This week's screen, run through the Financial Modeling Prep Income Statement API, surfaced a consistent signal across five names: operating performance is compounding faster than top-line growth. The pattern cuts across semiconductors, networking, and gold producers — different industries, same underlying shift toward expanding operating leverage.
In this note, we break down the companies behind the signal and walk through how the FMP's Income Statement API can be used to systematically identify multi-year CAGR divergence at scale.
5 Companies With Strong CAGR Momentum
Broadcom Inc. (AVGO)
5-Year Revenue CAGR: 20.89%
5-Year EBITDA CAGR: 26.10%
Broadcom's spread between revenue and EBITDA growth reflects a business that has steadily increased its operating efficiency while scaling into higher-value segments. The differential is not extreme, but it is persistent — suggesting that margin expansion has been structural rather than episodic. Over a five-year window, that typically points to pricing power, disciplined cost control, or a shift toward software and recurring revenue streams.
The recent integration of VMware adds another layer to this signal. The transaction materially changes the mix of the business, introducing a larger base of subscription-driven software revenue alongside its semiconductor operations. That shift is consistent with the observed EBITDA outperformance, as software carries structurally higher margins. Tracking this evolution through the income statement — particularly segment-level revenue and operating income — helps clarify whether the margin profile continues to tilt upward as integration progresses. Additional context from segment disclosures and operating expense breakdowns would further illustrate how much of the expansion is mix-driven versus efficiency-driven.
Gold Fields Limited (GFI)
5-Year Revenue CAGR: 14.92%
5-Year EBITDA CAGR: 21.32%
Gold Fields shows a clear example of operating leverage within a commodity-linked business. Revenue growth in the mid-teens range is notable given the volatility of gold prices over the period, but the more important signal is EBITDA growing meaningfully faster. That divergence often indicates cost discipline, improved asset quality, or operational efficiencies at the mine level — all critical variables in a sector where margins can compress quickly during weaker pricing environments.
In this case, the spread suggests that Gold Fields has been able to manage input costs and optimize production across its portfolio. This becomes particularly relevant when viewed alongside periods of fluctuating gold prices, where maintaining margin expansion requires more than favorable commodity tailwinds. Monitoring cost of sales, all-in sustaining costs (AISC), and production volumes through income statement and supplementary operational disclosures would provide a clearer read on whether this margin profile is being sustained through operational execution rather than external pricing support.
Micron Technology, Inc. (MU)
5-Year Revenue CAGR: 18.81%
5-Year EBITDA CAGR: 88.34%
Micron stands out in the dataset due to the magnitude of the divergence: EBITDA growth at 88.34% versus revenue growth of 18.81%. That scale of expansion is rarely linear and typically reflects the cyclical nature of the memory market, where profitability can swing sharply depending on supply-demand balance and pricing conditions. The data compresses multiple cycle phases into a single CAGR figure, effectively capturing both trough and peak margin environments.
What the numbers highlight is not just growth, but volatility embedded within that growth. Periods of tight supply and strong pricing — particularly in DRAM and NAND — tend to drive outsized EBITDA expansion relative to revenue. Conversely, downturns compress margins rapidly. The CAGR signal, therefore, is best interpreted as evidence of operating leverage sensitivity rather than steady margin progression. Using income statement data alongside pricing trends and inventory levels can help contextualize where current margins sit within the broader cycle, and whether recent performance aligns more closely with expansionary or contractionary phases.
Arista Networks, Inc. (ANET)
5-Year Revenue CAGR: 28.83%
5-Year EBITDA CAGR: 36.07%
Arista's growth profile reflects a company scaling within a structurally expanding segment of networking — particularly cloud and data center infrastructure. Revenue growth approaching 30% annually over five years indicates sustained demand, but the faster pace of EBITDA expansion suggests that the company has been able to translate that demand into improving operating efficiency.
This pattern is often associated with a combination of product standardization, software integration, and disciplined operating expense management. As hyperscale customers expand capacity, vendors like Arista can benefit from both volume and efficiency gains, especially when incremental revenue carries higher contribution margins. Reviewing income statement line items such as gross margin trends and operating expenses relative to revenue can help determine whether the expansion is being driven by cost control, pricing dynamics, or product mix. The consistency of this spread across multiple years reinforces the signal as structural rather than opportunistic.
