Signals Desk Weekly | Multi-Year CAGR Strength Taking Shape Across Five Names (April 20-24)

This week's data scan using the FMP's Income Statement surfaced a consistent cross-sector signal: operating earnings are compounding faster than top-line growth across a select group of names.

The pattern isn't isolated to one industry or business model — it's showing up in companies where margin expansion, not just revenue acceleration, is driving the trajectory. In this note, we'll break down the five names where that spread is most pronounced, and walk through how the API framework behind the screen makes the signal repeatable.

Key Takeaways

• EBITDA growth outpacing revenue across COHR, CHEF, RTX, MLI, and ADSK points to sustained margin expansion, not just top-line momentum.
• The signal appears across unrelated sectors, suggesting a broader shift toward operating efficiency rather than isolated industry tailwinds.
• CAGR spread acts as a proxy for earnings conversion — highlighting how effectively revenue is translating into operating profit over time.
• Deeper validation comes from layering income statement trends with cash flow, balance sheet, and forward estimate data to assess durability.

Five Names Where EBITDA Growth Is Outrunning Revenue

COHR Coherent Corp.

5-Year Revenue CAGR: 18.63%
5-Year EBITDA CAGR: 32.60%

Coherent's spread between revenue and EBITDA growth stands out even within a group already defined by operating leverage. A 32.60% EBITDA CAGR against 18.63% revenue growth points to sustained margin expansion rather than a one-off efficiency gain. In Coherent's case, that dynamic aligns with a business mix increasingly tilted toward higher-value photonics and compound semiconductor solutions, where incremental revenue tends to carry stronger contribution margins. The data suggests that growth has been accompanied by structural improvement in cost absorption and pricing discipline, not just volume.

What makes this signal worth tracking is how it intersects with current demand themes in optical networking and AI-related infrastructure. As hyperscale and telecom capex cycles evolve, Coherent sits upstream in components that tend to reflect both cyclical swings and secular upgrades. The margin profile implied by the EBITDA trajectory suggests that even moderate revenue variability could still translate into relatively stable operating earnings. Pulling segment-level detail from the income statement dataset — particularly gross margin and operating expense trends — would help determine how much of this expansion is mix-driven versus cost-optimized, and whether it persists across reporting periods.

CHEF The Chefs' Warehouse, Inc.

5-Year Revenue CAGR: 9.36%
5-Year EBITDA CAGR: 22.07%

The Chefs' Warehouse presents a different kind of operating leverage profile — one shaped less by product innovation and more by distribution efficiency and pricing structure. With EBITDA growing at 22.07% against 9.36% revenue growth, the spread indicates a business that has steadily improved its ability to translate sales into earnings, despite operating in a relatively low-margin segment of foodservice distribution. This suggests tighter control over logistics, procurement, and customer mix, particularly as the company focuses on specialty and premium products.

The signal becomes more relevant when viewed against the broader normalization of restaurant demand post-pandemic. As volumes stabilize, incremental gains in EBITDA are less likely to come from demand recovery and more from execution — routing efficiency, supplier terms, and SKU-level margin management. That makes the consistency of this CAGR spread notable. Reviewing operating expense lines and cost of goods sold through the income statement endpoint can help clarify whether margin expansion is being driven by scale efficiencies or selective pricing power within niche categories. The distinction matters, as it influences how resilient the spread is under different demand conditions.

RTX RTX Corporation

5-Year Revenue CAGR: 10.56%
5-Year EBITDA CAGR: 15.12%

RTX's growth profile reflects a more measured but still meaningful divergence between revenue and EBITDA, with a 15.12% EBITDA CAGR compared to 10.56% on the top line. In large-scale aerospace and defense businesses, this type of spread often signals disciplined program management and margin recovery within long-cycle contracts. Unlike more transactional industries, revenue growth here tends to be constrained by backlog conversion and government spending cycles, so EBITDA acceleration typically points to internal efficiencies — cost control, contract repricing, or improved execution on existing programs.

Recent operational challenges in parts of the aerospace supply chain have added complexity to that narrative, particularly around engine components and maintenance cycles. Against that backdrop, the persistence of EBITDA outpacing revenue suggests that margin management efforts are offsetting some of those pressures. This is where tracking segment-level operating profit and backlog data — alongside income statement trends — becomes useful. It helps distinguish between temporary cost recoveries and more durable improvements in program economics, especially as defense budgets and commercial aerospace demand continue to evolve.

MLI Mueller Industries, Inc.

5-Year Revenue CAGR: 11.54%
5-Year EBITDA CAGR: 25.46%

Mueller Industries shows one of the widest spreads in the group, with EBITDA growing at 25.46% versus 11.54% revenue growth. In a metals and industrial components business, that kind of divergence often reflects a combination of pricing cycles and operational efficiency. Over the past several years, input cost volatility — particularly in copper and other base metals — has created an environment where companies with disciplined inventory management and pricing strategies can expand margins even without outsized revenue growth.

