Signals Desk Weekly | Multi-Year CAGR Strength Taking Shape Across Five Names (Oct 26-30)

Deere's earnings are well below their peak, with farm equipment demand in a cyclical downturn and tariffs adding to costs. Yet measured over five years, its EBITDA has still compounded at nearly twice the rate of its revenue. That contrast is a useful reminder of what a multi-year growth screen actually captures: not the current quarter, but how much more profit a business now extracts from each dollar of sales than it did at the start of the window.

This week's screen applies that lens to five large companies where five-year EBITDA growth exceeded revenue growth, using FMP's Income Statement API. The article reviews what is driving each spread and then shows how to build the same CAGR comparison into a repeatable screening workflow.

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Key Takeaways

• Microsoft, Deere, Cadence Design Systems, Apple and Cintas all grew EBITDA faster than revenue over their latest five fiscal years, with spreads ranging from about 2 to 5 percentage points.
• The sources of that leverage differ: pricing and mix at Deere, software scale at Cadence, services mix at Apple, route density at Cintas, and cloud scale at Microsoft.
• For Microsoft in particular, rising depreciation from AI infrastructure flatters EBITDA relative to operating income, so the spread needs to be read alongside capital spending.
• Pairing income statement CAGRs with cash flow, segment and estimates data helps separate durable margin gains from cyclical or accounting effects.

Where Operating Leverage Has Been Compounding

Microsoft Corporation (MSFT)

5-Year Revenue CAGR: 14.57%
5-Year EBITDA CAGR: 19.48%

Microsoft roughly doubled revenue between fiscal 2021 and fiscal 2026, while EBITDA grew substantially faster. The spread reflects the scale economics of its cloud and productivity businesses, where incremental revenue from Azure and commercial subscriptions carries high margins once the platform is built. The latest quarter showed that momentum continuing, with revenue above $92 billion and Azure still growing at a rate well above the company average.

The important nuance is the role of depreciation. Microsoft's capital spending on AI data centers has risen sharply, and the depreciation that follows is added back in EBITDA. That means EBITDA growth can look stronger than growth in operating income as the asset base expands. For this screen, EBITDA was built from operating income plus depreciation and amortization to exclude a large non-operating gain in fiscal 2026, but the depreciation effect remains part of the result.

The data suggests this may be an area to monitor through the gap between EBITDA and free cash flow. If capital spending continues to outpace depreciation, cash conversion will lag the EBITDA trend. FMP's Cash Flow Statement API shows capital expenditure against operating cash flow, which is the clearest test of whether the margin story is translating into cash.

Deere & Company (DE)

5-Year Revenue CAGR: 5.17%
5-Year EBITDA CAGR: 9.97%

Deere's five-year numbers capture an entire agricultural cycle: a strong recovery from fiscal 2020, a peak in fiscal 2023, and a sharp decline since. Despite the downturn, EBITDA in fiscal 2025 remained well above its fiscal 2020 level relative to revenue. That points to structural changes in the business, including pricing discipline, a richer mix of precision technology on new equipment, and tighter inventory management through the down cycle.

The base year matters here. Fiscal 2020 was a soft year but not distorted enough to fail the screen's quality gate, so the spread reflects genuine margin improvement rather than a depressed starting point alone. The current environment is challenging, with management pointing to a sizable tariff cost this year and dealer inventories of large tractors at multi-year lows. The question is how much of the margin gain holds at the bottom of the cycle.

Fourth-quarter results in late November will provide the next data point. FMP's Revenue Product Segmentation API separates large agriculture, small agriculture and construction, which helps identify where margin resilience is coming from, while quarterly data from the Income Statement API tracks whether margins stabilize as volumes bottom.

Cadence Design Systems, Inc. (CDNS)

5-Year Revenue CAGR: 14.57%
5-Year EBITDA CAGR: 18.43%

Cadence has grown revenue at a mid-teens pace since 2020 while expanding margins, a combination that reflects the economics of design software. Chip designers rely on its tools across every stage of development, and demand has been strengthened by the increasing complexity of AI processors and custom silicon. Much of the revenue comes from multi-year contracts, which supports visibility and allows the company to scale costs more slowly than sales.

Recent results reinforce that picture. Third-quarter results showed a record backlog and a third increase to the full-year outlook. Cadence has also expanded through acquisitions in system analysis and simulation, so part of recent revenue growth is acquired, and the margin trend should be checked against those additions.

The key variables to monitor are backlog conversion and the contribution from newer segments, including hardware emulation systems, which tend to be lumpier than software. FMP's Key Metrics TTM API tracks margin and return measures on a rolling basis, and the Financial Estimates API shows whether analysts expect margin expansion to continue at the same pace.

Apple Inc. (AAPL)

5-Year Revenue CAGR: 8.68%
5-Year EBITDA CAGR: 12.26%

Apple's spread over fiscal 2020 to fiscal 2025 is driven primarily by mix. Services, which include the App Store, cloud storage, payments and subscriptions, have grown faster than hardware and carry much higher gross margins. As services became a larger share of revenue, overall profitability rose even though total revenue growth was modest by the standards of the other names in this screen.

