This week's multi-year growth scan surfaced five companies where EBITDA has compounded materially faster than revenue — a signal that operating leverage is quietly accelerating beneath otherwise familiar top-line trends. In a tape increasingly sensitive to margin durability and capital efficiency, that divergence matters.

Using the FMP's Income Statement API, we isolated names where five-year revenue CAGR is solid — but five-year EBITDA CAGR is meaningfully stronger. The result is a small cluster of companies where cost structure, pricing power, or mix shift is translating into real earnings torque.

Below, we break down the five names that met the screen and walk through the exact workflow behind the data pull — including how to structure a clean CAGR framework directly from the API.

5 Companies With Strong CAGR Momentum

HF Sinclair Corporation (DINO)

5-Year Revenue CAGR: 21.70%
5-Year EBITDA CAGR: 78.87%

HF Sinclair's five-year spread between revenue growth (21.70%) and EBITDA growth (78.87%) is not incremental — it's structural. That magnitude of EBITDA acceleration relative to top-line expansion typically reflects operating leverage amplified by refining margin cycles, asset optimization, and integration effects from portfolio transactions. In refining, revenue can swing with commodity prices, but EBITDA outperformance signals margin capture rather than just volume expansion.

Over the past several years, the company's acquisition of Sinclair Oil and integration of renewable diesel capacity materially altered its earnings mix. Refiners experienced unusually strong crack spreads in 2022-2023 amid global supply dislocations, and those dynamics fed directly into EBITDA expansion across the sector. The relevant dataset to monitor here is the income statement (EBITDA and operating income trends), paired with segment-level disclosures in SEC filings to determine how much of the compounding came from cyclical margin expansion versus structural cost repositioning. The scale of the CAGR divergence suggests a period of exceptional margin conditions embedded in the five-year window — an important context variable when evaluating durability.

Newmont Corporation (NEM)

5-Year Revenue CAGR: 15.73%
5-Year EBITDA CAGR: 32.97%

Newmont's revenue CAGR of 15.73% alongside a 32.97% EBITDA CAGR reflects a miner that has expanded operating earnings at roughly double the pace of top-line growth. In gold producers, that kind of spread usually indicates a combination of realized price strength, disciplined capital allocation, and cost controls relative to production growth.

The company's recent acquisition of Newcrest materially expanded its reserve base and production profile, reshaping scale and geographic exposure. At the same time, gold prices have remained elevated relative to pre-2020 averages, supporting cash generation. To assess signal quality, the income statement endpoint provides EBITDA progression, while production cost metrics (AISC — all-in sustaining costs) from filings and earnings releases clarify whether the margin improvement is price-driven or efficiency-driven. The multi-year compounding suggests the business captured favorable commodity pricing while maintaining cost discipline — a pattern that historically requires ongoing monitoring of cost inflation and capital expenditure trends to validate continuity.

Broadcom Inc. (AVGO)

5-Year Revenue CAGR: 20.54%
5-Year EBITDA CAGR: 25.71%

Broadcom's five-year revenue CAGR of 20.54% and EBITDA CAGR of 25.71% represent steady operating leverage in a large-cap semiconductor platform. The differential is narrower than commodity-linked names, but in a company of this scale, a five-percentage-point spread sustained over multiple years indicates consistent margin discipline rather than episodic windfalls.

Recent developments — including the integration of VMware and continued demand tied to AI infrastructure and custom silicon — have reshaped the earnings mix toward higher-margin software and infrastructure segments. Income statement data illustrates EBITDA expansion, while segment reporting and cash flow statements clarify the contribution from recurring software revenue versus cyclical semiconductor demand. The compounding profile reflects a company that has layered acquisition-driven scale onto core chip franchises, with EBITDA growth slightly outpacing revenue in a way that signals incremental pricing power and mix improvement rather than volatility-driven swings.

