This week's data scan surfaced a subtle but repeatable signal: in several corners of the market, EBITDA growth is compounding meaningfully faster than top-line revenue. That divergence often shows up during periods of rotation and margin re-rating—when capital starts rewarding operating leverage before narratives catch up. Using the FMP's Income Statement API, this note isolates five companies where multi-year CAGR trends point to underlying efficiency gains rather than headline growth alone.

The analysis below walks through what the screen is showing, why this pattern matters in the current environment, and how the same FMP Income Statement API workflow can be replicated and scaled to monitor similar signals across the market.

5 Companies With Strong CAGR Momentum

Broadcom Inc. (AVGO)

5-Year Revenue CAGR: 20.11%
5-Year EBITDA CAGR: 25.04%

Broadcom's multi-year growth profile reflects more than cyclical semiconductor demand. The spread between revenue and EBITDA CAGR highlights sustained operating leverage across a period that included multiple end-market resets. This pattern is consistent with a business that has steadily shifted its mix toward higher-margin software and infrastructure assets, where incremental revenue carries disproportionate profitability.

What stands out in the data is not just the level of growth, but its consistency. EBITDA compounding ahead of revenue over a full five-year window suggests cost discipline and pricing power held through varying demand environments. For readers tracking this signal, the income statement dataset is the primary anchor, but pairing it with segment-level disclosures and acquisition-related cash flow data can help clarify how much of the margin expansion is structural versus integration-driven.

DRDGOLD Limited (DRD)

5-Year Revenue CAGR: 15.31%
5-Year EBITDA CAGR: 38.22%

DRDGOLD shows one of the widest gaps between revenue and EBITDA growth in this screen. That divergence reflects the economics of a tailings-based gold producer operating with relatively fixed infrastructure and improving recovery efficiency. Over time, incremental revenue has translated into sharply higher operating profit, a pattern that tends to surface when cost per unit trends move favorably relative to realized pricing.

The signal here is less about top-line expansion and more about margin capture. EBITDA growing at more than twice the pace of revenue implies a sensitivity to operational efficiency and commodity pricing that is asymmetric on the upside. Beyond income statement data, investors monitoring this profile often look to cash cost disclosures, production volumes, and capital expenditure trends to assess whether the observed leverage remains supported by underlying operations.

Micron Technology, Inc. (MU)

5-Year Revenue CAGR: 16.95%
5-Year EBITDA CAGR: 84.68%

Micron's numbers immediately read as cyclical, but the magnitude of EBITDA compounding relative to revenue deserves closer inspection. Memory markets are volatile by design, yet a near fivefold difference between revenue and EBITDA CAGR over five years points to operating leverage amplified by disciplined capacity management and pricing cycles rather than volume growth alone.

This type of signal typically appears when a company exits downturns with a leaner cost base than prior cycles. The data suggests Micron's profitability has rebounded more aggressively than its revenue trajectory would imply. To contextualize this, analysts often complement income statement trends with inventory levels, capital spending cycles, and pricing data from industry trackers—datasets that help distinguish structural margin change from purely cyclical rebound effects.

NetEase, Inc. (NTES)

5-Year Revenue CAGR: 12.26%
5-Year EBITDA CAGR: 15.13%

NetEase presents a more compressed—but still notable—spread between revenue and EBITDA growth. In contrast to capital-intensive sectors, this profile reflects a mature digital business balancing content investment with monetization efficiency. EBITDA growing modestly faster than revenue suggests operating costs have scaled more slowly than sales despite ongoing investment in games, music, and online services.

The signal here is about stability rather than acceleration. Over a multi-year horizon that included regulatory and macro headwinds, NetEase maintained margin discipline while expanding revenue at a double-digit rate. Analysts tracking this setup often combine income statement data with segment revenue breakdowns and R&D spend to evaluate how content pipelines and user engagement translate into sustained operating leverage.

