Signals Desk Weekly | Multi-Year CAGR Strength Taking Shape Across 5 Names (Feb 16-20)

This week's multi-year CAGR screen surfaced five companies where EBITDA is compounding materially faster than revenue — a pattern that tends to emerge when operating leverage is quietly strengthening beneath the surface. The dispersion spans refining, copper, networking hardware, electronics manufacturing, and infrastructure, suggesting the signal isn't sector-specific but structural.

The scan was built using the FMP's Income Statement API, pulling standardized multi-year financials to isolate consistent revenue and EBITDA growth rates across identical reporting windows. In this note, we'll break down what the screen revealed — and how to replicate the workflow directly from the API data.

5 Companies With Strong CAGR Momentum

SCCO Southern Copper Corporation

5-Year Revenue CAGR: 11.39%
5-Year EBITDA CAGR: 19.35%

Southern Copper's five-year spread — EBITDA compounding roughly eight percentage points faster than revenue — indicates expanding operating efficiency within a cyclical commodity framework. In capital-intensive mining businesses, EBITDA acceleration relative to top-line growth often reflects a combination of production scale, cost discipline, and favorable realized pricing. The magnitude of the gap suggests margin expansion has been doing more of the work than volume alone.

Copper markets have recently been shaped by tight supply dynamics, project delays across Latin America, and sustained electrification-related demand narratives covered widely in industry and financial press. Against that backdrop, SCCO's multi-year EBITDA trajectory signals that incremental revenue has been translating into disproportionately higher operating cash flow. That relationship is observable directly through the income statement dataset, but pairing it with segment production data and cost-per-pound disclosures in SEC filings would help contextualize whether margin expansion is coming from structural cost improvements or pricing leverage.

From a screening standpoint, the signal here is not simply “copper exposure.” It is margin durability across cycles. Monitoring future income statement updates — particularly gross margin and operating cost trends — will clarify whether the EBITDA growth rate continues to outpace revenue in varying commodity environments.

ANET Arista Networks, Inc.

5-Year Revenue CAGR: 28.29%
5-Year EBITDA CAGR: 35.39%

Arista's revenue growth near 30% annualized over five years is notable on its own; the faster 35.39% EBITDA CAGR adds a second layer: scaling efficiency. When EBITDA compounds meaningfully faster than already elevated revenue growth, it often reflects operating leverage embedded in a software-heavy or high-value hardware mix.

Recent coverage of AI-driven data center investment and hyperscaler infrastructure buildouts has kept networking suppliers in focus. Arista's financial profile suggests that growth has not come at the expense of margin discipline. Instead, incremental revenue has translated into improving operating performance — a dynamic visible in the widening gap between top-line and EBITDA compounding rates.

To deepen the analysis, combining income statement data with customer concentration disclosures and segment-level revenue breakdowns would provide additional clarity on growth composition. Analyst estimate revisions and backlog commentary, where available, can further contextualize whether EBITDA expansion aligns with sustained demand patterns or episodic infrastructure cycles. The multi-year compounding spread signals operational scaling — a pattern worth tracking against future margin disclosures.

FN Fabrinet

5-Year Revenue CAGR: 15.53%
5-Year EBITDA CAGR: 19.78%

Fabrinet's five-year revenue CAGR of 15.53%, paired with a 19.78% EBITDA CAGR, reflects steady operational improvement within a contract manufacturing model. In outsourced manufacturing, margin expansion is rarely automatic; it typically reflects mix shift toward higher-value programs, improved utilization, or disciplined cost controls.

The company's exposure to optical communications, automotive, and industrial customers places it within supply chains that have undergone post-pandemic normalization and selective reshoring adjustments. EBITDA compounding ahead of revenue suggests that Fabrinet has been capturing efficiency as volumes scale or product mix shifts upward. Income statement data shows the quantitative outcome; integrating inventory turnover and working capital trends would help determine how much of the margin gain is structural versus cyclical normalization.

For observers, the relevant signal is consistency. A sustained EBITDA growth rate above revenue over multiple years indicates improving incremental margins. Tracking future quarterly EBITDA margins and segment revenue composition would clarify whether this trajectory is stable across varying demand conditions.

CLS Celestica Inc.

5-Year Revenue CAGR: 13.24%
5-Year EBITDA CAGR: 38.10%

Celestica's profile stands out for magnitude. A 13.24% five-year revenue CAGR alongside a 38.10% EBITDA CAGR reflects a substantial widening between sales growth and operating profitability. That degree of spread often coincides with restructuring benefits, portfolio repositioning, or mix evolution toward higher-margin verticals.

Recent public reporting has highlighted strength in aerospace, defense, and advanced technology segments within diversified manufacturing providers. Celestica's multi-year EBITDA trajectory suggests that the company's operational model has shifted meaningfully relative to its revenue base. This is less a story of acceleration in sales and more a story of improving earnings quality.

