income statement should be treated as a foundation rather than a conclusion. Pairing five-year revenue and EBITDA trends with cash flow data helps distinguish accounting leverage from operating cash generation, especially when all inputs are sourced consistently from a single financial dataset such as that maintained at Financial Modeling Prep. Adding analyst estimates provides perspective on how much of that efficiency improvement is already reflected in expectations, while historical valuation multiples help frame whether margin expansion has coincided with re-rating or accumulated quietly beneath stable pricing. Even insider transaction data, viewed alongside improving EBITDA efficiency, can offer incremental context around internal confidence without leaning on forward guidance.

Seen together, the takeaway isn't that these five companies point to a single outcome, but that they share a measurable operating behavior: profitability compounding faster than sales, consistently, over time. That pattern doesn't forecast direction on its own — but it does narrow attention toward businesses where underlying efficiency is changing in ways worth monitoring as new data prints.

How to Build a Clean CAGR Workflow Using FMP Data

A reliable CAGR screen isn't driven by formula complexity — it's driven by discipline around inputs. As long as each company is evaluated using the same reporting structure and time span, growth rates become consistent, comparable, and easy to refresh. The workflow below reflects how analysts typically operationalize CAGR calculations using FMP's Income Statement data, starting with a single ticker and scaling cleanly to broader coverage without modifying the core 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

CAGR screens scale best when the rigor of the initial build is carried forward intact. The early phase is deliberately narrow — a small, controlled set of symbols used to verify that the growth logic holds across different capital structures, reporting histories, and business models. At this point, the Basic plan is sufficient, giving access to the Income Statement endpoints needed to validate calculations, work through missing data, and test edge cases without introducing unnecessary scope.

Once the outputs are behaving consistently, stepping up to the Starter tier extends the same framework across a broader portion of the U.S. equity universe. The benefit isn't sheer coverage; it's comparative signal quality. A wider sample set makes relative growth patterns easier to contextualize, allowing CAGR-based filters to surface behaviors that are difficult to distinguish when working with a limited group of names.

For analysts looking beyond U.S. equities or requiring longer historical depth, the Premium plan expands coverage without altering the underlying methodology. The screening logic stays exactly the same — only the dataset grows. That continuity is the objective: a workflow that begins as a focused validation exercise and scales cleanly into a durable, market-wide research input without needing to be rebuilt at each stage.

From Periodic Screens to an Ongoing Operating Read

When refreshed on a consistent cadence, data drawn from the Financial Modeling Prep Income Statement API and Income Statement Bulk API turns CAGR from a static snapshot into a living operating read. Applying the same framework over time helps surface shifts in efficiency and margin behavior as they form, rather than after they've been absorbed into consensus. At that point, the value isn't the calculation itself — it's the continuity in how operating performance is observed and interpreted.

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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.