Southern Copper Corporation (SCCO)

5-Year Revenue CAGR: 11.17%
5-Year EBITDA CAGR: 18.93%

Southern Copper's CAGR profile reflects the operating dynamics of a large-scale, low-cost copper producer. EBITDA outpacing revenue over five years points to favorable cost positioning and the embedded leverage of commodity-linked earnings. Unlike pure volume growth stories, this spread highlights how stable production combined with cost control can drive profitability through price cycles.

For readers evaluating this signal, the key is understanding sensitivity. EBITDA growth materially exceeding revenue suggests margins have expanded during periods of stronger copper pricing or improved cost efficiency. Income statement data provides the foundation, but pairing it with unit cash costs, production guidance, and capital investment disclosures helps assess whether observed leverage aligns with operational fundamentals rather than transient market conditions.

Reading the Signal Beneath the Surface

Across these five companies, the common thread isn't sector, geography, or narrative momentum—it's the persistence of operating leverage showing up ahead of broader consensus recognition. In each case, EBITDA has compounded faster than revenue over a full multi-year window, suggesting that efficiency, mix, or cost structure has quietly improved while top-line growth remained visible but not exceptional. This type of divergence tends to surface when capital is gravitating toward durability and margin quality rather than headline growth alone—conditions that make clean financial baselines especially valuable.

What makes the signal more informative is its cross-industry consistency. Semiconductors, commodities, internet platforms, and precious metals rarely move in sync, yet the same financial relationship appears in all five. That reduces the likelihood of a single-cycle explanation and instead points to a broader environment where disciplined operators are separating themselves financially. Screens built on standardized datasets—such as those sourced from the Financial Modeling Prep platform — help make these cross-sector comparisons possible without relying on narrative alignment.

To sharpen that read, income statement data is only the starting layer. Pairing multi-year EBITDA and revenue trends with balance sheet and cash flow statements clarifies whether margin expansion is being supported by conservative funding and real cash generation rather than accounting effects. When those operating signals are viewed alongside analyst expectations or price targets, gaps between fundamentals and forward assumptions become observable conditions rather than interpretive claims.

At scale, combining bulk income statement pulls with complementary financial and consensus datasets turns this framework into an ongoing diagnostic rather than a one-off screen. The objective isn't prediction; it's continuity. When the same relationships persist across refresh cycles, they provide a grounded context for interpreting earnings updates, capital allocation decisions, and shifts in market attention as they unfold.

How to Build a Clean CAGR Workflow Using FMP Data

A dependable CAGR screen is less about the formula and more about process discipline. The goal is to ensure every company is evaluated using the same inputs, the same structure, and the same assumptions. When that foundation is solid, the output becomes something you can rerun, compare, and extend—rather than a one-off snapshot. Below is a straightforward way practitioners typically set this up 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 this type of screen works best when it follows the same discipline as the initial build. Start small, using a limited group of symbols to confirm that the CAGR logic behaves consistently across different reporting histories and business models. At this stage, the Basic plan is sufficient—its access to the Income Statement endpoints allows you to validate calculations and edge cases without pulling unnecessary volume.

Once the outputs look stable, moving to the Starter tier enables the same framework to run across the full U.S. equity universe. That shift matters less for raw coverage and more for context: relative comparisons become clearer, and CAGR-based filters start surfacing patterns that don't appear in narrower samples.

When the scope needs to extend further—whether into non-U.S. listings or longer historical windows—the Premium plan adds global exchange coverage and deeper reporting history. Importantly, the workflow itself doesn't change at any point. The same logic simply operates on a broader dataset, turning the screen from a controlled test 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 shifts CAGR from a static metric into a live operating reference. Running the same framework repeatedly makes margin inflections and efficiency drift easier to contextualize as they develop, rather than after they've already been absorbed into narrative. At that point, the value is less about the math and more about maintaining continuity in how operating performance is observed over time.

If you found this useful, you might also like: Weekly Signals Desk | 5 Valuation Disconnects Identified via the FMP API (Dec 22-26)

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.