What stands out in Mueller's case is the consistency implied by the CAGR figures, suggesting that margin expansion has not been purely cyclical. Instead, it points to a business that has managed cost structures and product mix effectively across different commodity environments. To validate that, it's useful to examine gross margin trends and operating income stability within the income statement dataset, alongside any available data on input costs. This helps separate commodity-driven tailwinds from underlying operational improvements — a key distinction when interpreting whether the observed spread is structural or environment-dependent.

ADSK Autodesk, Inc.

5-Year Revenue CAGR: 17.53%
5-Year EBITDA CAGR: 26.23%

Autodesk's profile reflects a familiar pattern within software — but the magnitude of the spread still warrants attention. With EBITDA compounding at 26.23% against 17.53% revenue growth, the data points to continued operating leverage within a subscription-based model. As recurring revenue builds and customer acquisition costs stabilize, incremental revenue tends to flow through at higher margins, particularly once the platform reaches scale. Autodesk's transition toward cloud and subscription licensing over recent years aligns closely with this type of margin expansion.

The more interesting aspect is how this trend holds up as growth normalizes. In mature SaaS businesses, maintaining a gap between revenue and EBITDA growth often depends on controlling operating expenses, especially in sales and marketing. The current figures suggest that Autodesk has managed that balance effectively so far. Looking deeper into operating expense ratios and deferred revenue trends through the income statement data can provide additional context — specifically whether margin expansion is being sustained through efficiency gains or supported by changes in billing and revenue recognition patterns.

Interpreting the Spread: What the Data Is Actually Signaling

Across these five names, the pattern isn't just growth — it's conversion. Revenue is rising, but EBITDA is scaling faster, pointing to a shared shift in operating efficiency. Whether it's mix improvements in semiconductors, execution discipline in aerospace, or scale effects in software, the spread reflects how effectively incremental revenue is turning into earnings. In that sense, it functions less as a growth metric and more as a read on operational quality over time.

What makes the signal more compelling is its breadth. This isn't tied to a single cycle or sector tailwind — it appears across businesses with very different demand drivers. When margin expansion shows up this consistently, it suggests a broader emphasis on cost structure, pricing discipline, and efficiency rather than purely top-line acceleration. The takeaway isn't that growth is strong, but that growth is being managed differently.

Extending the analysis requires moving beyond the income statement. Cash flow data helps determine whether EBITDA gains are translating into real cash generation or being absorbed by working capital and capital expenditures. Balance sheet context adds another layer, particularly around leverage and capital allocation. This is where a multi-endpoint approach — combining income statements, cash flow statements, and balance sheets through datasets available on FMP — turns a static screen into a more complete operating view.

Positioning the signal against forward expectations sharpens it further. Comparing historical margin expansion with analyst estimates and price targets helps identify whether the improvement is already embedded in consensus or still underrecognized. The spread itself is only a starting point — its relevance depends on whether it aligns with cash generation, capital structure, and forward assumptions.

Building a Clean, Repeatable CAGR Framework with FMP

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

Scaling this type of screen is less about adding complexity and more about holding the framework steady. The CAGR formula, the chosen financial line items, and the time horizon should remain unchanged — expansion should only affect how many companies are being evaluated, not how they're being measured. That's why the process starts narrow. Within the Basic plan, the Income Statement endpoints provide enough coverage to ensure reporting periods line up, missing data is handled consistently, and company-specific nuances aren't skewing results.

Once that baseline is validated, widening the universe becomes a straightforward extension. Moving into the Starter plan allows the same logic to be applied across a broader segment of the U.S. market without modifying the screen itself. The benefit here isn't just more data — it's context. With a larger sample, it becomes easier to distinguish between company-specific operating leverage and patterns that are common within certain sectors or market caps.

For deeper coverage — whether that means expanding internationally or extending the historical window — the Premium plan simply increases the dataset's reach. The methodology doesn't change. What evolves is the scope, not the structure. That continuity is what makes the screen repeatable: defined once, pressure-tested in a controlled set, and then applied more broadly without introducing inconsistencies into the analysis.

From Periodic Screens to an Ongoing Operating Read

What starts as a one-off screen becomes more useful when treated as a recurring read on operating efficiency — refreshed as new filings update the underlying data through the FMP Income Statement API and Income Statement Bulk API. Over time, the signal shifts from a static comparison into a moving view of how consistently revenue is converting into earnings.

If you found this useful, you might also like: Weekly Signals Desk | Five Notable Valuation Disconnects via the FMP API (April 13-17)

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.