Signals Desk Weekly | Multi-Year CAGR Strength Taking Shape Across Five Names (Oct 5-9)

With third-quarter reports days away, the more useful question for many companies is not how fast revenue is growing but how much of each additional dollar reaches operating earnings. This week's screen surfaced five names where the answer has been consistently favorable for five years: Eli Lilly, Amphenol, Trane Technologies, Applied Materials and Linde. Their revenue growth rates range from mid-single digits to above 20%, yet in every case EBITDA has compounded faster, and the reasons differ more than the numbers suggest.

The analysis uses FMP's Income Statement API to compare five-year revenue and EBITDA growth on a strict window, measuring each company's latest fiscal year against the year five years earlier. The article covers the five names, what the spread between the two growth rates reveals, and how to build the same profitability screen with the API.

Key Takeaways

• Lilly and Amphenol pair revenue growth above 20% with even faster EBITDA growth, showing operating leverage at scale rather than margin repair on a shrinking base.
• Trane, Applied Materials and Linde demonstrate that a meaningful spread can come from pricing, mix and productivity even when revenue grows more modestly.
• Each base year passed a quality check against the year before it, so none of the spreads rests on a pandemic trough or a charge-depressed starting point.
• Cash conversion and capital intensity are the necessary next test, since EBITDA growth reads very differently at a capital-heavy gas producer than at an asset-light drugmaker.

Five Businesses Turning Growth Into Wider Margins

Eli Lilly and Company (LLY)

5-Year Revenue CAGR: 21.58%
5-Year EBITDA CAGR: 28.88%

Lilly more than tripled its EBITDA between 2020 and 2025 while revenue grew a little over two and a half times, lifting its EBITDA margin by more than twelve percentage points. The driver is well known, but the scale of the operating leverage is easy to understate: the incretin franchise built around Mounjaro and Zepbound has added revenue far faster than the cost base required to support it, even as the company invested heavily in new manufacturing capacity.

Recent results show the trend has not plateaued. Second-quarter 2026 revenue rose 48%, with Mounjaro growing more than 90% worldwide, and management raised full-year guidance. The spread is notable because it held despite acquired in-process R&D charges that reduce reported earnings, including a sizable charge in the latest quarter. The concentration risk is the flip side: a growing share of earnings depends on one drug class, so pricing pressure or competition in obesity treatment would feed through to margins quickly.

FMP's Revenue Product Segmentation API shows how concentrated revenue has become in the incretin products, which is the clearest way to judge how exposed the margin profile is to a single category. The Financial Estimates API then reveals whether consensus expects margins to keep expanding as oral therapies such as orforglipron move through regulatory review.

Amphenol Corporation (APH)

5-Year Revenue CAGR: 21.85%
5-Year EBITDA CAGR: 29.02%

Amphenol has the fastest revenue growth in the screen and one of its largest margin gains, with EBITDA margin rising from roughly 23% to about 30% over the window. Much of the recent acceleration traces to IT datacom, where demand for high-speed interconnect and power products tied to AI data centers has grown far faster than the rest of the portfolio. Second-quarter 2026 sales rose 55% on record orders, with a book-to-bill ratio above 1.2.

Acquisitions are part of the story and need to be separated from organic leverage. Amphenol has been an active acquirer throughout the window, and the purchase of CommScope's connectivity and cable business closed in January 2026, just after the measured period, so it will reshape next year's comparison. The fact that margins expanded while the company was integrating acquired businesses suggests the leverage is operational, but the organic contribution is the cleaner measure.

FMP's Search Mergers & Acquisitions API catalogs the company's deal history, making it possible to estimate how much revenue was purchased rather than grown, and the Income Statement Growth API shows whether operating income growth has outpaced revenue in the years with fewer acquisitions, which is the most direct test of organic leverage.

Trane Technologies plc (TT)

5-Year Revenue CAGR: 11.35%
5-Year EBITDA CAGR: 18.12%

Trane's spread of nearly seven percentage points comes from a business growing at a low double-digit rate, which makes the margin story the more important one. EBITDA margin expanded by about five percentage points over the window, reflecting pricing discipline, a growing services business and a commercial HVAC portfolio weighted toward energy efficiency, where customers pay for performance rather than simply replacing equipment.

Data center cooling has become an increasingly important source of that mix improvement. The company raised its 2026 outlook in July on strong bookings and an expanding data center backlog, although the stock declined after the report as investors weighed residential softness and the cost of new capacity. That tension frames the near-term question: whether commercial and data center strength continues to offset weaker residential demand without diluting margins.

