This week's screen surfaced five companies where EBITDA growth has materially outpaced revenue growth over the past five years: Monolithic Power Systems, Amphenol, Trane Technologies, Motorola Solutions, and Analog Devices. The common signal is not simply expansion, but improving earnings power beneath the headline growth rate.
Using FMP's Income Statement API, this article examines the revenue and EBITDA CAGR spread across all five names. It also breaks down how to use the API to build the same screening framework, validate the historical inputs, and scale the analysis across a broader company universe.
Key Takeaways
- All five companies posted five-year EBITDA growth above revenue growth, signaling stronger operating leverage beneath the headline sales figures.
- Monolithic Power Systems and Amphenol showed the widest CAGR spreads, while Motorola Solutions displayed a more gradual profitability improvement.
- The same signal reflects different operating drivers across the group, including product mix, software revenue, backlog conversion, pricing, and semiconductor utilization.
- Income statement data becomes more useful when tested against cash flow, balance-sheet trends, analyst estimates, and valuation expectations.
Five Companies Showing a Clear Profitability Inflection
Monolithic Power Systems, Inc. (MPWR)
5-Year Revenue CAGR: 29.09%
5-Year EBITDA CAGR: 39.75%
Monolithic Power Systems shows the widest growth spread in the group, with EBITDA compounding 10.66 percentage points faster than revenue. Revenue growth near 30% is already substantial, but the faster EBITDA rate indicates that a growing share of incremental sales has been reaching operating profit. That pattern is consistent with a business benefiting from product mix, expense leverage, and greater scale rather than relying solely on unit growth.
The latest quarterly figures help illustrate the mechanism. First-quarter 2026 revenue rose 26.1% year over year to $804.2 million, while operating margin increased to 30.0% from 26.5%. Enterprise data revenue nearly doubled, driven by power solutions for AI and server applications, and communications revenue increased 55.5%. Gross margin, however, was broadly unchanged at 55.3%, suggesting that much of the operating-margin improvement came from R&D and SG&A growing more slowly than sales.
The key issue to monitor is whether that expense leverage remains visible as the revenue mix changes. Storage and computing revenue declined in the same quarter, so the current signal is not broad-based strength across every end market. FMP income statement data is most useful here when paired with quarterly revenue-segmentation data, allowing readers to separate margin improvement created by scale from improvement driven by unusually strong demand in enterprise data.
Amphenol Corporation (APH)
5-Year Revenue CAGR: 21.79%
5-Year EBITDA CAGR: 29.42%
Amphenol's EBITDA CAGR exceeded its revenue CAGR by 7.63 percentage points. For a company already producing annualized revenue growth above 20%, that gap is notable because it suggests that expansion has not required a proportional increase in the operating cost base. The signal reflects a combination of organic growth, portfolio breadth, disciplined acquisition activity, and the ability to maintain profitability as the company adds new products and end markets.
That framework remains visible in the latest results. First-quarter 2026 sales reached $7.6 billion, up 58% year over year and 33% organically. Orders totaled $9.4 billion, producing a book-to-bill ratio of 1.24, while adjusted operating margin reached 27.3%. Management attributed the quarter's growth to strength across most end markets, particularly IT datacom, alongside contributions from acquisitions, including the recently completed CommScope transaction.
The analytical question is how much of the EBITDA advantage is structural and how much reflects acquisition timing or unusually strong datacenter demand. Order growth above sales provides evidence of continued demand visibility, but integration costs, purchase-accounting adjustments, and changes in end-market mix can affect the comparability of reported margins. The FMP Income Statement API can establish the profitability trend, while cash flow statements and merger-and-acquisition datasets help test whether higher earnings are translating into cash after integration and capital requirements.
Trane Technologies plc (TT)
5-Year Revenue CAGR: 10.93%
5-Year EBITDA CAGR: 18.46%
Trane Technologies produced a 7.53-percentage-point spread between EBITDA and revenue CAGR. Unlike a high-growth technology company, Trane's signal is built on moderate top-line expansion accompanied by significantly faster profit growth. That profile generally points to pricing discipline, service and aftermarket contribution, productivity gains, and a favorable mix of commercial HVAC projects. It also makes margin progression more important than headline revenue acceleration when evaluating the strength of the underlying trend.
The current operating picture is more nuanced than the five-year CAGR alone. First-quarter 2026 revenue increased 6% to approximately $5.0 billion, while bookings rose 27% to $6.7 billion and backlog reached a record $10.7 billion. At the same time, adjusted EBITDA margin declined 40 basis points year over year to 17.7%. Demand was especially strong in Americas Commercial HVAC, but the latest quarter shows that a strong order environment does not automatically produce immediate margin expansion.
For that reason, backlog conversion is the central item to follow. Readers should examine whether higher bookings translate into revenue without creating unfavorable project mix, installation costs, or working-capital pressure. FMP income statement data provides the historical margin trend, but it is best read alongside quarterly earnings materials containing bookings and backlog, as well as analyst-estimate data that shows how consensus revenue and margin assumptions change as projects move through the pipeline.
