This week's scan of the FMP's Income Statement API surfaced a familiar but quietly strengthening pattern: operating leverage showing up ahead of headline growth. Across five otherwise unrelated companies, EBITDA is compounding meaningfully faster than revenue — a signal that cost structures, pricing power, or mix improvements are doing more of the work than top-line expansion alone.

Using the Income Statement endpoint from Financial Modeling Prep, this note breaks down how that signal emerged, why it's appearing across multiple sectors at once, and what it suggests about where efficiency — not just growth — is starting to matter again in the current market tape.

5 Companies With Strong CAGR Momentum

B Barrick Mining Corporation

5-Year Revenue CAGR: 5.90%
5-Year EBITDA CAGR: 9.51%

Barrick's multi-year spread between revenue and EBITDA growth points to a steady improvement in operating leverage rather than cyclical revenue spikes. The divergence suggests cost discipline and portfolio rationalization have mattered as much as commodity pricing, particularly as the company has prioritized Tier One assets and balance sheet resilience. In recent quarters, margin stability has held despite input cost volatility — a signal that operating efficiency, not just gold price exposure, is doing meaningful work. Income statement trends remain the cleanest lens here, especially when paired with sustaining capital and unit cost disclosures to assess how durable the margin expansion really is.

MRK Merck & Co.

5-Year Revenue CAGR: 10.47%
5-Year EBITDA CAGR: 13.01%

Merck's numbers reflect a familiar pharmaceutical dynamic: revenue growth driven by a small number of high-impact therapies translating into disproportionately strong operating leverage. The EBITDA outperformance relative to revenue aligns with sustained margin expansion tied to mix, scale, and R&D efficiency rather than one-off events. With oncology and vaccine franchises continuing to anchor results, the income statement shows how operating leverage has compounded alongside top-line growth. Tracking segment-level revenue concentration and margin evolution over time offers a clearer read on how durable this efficiency trend remains.

DY Dycom Industries

5-Year Revenue CAGR: 8.20%
5-Year EBITDA CAGR: 16.35%

Dycom stands out for the magnitude of its EBITDA acceleration relative to revenue — one of the widest gaps in this screen. That divergence reflects operating leverage inherent in project-based infrastructure work, particularly as volumes scale against a largely fixed cost base. The data suggest improved execution and cost absorption rather than purely cyclical tailwinds. Watching backlog trends, margin progression, and cash flow conversion alongside income statement data provides useful context for whether these efficiency gains are structural or cycle-dependent.

DIS The Walt Disney Company

5-Year Revenue CAGR: 6.12%
5-Year EBITDA CAGR: 13.56%

Disney's figures highlight a long-running margin recovery story masked by volatile segment performance. While headline revenue growth has been moderate, EBITDA expansion points to cost restructuring, pricing power within parks, and a gradual normalization of streaming economics. The widening gap between revenue and EBITDA underscores how internal efficiency initiatives can materially influence consolidated performance even during periods of strategic transition. Segment-level income data and trend analysis across media, parks, and direct-to-consumer operations help clarify where that leverage is actually being generated.

RIG Transocean Ltd.

5-Year Revenue CAGR: 4%
5-Year EBITDA CAGR: 7.92%

Transocean's numbers reflect a capital-intensive business emerging from a prolonged downturn with improving utilization and contract economics. Revenue growth remains measured, but EBITDA expansion indicates better fleet utilization and operating leverage as offshore activity stabilizes. The divergence between the two metrics underscores how incremental contract wins can disproportionately impact profitability in high fixed-cost environments. Monitoring backlog quality, day rates, and cost structure through income statement data provides a clearer view of whether this operational improvement is becoming structurally embedded.

Reading the Signal Beneath the Surface

Across the five companies, a consistent pattern emerges: operating performance is improving in ways that aren't immediately visible through headline growth alone. The common thread isn't sector exposure or cyclical timing, but the quiet expansion of operating leverage — where EBITDA is pulling ahead of revenue as cost structures, asset utilization, and pricing discipline improve beneath the surface.

That signal becomes clearer when income statement trends are viewed alongside adjacent data. Using multi-year EBITDA series from the Financial Modeling Prep platform, and pairing them with balance sheet and cash flow context, helps distinguish whether margin expansion reflects genuine operational improvement or temporary accounting effects. In several cases here, cash generation has tracked EBITDA more closely than revenue, reinforcing the idea that efficiency gains are structural rather than transient.

Looking across the group, the takeaway isn't tied to any single catalyst. Instead, it's the recurring pattern that stands out: businesses quietly strengthening their economic engines in ways that aren't always captured by surface-level growth metrics. For analysts, these kinds of divergences are less about prediction and more about orientation — signals that warrant closer inspection before they show up in broader narratives.

How to Build a Clean CAGR Workflow Using FMP Data

A reliable CAGR screen isn't about clever math — it's about consistency. The objective is to pull the same data, in the same format, across symbols, and apply identical logic every time. When done correctly, the process becomes repeatable and scalable rather than a one-off calculation. Below is a practical way analysts typically structure that workflow using 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 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.

If you found this useful, you might also like: Signals Desk Hot Take for the Week | 5 Companies with Long Earnings Beat Streaks, Powered by FMP API (Dec 15-19)

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.