The Key Metrics TTM API puts Trane's return on invested capital alongside the margin trend, which matters as the company commits capital to expand production, and the Cash Flow Statement API shows whether the higher EBITDA is converting into free cash flow at the same rate as capital spending rises.

Applied Materials, Inc. (AMAT)

5-Year Revenue CAGR: 10.52%
5-Year EBITDA CAGR: 15.87%

Applied Materials grew revenue at about 10.5% a year through fiscal 2025, while EBITDA compounded more than five points faster, lifting margins from roughly 28% to 35%. For a semiconductor equipment maker, that spread reflects a shift toward leading-edge logic, advanced packaging and high-bandwidth memory tools, where technical complexity supports higher pricing, along with a services business that adds recurring revenue to an otherwise cyclical model.

The most recent quarter, fiscal third quarter 2026, delivered record revenue and a raised outlook on AI-related demand, yet the shares fell as investors focused on China. Export controls have weighed on sales to Chinese customers, which had accounted for a large share of revenue during the window, so part of the historical margin gain was earned in a market that is now partially restricted. That makes geographic mix the main variable for whether the spread persists.

FMP's Revenue Geographic Segments API shows how much revenue still comes from China and how fast that share is shifting, and the Earnings Transcript API captures management's commentary on export-control effects that the financial statements alone will not quantify until later quarters.

Linde plc (LIN)

5-Year Revenue CAGR: 4.52%
5-Year EBITDA CAGR: 8.86%

Linde has the slowest revenue growth in the group, yet EBITDA has compounded at nearly twice the pace, raising its margin by about seven percentage points. In industrial gases, that kind of leverage typically comes from pricing power under long-term contracts, productivity programs and disciplined project selection, rather than from volume. The result is a margin profile that has improved steadily regardless of the industrial cycle.

The latest quarter showed that the path is not perfectly smooth. Second-quarter 2026 revenue and EPS beat expectations, but EBITDA margin slipped year over year, which management attributed mainly to its U.S. homecare business, now under strategic evaluation. At the same time, the sale-of-gas project backlog reached a record, including new electronics work tied to semiconductor fabs, which supports future contracted volume.

Because industrial gas production is capital intensive, EBITDA growth needs to be read alongside reinvestment. FMP's Owner Earnings API adjusts cash flow for maintenance capital spending, and the Financial Ratios API tracks return on capital over time, together showing whether Linde's margin gains are translating into higher returns on the plants it builds.

What the Spread Is Really Measuring

The five names arrive at the same result through two distinct routes. Lilly and Amphenol are scaling businesses where revenue is growing so quickly that fixed costs are spread across a much larger base, producing operating leverage almost mechanically. Trane, Applied Materials and Linde are growing more slowly, and their spreads come from pricing, mix and productivity, a form of leverage that depends more on competitive position than on volume.

The distinction matters because the two routes carry different risks. Volume-driven leverage can reverse when growth slows, as it did for many hardware businesses after past demand peaks. Pricing and mix leverage tends to be more durable but is exposed to policy and competition, whether export controls at Applied Materials or homecare reimbursement at Linde. Reading the spread without knowing which route produced it gives an incomplete picture.

Capital intensity is the second filter. EBITDA ignores the capital required to generate it, and these five companies sit at very different points on that spectrum. Pairing the Income Statement Bulk API with FMP's Cash Flow Statement API shows how much of the EBITDA gain survives capital expenditure, while the Key Metrics TTM API puts free cash flow yield and returns on capital on a comparable basis. Taken together through the FMP platform, those datasets turn a single growth comparison into a view of how efficiently each business converts growth into cash, which is ultimately what sustains a margin trend.

The practical takeaway is that a positive spread is a starting point. The upcoming reporting season will show whether Lilly's leverage holds as the incretin category matures, whether Amphenol's organic margin survives its largest acquisition, and whether the pricing-led gains at Trane, Applied Materials and Linde can withstand residential softness, export controls and segment pressure respectively.

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.

Turning a One-Off Screen Into an Ongoing Read

Five-year spreads change slowly, but each new annual filing shifts the window by a year and can confirm or erode the pattern. Rerunning the comparison with FMP's Income Statement API and Income Statement Bulk API as those filings arrive keeps the read on operating leverage current rather than historical.

If you found this useful, you might also like: Weekly Signals Desk | Concentrated Analyst Revisions via the FMP API (Sept 28-Oct 2)

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.