Hardware margins also improved over the period, helped by premium product pricing and supply chain scale. The fiscal 2026 annual figures had not yet been incorporated into the dataset at the time of the screen, so the window runs through fiscal 2025. That matters because the most recent year included tariff-related costs and ongoing regulatory scrutiny of App Store fees, both of which could affect the mix benefit going forward.

The most useful follow-up is to track services revenue as a share of the total and any changes in services margins. FMP's Revenue Product Segmentation API separates iPhone, Mac, iPad, wearables and services, which makes the mix effect visible, while the Income Statement API will show whether the spread holds once fiscal 2026 is included.

Cintas Corporation (CTAS)

5-Year Revenue CAGR: 9.62%
5-Year EBITDA CAGR: 11.96%

Cintas has steadily widened margins in its uniform rental and facility services business, where adding customers to existing delivery routes raises profit per route. Over fiscal 2021 to fiscal 2026, that density effect, combined with technology investment in route planning and inventory, allowed EBITDA to grow meaningfully faster than revenue. The first quarter of fiscal 2027 extended the trend, with revenue growth near 11% and a higher full-year outlook.

The next phase looks different. Cintas has agreed to acquire UniFirst in a cash and stock transaction expected to close by the end of the calendar year, pending regulatory approvals. UniFirst runs at noticeably lower margins, so the combination would blend a lower-margin business into the reported figures, with the planned cost synergies central to how the gap evolves. That makes the historical spread a baseline against which integration progress can be measured.

The integration period will make reported growth rates harder to compare, so organic growth and margin disclosures deserve close attention. FMP's Search Mergers & Acquisitions API records the deal terms, and the Balance Sheet Statement API tracks the increase in debt used to fund the cash component.

What the Spread Is Really Measuring

Taken together, the five companies show that EBITDA growing faster than revenue is a common outcome with very different causes. At Cadence and Microsoft, the driver is software and platform scale, where incremental revenue costs little to deliver. Apple's spread comes mainly from mix, as higher-margin services grew faster than hardware. Cintas reflects route density in a physical business, and Deere shows how pricing and product mix can lift margins in a cyclical industry even when volumes fall.

The spread is also sensitive to choices that are easy to overlook. The starting year sets the base, so a depressed or inflated base can create or erase a signal. Depreciation affects EBITDA differently from operating income, which matters most for capital-intensive businesses like Microsoft today. Acquisitions can add revenue and costs that change margins independently of organic performance, as with Cadence's recent deals and Cintas's pending UniFirst combination. A screen that does not account for these factors risks confusing accounting or timing effects with operating improvement.

The practical step is to treat the CAGR spread as the start of an analysis. Within the FMP platform, the Income Statement Bulk API supports running the comparison across a full universe, while the Cash Flow Statement API tests whether EBITDA growth is turning into free cash flow after capital spending. The Revenue Product Segmentation API identifies which business lines are responsible for the margin shift, and the Enterprise Values API shows how much of that improvement is already reflected in valuation multiples.

Expectations complete the picture. Comparing historical spreads with forecasts from the Financial Estimates API indicates whether analysts assume the trend continues, fades or reverses. Where historical leverage, cash conversion and forward estimates align, the signal is more robust; where they diverge, as may be the case for Deere in a downturn or Microsoft during a period of heavy investment, the spread is better treated as a question than a conclusion.

Building a Consistent CAGR Screening Framework

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.

Scaling the Framework Without Changing the Methodology

The strength of this type of screen comes from consistency, not complexity. Once the formula, reporting periods, and financial line items are defined, the objective is to keep the methodology fixed while gradually widening the universe being tested. Expanding coverage should not require changing the framework itself — only the number of companies moving through it.

That's why the workflow is easiest to validate in a smaller environment first. Within the Basic plan, the Income Statement endpoints provide enough historical coverage to align reporting periods properly, normalize the selected metrics, and verify that the CAGR calculations are producing comparable outputs across companies. At this stage, the emphasis is less about scale and more about making sure inconsistencies in filings or missing data are not distorting the screen.

From there, expanding into the Starter plan simply broadens the sample size. The screening logic remains identical, but the larger universe makes sector-level comparisons more useful. Patterns that initially appear company-specific can then be evaluated against peers, industries, or market-cap cohorts to determine whether the operating leverage signal is isolated or part of a broader trend developing within a segment of the market.

The Premium plan extends that same structure further by increasing historical depth and geographic coverage. The underlying process still does not change. What changes is the scope of observation — allowing the same framework to be applied across wider datasets without introducing new assumptions or altering the screening criteria midstream. That continuity is what makes the process repeatable over time rather than dependent on one-off observations or isolated market conditions.

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Operating Leverage as a Multi-Year Signal

Five years is long enough to see whether margin gains survive a cycle, and this week's group shows how differently that leverage can be earned. Running the comparison through FMP's Income Statement API and Income Statement Bulk API keeps the method fixed, so the next update shows whether those spreads are widening or starting to narrow.

If you found this useful, you might also like: Weekly Signals Desk | Five Dividend Increases Flagged by the FMP API (Oct 19-23)

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.