A fresh data sweep through the Financial Modeling Prep API surfaced a pattern worth isolating: in several unrelated industries, operating profits are compounding materially faster than top-line growth. When EBITDA begins to accelerate ahead of revenue across a multi-year window, it usually signals something deeper than cyclical demand — operating leverage is quietly expanding.
Using the FMP's Income Statement API, a five-year CAGR scan highlighted five companies where that spread between revenue growth and EBITDA growth has widened meaningfully. In this article, we break down the names that surfaced in the screen and walk through how the same API can be used to build a clean, repeatable CAGR workflow.
5 Companies With Strong CAGR Momentum
Boot Barn Holdings, Inc. (BOOT)
5-Year Revenue CAGR: 20.65%
5-Year EBITDA CAGR: 39.44%
Boot Barn's growth profile illustrates a classic operating-leverage expansion story. Revenue has compounded at a strong 20.65% annualized rate over the past five years, but EBITDA has grown nearly twice as fast at 39.44%, indicating that the company has been converting incremental sales into operating profit with increasing efficiency. When EBITDA growth significantly outpaces revenue over a sustained window, it often reflects improvements in merchandise mix, store productivity, or supply-chain scale rather than simple demand expansion.
The western and workwear retailer has steadily expanded its store footprint while increasing penetration of exclusive brands, which typically carry higher margins than third-party labels. That combination — physical expansion alongside margin-accretive product mix — often produces the kind of widening revenue-to-EBITDA growth spread seen in the data. The pattern suggests the underlying business model is scaling rather than merely cycling with consumer demand.
From a research perspective, the signal becomes clearer when cross-referencing income statement data with store-count trends and margin metrics over the same period. Pulling historical operating income and SG&A ratios from the Income Statement dataset can help determine whether the EBITDA acceleration is driven by structural margin improvements or simply periods of favorable demand.
Advanced Micro Devices, Inc. (AMD)
5-Year Revenue CAGR: 33.27%
5-Year EBITDA CAGR: 51.34%
Over the past five years, AMD has produced one of the strongest growth profiles in large-cap semiconductors, with revenue compounding at 33.27% annually and EBITDA rising at 51.34%. The widening spread between those figures highlights how the company's product portfolio shift — toward higher-margin data-center and AI compute products — has translated into accelerating operating profitability.
Unlike pure revenue-driven growth cycles often seen in semiconductors, this pattern reflects a structural repositioning. AMD's expansion into data-center CPUs, GPUs, and AI accelerators has gradually shifted the revenue mix toward segments with stronger pricing power and higher gross margins. When EBITDA growth materially exceeds revenue growth over multiple years in hardware businesses, it often signals that the product mix itself is moving up the value chain.
To contextualize this trend, analysts typically examine segment-level revenue and margin data from the Income Statement endpoint, alongside analyst estimate datasets that track forward expectations for data-center and AI revenue contribution. The combination of those datasets helps frame whether the observed operating leverage is tied to cyclical semiconductor demand or to a more durable shift in AMD's earnings structure.
Exelixis, Inc. (EXEL)
5-Year Revenue CAGR: 18.60%
5-Year EBITDA CAGR: 115.47%
Exelixis presents one of the most pronounced operating-leverage signals in the screen. Revenue has grown at a solid 18.60% CAGR, yet EBITDA has expanded at an extraordinary 115.47% annualized pace over the same period. Such a divergence typically occurs when a biotechnology company transitions from heavy development spending toward commercialization of an established therapy.
In Exelixis' case, the financial profile reflects the maturation of its oncology franchise. Once the core therapy reached broader market adoption and development spending stabilized, operating profitability began scaling rapidly relative to revenue growth. Biotech companies often experience this shift once early R&D investment phases taper while drug revenues continue expanding globally.
Evaluating the durability of that growth pattern generally requires combining income statement operating metrics with product-level revenue disclosures and pipeline updates from filings. Historical expense trends — particularly R&D as a percentage of revenue — provide context for whether the EBITDA surge reflects a sustainable operating phase or a temporary normalization following earlier investment cycles.
Voya Financial, Inc. (VOYA)
5-Year Revenue CAGR: 6.35%
5-Year EBITDA CAGR: 81.14%
Voya's numbers stand out because the acceleration in EBITDA growth occurs despite relatively modest top-line expansion. Revenue has compounded at 6.35% annually, while EBITDA has climbed 81.14% over the same five-year period. In financial services, that type of spread often reflects structural balance-sheet repositioning, expense discipline, or a shift toward higher-margin business lines.
Over the past several years, Voya has reshaped its portfolio through divestitures and a deeper focus on retirement, investment management, and workplace benefits. When financial firms streamline operations and concentrate on fee-generating segments, operating margins can expand meaningfully even without rapid revenue growth. The widening gap between revenue and EBITDA growth therefore suggests efficiency improvements and business mix optimization rather than aggressive expansion.
To analyze the trend further, researchers often compare income statement operating expenses and segment profitability alongside capital allocation data such as share repurchases or divestiture proceeds. Tracking those figures across multiple fiscal years can clarify whether the EBITDA acceleration is primarily cost-driven or tied to structural changes in Voya's revenue mix.