Motorola Solutions, Inc. (MSI)
5-Year Revenue CAGR: 8.61%
5-Year EBITDA CAGR: 10.27%
Motorola Solutions has the narrowest EBITDA-to-revenue spread in the screen at 1.66 percentage points. The difference is still meaningful, but it represents gradual operating leverage rather than a dramatic profitability reset. The company's revenue base includes both equipment-oriented activity and higher-value software and services, so the signal depends heavily on mix, recurring revenue contribution, and the efficiency with which the company supports a growing installed base.
First-quarter 2026 results provide a useful example. Total sales rose 7% to $2.7 billion, but Software and Services revenue increased 18%, compared with 1% growth in Products and Systems Integration. Non-GAAP operating margin improved 50 basis points to 28.8%, and backlog rose 11% to a record $15.7 billion. The quarter therefore supports the view that faster growth in software and services is contributing to operating leverage. However, free cash flow declined to $389 million from $473 million because of inventory investment and higher interest and tax payments.
That cash-flow divergence deserves attention because EBITDA expansion is most useful when it is accompanied by durable cash conversion. The relevant monitoring points include software and services mix, acquisition contributions, deferred revenue, inventory requirements, and the rate at which backlog becomes recognized sales. An FMP income statement dataset can show the earnings progression, while segment revenue and cash flow statement data provide the additional context needed to judge the quality of that growth.
Analog Devices, Inc. (ADI)
5-Year Revenue CAGR: 17.82%
5-Year EBITDA CAGR: 21.24%
Analog Devices recorded a 3.42-percentage-point advantage in EBITDA CAGR over revenue CAGR. The spread is less pronounced than those of MPWR, APH, or TT, but it remains relevant given the cyclicality of analog semiconductor demand. ADI's margins can move sharply as factory utilization, customer inventories, product mix, and industrial demand change, so a five-year CAGR should be interpreted as a through-cycle measure rather than evidence of uninterrupted annual margin expansion.
Fiscal second-quarter 2026 results showed a significant improvement from the prior-year comparison. Revenue increased 37% to $3.62 billion, with growth across all end markets and particular strength in Industrial and Communications. Reported operating margin rose to 38.1% from 25.7%, while adjusted operating margin increased to 49.0% from 41.2%. Management also reported record bookings across its Industrial, Automotive, and Communications markets.
The magnitude of the latest margin increase makes the quality and durability of the recovery the main area to monitor. Reported and adjusted results remain meaningfully different because of acquisition-related expenses, and semiconductor earnings can benefit quickly when revenue recovers against a relatively fixed manufacturing and operating structure. FMP income statement data should therefore be combined with balance-sheet inventory, end-market revenue, and analyst-estimate datasets. Together, those inputs help distinguish sustained operating improvement from the normal effects of inventory replenishment and higher capacity utilization.
The Signal Beneath the Growth Numbers
Taken together, these five companies do not form a single sector trade. They span semiconductors, electronic components, climate systems, and public-safety technology. What links them is a common operating pattern: EBITDA has compounded faster than revenue over five years, indicating that incremental sales have generated a disproportionate increase in operating earnings. The breadth of the group matters because it suggests the screen is capturing several forms of operating leverage rather than one isolated industry cycle.
The signal is not equally strong or equally clean across every name. Monolithic Power Systems and Amphenol show the widest separation between revenue and EBITDA growth, placing greater emphasis on product mix, scale, and cost absorption. Trane Technologies presents a different case, where pricing, productivity, and backlog conversion are central to the margin story. Motorola Solutions shows a narrower spread, making software mix and cash conversion more important than the headline CAGR alone. Analog Devices sits within a cyclical semiconductor framework, where utilization rates and inventory normalization can materially influence EBITDA growth.
That distinction is why the screen works best as an entry point rather than a conclusion. The broader financial dataset available through FMP allows the initial EBITDA-to-revenue spread to be tested against cash generation, balance-sheet changes, and forward expectations. FMP's Income Statement API can identify where profitability is expanding faster than sales, while the Cash Flow Statement API helps determine whether that improvement is translating into operating and free cash flow. The Balance Sheet Statement Growth API adds another control by showing whether earnings growth has been accompanied by rising inventories, debt, goodwill, or working-capital requirements.
A second layer is to compare the historical signal with what the market already expects. FMP's Financial Estimates API can show whether analysts are incorporating continued revenue and earnings improvement, while the Price Target Summary API provides a broader view of how valuation expectations are distributed across the screened universe. The relevant question is not simply whether a consensus target sits above or below the current share price. It is whether revisions to forecasts and valuation assumptions are moving in step with the underlying cash-flow and margin data.
The practical takeaway is that EBITDA outgrowing revenue identifies where the economics of a business may be changing faster than its headline sales trend suggests. It does not establish the durability or valuation of that change on its own. The strongest signals are those supported by margin expansion, cash generation, balance-sheet quality, and forward estimates pointing in the same direction.
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:
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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:
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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.
From Static Screens to a Continuous Operating Signal
A one-time screen captures a snapshot, but repeating the same methodology through FMP's Income Statement API and Income Statement Bulk API turns that snapshot into a consistent operating signal. The value lies in tracking whether the gap between revenue and EBITDA growth persists, narrows, or reverses as new financial results are reported.
If you found this useful, you might also like: Weekly Signals Desk | Concentrated Analyst Revisions via the FMP API (June 29-July 3)
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.