Fabrinet (FN)

5-Year Revenue CAGR: 15.47%
5-Year EBITDA CAGR: 19.71%

Fabrinet's five-year revenue CAGR of 15.47% compared to EBITDA CAGR of 19.71% highlights moderate but consistent operating leverage in a contract manufacturing context. In electronics manufacturing services, margin expansion tends to be gradual, tied to customer concentration shifts, product complexity, and mix improvements rather than headline pricing moves.

The company has benefited from exposure to optical networking, data communications, and industrial laser markets — areas that experienced cyclical demand fluctuations but also structural bandwidth growth. Income statement analysis reveals EBITDA steadily expanding relative to revenue, suggesting incremental efficiency gains or improved product mix. To deepen the picture, reviewing customer concentration data and capital expenditure trends in filings helps determine whether leverage stems from scale efficiencies or from higher-value production programs. The CAGR spread here signals operational refinement rather than a commodity-driven earnings spike.

Viasat, Inc. (VSAT)

5-Year Revenue CAGR: 20.49%
5-Year EBITDA CAGR: 21.99%

Viasat's revenue CAGR of 20.49% and EBITDA CAGR of 21.99% reflect near-parallel compounding — a profile that suggests expansion primarily through scale rather than sharp margin inflection. The company's multi-year growth has been shaped by satellite launches, broadband expansion, and its acquisition of Inmarsat, which significantly broadened its global communications footprint.

In capital-intensive satellite businesses, EBITDA trends must be interpreted alongside debt levels, interest expense, and capital deployment cycles. The income statement provides EBITDA growth visibility, but the balance sheet and cash flow statements are equally critical to contextualize leverage and investment intensity. The tight alignment between revenue and EBITDA growth indicates that operating leverage exists but is not extreme; the pattern resembles infrastructure scaling rather than cost-structure transformation. Monitoring integration progress and satellite deployment cadence would help clarify whether margin structure stabilizes or shifts over subsequent reporting periods.

Reading the Signal Beneath the Surface

Across refiners, miners, semiconductors, contract manufacturers, and satellite operators, the common thread is not sector — it's operating leverage. In each case, EBITDA has compounded at least in line with — and often materially faster than — revenue over five years. That pattern cuts across commodity cycles, hardware demand swings, and infrastructure buildouts. When EBITDA growth persistently exceeds top-line expansion over a full cycle, it typically points to structural shifts in cost architecture, pricing discipline, or business mix rather than a single favorable stretch.

What's notable is how different the underlying drivers are despite producing a similar CAGR outcome. In HF Sinclair and Newmont, commodity exposure intersected with operational repositioning. In Broadcom and Fabrinet, margin expansion appears more tied to scale and product mix. Viasat's trajectory reflects infrastructure scaling where revenue and EBITDA rise in tandem. The shared signal is efficiency capture — incremental margin embedded within the model rather than surface-level growth.

Testing whether that efficiency is cyclical or durable requires layering datasets. Income statement trends establish the compounding profile, but cash flow conversion, balance sheet leverage, and forward analyst revisions provide the necessary context. When EBITDA expansion is matched by improving operating cash flow and stable capital intensity, the signal strengthens; when it is not, the divergence warrants scrutiny. Using structured financial datasets available through platforms such as Financial Modeling Prep allows those cross-checks to be performed systematically.

How to Build a Clean CAGR Workflow Using FMP Data

A dependable CAGR screen starts with input control, not mathematical complexity. The formula is standard; the edge comes from using consistent reporting periods, identical line items, and a uniform time horizon across every company tested. When those variables are locked in, the growth rates become comparable and repeatable. 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

A CAGR screen shouldn't be a one-off exercise — it works best as a recurring diagnostic layered into the research process. With consistent pulls from the Financial Modeling Prep Income Statement API and Income Statement Bulk API, the same framework can transition from a periodic scan into a standing read on operating leverage across the market. Over time, the compounding patterns themselves become part of the signal.

If you found this useful, you might also like: Weekly Signals Desk | 5 Notable Valuation Disconnects via the FMP API (Feb 2-6)

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.