Income statement trends alone surface the compounding disparity, but pairing that data with segment margin disclosures and capital allocation patterns (such as debt reduction or share repurchases) would clarify how incremental cash generation is being deployed. The key analytical takeaway is margin transformation. Sustained divergence between EBITDA and revenue growth signals structural operating improvement — a pattern that merits ongoing monitoring through future filings.

STRL Sterling Infrastructure, Inc.

5-Year Revenue CAGR: 14.64%
5-Year EBITDA CAGR: 33.77%

Sterling Infrastructure's five-year revenue CAGR of 14.64% combined with a 33.77% EBITDA CAGR reflects operating leverage within a project-driven business. Infrastructure and civil construction firms typically operate on relatively tight margins, so multi-year EBITDA compounding at more than double the revenue growth rate points to improved project mix, pricing discipline, or execution efficiency.

Public infrastructure spending, energy transition projects, and data center-related construction activity have been recurring themes in financial coverage. Against that backdrop, Sterling's compounding spread suggests that incremental backlog has translated into more profitable work rather than simply higher volume. Reviewing backlog disclosures, segment margin data, and cash flow statements would help confirm whether margin gains stem from mix shift or operational execution.

From a screening perspective, this is a margin-expansion signal inside a traditionally cyclical sector. Monitoring upcoming income statement releases for stability in EBITDA margins relative to revenue growth will help determine whether the multi-year trend continues to reflect structural improvement or normalizes with project cycles.

Reading the Signal Beneath the Surface

Across copper mining, hyperscale networking, contract manufacturing, EMS, and civil infrastructure, the shared feature is not sector momentum — it is margin acceleration. In each case, EBITDA has compounded materially faster than revenue over a five-year period, isolating operating leverage as the central variable. When earnings growth consistently outruns top-line expansion across unrelated industries, it points to internal efficiency and mix discipline rather than broad cyclical uplift.

Interpreting that properly requires stepping beyond a single dataset. The Income Statement series establishes the revenue-to-EBITDA spread, but pairing it with cash flow statements clarifies whether operating gains are converting into durable free cash flow or being absorbed by reinvestment and working capital swings. Layering in key metrics such as ROIC and incremental margin trends shows whether higher EBITDA reflects genuine capital efficiency. Enterprise value and valuation ratios then frame how much of that improvement is already embedded in pricing. Used together — through a structured workflow built on Financial Modeling Prep's broader dataset — these endpoints transform a simple CAGR screen into a cross-validated operating read.

The analysis deepens further when earnings trajectories are set against positioning data. Analyst estimate revisions can indicate whether consensus models are adjusting to the margin structure, while insider transaction records add a behavioral dimension to the earnings trend. Aligning those with historical price data helps separate earnings-led strength from sentiment-driven momentum.

What ultimately links these five companies is structural, not speculative: incremental revenue has translated into operating earnings at an increasing rate. That pattern tends to be more resilient than episodic revenue acceleration because it reflects changes inside the cost structure. The signal is less about timing a cycle and more about identifying where operational compounding is visibly taking hold across multiple data layers rather than a single headline metric.

How to Build a Clean CAGR Workflow Using FMP Data

A reliable CAGR screen is less about the formula and more about controlling the dataset. The math does not change. What determines whether the output is useful is consistency — same reporting periods, same financial line items, same time horizon across every ticker. Lock those variables down first, and the growth rates become comparable across sectors and capital structures. The process below outlines how to structure that workflow using FMP's Income Statement data, beginning with one company and then expanding without altering the underlying logic.

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 a CAGR framework works best when nothing about the core methodology changes — only the breadth of the dataset does. The initial build should remain intentionally tight: a contained group of symbols used to confirm that the growth calculations behave consistently across varying fiscal calendars, capital structures, and reporting quirks. At this stage, the Basic plan is sufficient. It provides access to the Income Statement endpoints required to validate assumptions, reconcile missing fields, and pressure-test edge cases before expanding scope.

Once the calculation logic is stable, moving to the Starter plan allows the same screen to run across a much wider slice of the U.S. equity universe. The practical advantage is not just increased coverage — it is improved comparability. Broader datasets sharpen relative analysis, making it easier to identify which growth spreads are statistically unusual versus common within a sector or capitalization band.

For those extending beyond domestic coverage or requiring deeper historical series, the Premium plan increases geographic reach and time depth without requiring any structural change to the workflow. The screening formula, filters, and logic remain intact. Only the input universe expands. That continuity is the point: a process that begins as a controlled validation exercise and scales into a repeatable, market-wide research tool without being redesigned at each stage.

From Periodic Screens to an Ongoing Operating Read

Run consistently, a CAGR screen shifts from a one-time filter to a standing operating monitor. Using the Financial Modeling Prep Income Statement API and Income Statement Bulk API, the same framework can be refreshed alongside each reporting cycle, turning multi-year growth spreads into a rolling check on margin structure and earnings quality. Over time, the signal becomes less about isolated outperformance and more about whether operating leverage is compounding — or fading — beneath the headline numbers.

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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.