DRDGOLD Limited (DRD)
5-Year Revenue CAGR: 14.63%
5-Year EBITDA CAGR: 31.60%
DRDGOLD's financial trajectory highlights how operational efficiency in commodity businesses can materially amplify profitability. Over the past five years, revenue has grown at a 14.63% CAGR, while EBITDA has expanded at 31.60%, suggesting that the company has captured meaningful operating leverage during a period of fluctuating gold prices.
Unlike traditional mining companies that rely on new resource development, DRDGOLD focuses heavily on surface tailings retreatment — extracting gold from previously mined material. This approach often carries lower exploration risk and can deliver more predictable cost structures. When commodity prices remain supportive, incremental production from these operations tends to flow disproportionately into operating earnings.
For deeper analysis, combining income statement margin trends with production and cost data from company disclosures can clarify how much of the EBITDA growth is driven by gold price dynamics versus operational efficiencies. Observing those metrics across commodity cycles helps determine whether the widening revenue-to-EBITDA growth spread reflects temporary price support or sustained cost advantages within DRDGOLD's operating model.
Reading the Signal Beneath the Surface
Viewed together, the five companies highlighted above span very different industries — specialty retail, semiconductors, biotechnology, financial services, and gold mining. Yet the common thread is not sector exposure; it is the widening gap between revenue growth and EBITDA growth over a sustained multi-year window. When operating profit compounds materially faster than top-line sales, it typically reflects some form of structural operating leverage — improvements in product mix, cost discipline, scale efficiency, or capital structure that allow incremental revenue to convert into disproportionately higher earnings.
What makes this signal particularly useful is that it filters out much of the noise that accompanies short-term earnings volatility. Quarterly results often fluctuate with inventory cycles, commodity prices, or one-time charges. A five-year CAGR spread, however, smooths those fluctuations and isolates whether the core earnings engine of the business is becoming more efficient over time. In practical terms, it raises a simple but important analytical question: is the company merely growing, or is its economic model improving as it scales?
That question becomes more informative when the income-statement signal is layered with additional datasets. For example, once EBITDA acceleration is identified using the Income Statement API or Income Statement Bulk API, the next step is often to examine how the market is interpreting that change. Comparing the growth signal with analyst price targets or consensus estimates from FMP's Analyst Estimates dataset can reveal whether operating leverage is already reflected in expectations or still underappreciated in forecasts. Similarly, cross-referencing with cash-flow statement data can help determine whether the EBITDA expansion is translating into actual free cash flow generation or remaining largely accounting-driven.
Ownership behavior can add another layer of context. Pulling insider trading data through FMP's Insider Trades endpoint sometimes highlights whether management teams are increasing or reducing exposure during periods of improving operating metrics. Meanwhile, pairing income-statement trends with balance sheet data allows researchers to assess whether leverage, capital allocation, or share repurchases are amplifying the effect of underlying earnings growth.
Taken together, the objective is not to treat EBITDA acceleration as a standalone signal but as the first flag in a broader analytical workflow. When revenue growth, operating leverage, analyst expectations, and capital allocation data are evaluated side by side, the pattern begins to reveal whether the improvement is structural, cyclical, or simply statistical noise. The five companies above surfaced through a simple CAGR screen — but the deeper insight emerges when that signal is placed within a larger dataset-driven research framework.
How to Build a Clean CAGR Workflow Using FMP Data
Building a useful CAGR screen is less about the formula itself and more about maintaining discipline in the underlying dataset. The calculation is straightforward; what determines whether the result is meaningful is data consistency. Every company needs to be evaluated using the same reporting periods, identical financial line items, and the same time horizon. Once those inputs are standardized, growth rates become comparable across industries, capital structures, and business models. The workflow below shows how to structure that process using FMP's Income Statement data — starting with a single company and then scaling the exact same logic across a broader universe.
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
A screening model only scales effectively if its internal rules remain unchanged. The calculation method, the financial line items being measured, and the time horizon used for the CAGR should stay constant as the universe grows. What expands is simply the number of companies flowing through the filter. For that reason, the process usually begins with a small testing environment. At this stage, the Basic plan provides enough access to the Income Statement endpoints to verify that fiscal-year alignment, missing fields, and reporting variations are handled correctly before increasing the dataset.
Once the workflow is validated, the next step is less about methodology and more about data coverage. Moving to the Starter plan allows the same screening logic to run across a larger portion of the U.S. equity market. The advantage is not simply scale — it's context. With a broader dataset, the analyst can see whether a widening spread between revenue CAGR and EBITDA CAGR is truly unusual or simply common within certain sectors or market-cap ranges.
If the goal is to extend the screen further — across international markets or deeper historical datasets — the Premium plan expands both geographic reach and time coverage without requiring any changes to the model itself. The formula, filters, and logic remain exactly the same; only the input universe becomes larger. That consistency is what ultimately turns a one-off screen into a repeatable research tool — built once, tested carefully, and then applied at scale while preserving analytical integrity.
From Periodic Screens to an Ongoing Operating Read
Once the workflow is in place, the screen becomes less of a one-time exercise and more of a standing diagnostic. Re-running the dataset through the Income Statement API and Income Statement Bulk API feed periodically allows changes in revenue-to-EBITDA growth spreads to surface early, turning a static snapshot into a rolling view of how operating leverage is evolving across the market.
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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.