AngloGold Ashanti plc (AU)
5-Year Revenue CAGR: 16.25%
5-Year EBITDA CAGR: 34.15%
AngloGold Ashanti presents one of the more pronounced margin expansion profiles in the group, with EBITDA growth more than doubling the pace of revenue. In a gold producer, that level of divergence typically reflects a combination of portfolio optimization, cost restructuring, and operational improvements across key assets. It suggests that the company has not only benefited from supportive pricing periods but has also adjusted its cost base and production mix.
Recent strategic moves — including portfolio reshaping and geographic repositioning — align with this pattern, as companies in the sector increasingly focus on higher-margin assets and jurisdictional efficiency. The key analytical question is whether this margin expansion is repeatable under varying gold price conditions. Tracking cost structures, production efficiency, and capital allocation through income statement data and supporting disclosures can help determine whether the observed CAGR spread reflects a durable shift in the business or a favorable alignment of external factors over the measured period.
Reading the Signal Beneath the Surface
Across all five names, the common thread is not growth in isolation — it's the consistency of operating leverage showing up across very different business models. Semiconductors, networking infrastructure, and gold producers rarely move in tandem at the top line, yet each exhibits the same underlying pattern: EBITDA compounding faster than revenue over a multi-year window. That convergence suggests the signal is less about sector-specific tailwinds and more about how efficiently capital and cost structures are being managed beneath the surface.
What stands out is the variation in how that leverage is achieved. In Broadcom and Arista, the spread appears tied to mix shift and scalability — software exposure, standardized hardware, and operating discipline. In Micron, the signal is more cyclical, with margin expansion amplifying during favorable pricing environments. Meanwhile, Gold Fields and AngloGold reflect operational execution within commodity constraints, where cost control and asset quality drive the divergence. The takeaway is that identical CAGR spreads can emerge from fundamentally different drivers — which is why the signal needs to be contextualized, not generalized.
This is where combining multiple datasets becomes critical. Income statement data establishes the baseline — revenue, EBITDA, and their respective growth rates — but it only captures the outcome. Layering in cash flow data can help determine whether EBITDA expansion is translating into actual operating cash generation or being offset by working capital swings. Bringing in historical enterprise value or market cap data allows that operating performance to be viewed relative to valuation, highlighting whether margin expansion is already reflected in pricing. Analyst estimates and price target datasets add another dimension, showing whether forward expectations align with the observed historical leverage or diverge from it.
When these inputs are aligned — for example, comparing EBITDA CAGR from the Income Statement API with forward estimates and valuation multiples — the screen evolves from a static filter into a more complete analytical framework. The signal then becomes less about identifying companies that have exhibited operating leverage, and more about understanding whether that leverage is being sustained, recognized, or potentially misaligned with current expectations.
How to Build a Clean CAGR Workflow Using FMP Data
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
A screening framework only remains reliable as it grows if the underlying rules stay fixed. The CAGR formula, the financial line items being measured, and the time horizon all need to remain identical as the universe expands. What changes is simply the number of companies passing through the filter. That's why most workflows start in a controlled testing environment. At this stage, the Basic plan provides sufficient access to the Income Statement endpoints to confirm that fiscal years line up correctly, that missing fields are handled consistently, and that reporting variations across companies don't distort the calculation.
Once the process is working cleanly, scaling becomes a question of coverage rather than methodology. Moving to the Starter plan allows the same screening logic to run across a much broader portion of the U.S. equity market. The benefit isn't only the larger sample size — it's the additional context that comes with it. With more companies flowing through the model, it becomes easier to see whether the spread between revenue CAGR and EBITDA CAGR is an outlier signal or simply a common pattern within certain sectors or market-cap ranges.
For analysts looking to extend the screen further — across international equities or longer historical datasets — the Premium plan expands both geographic coverage and time depth without requiring any adjustments to the model itself. The formula, filters, and logic remain unchanged; only the dataset widens. That continuity is what ultimately transforms a one-off screen into a repeatable research tool — designed once, validated carefully, and then applied at scale without compromising analytical consistency.
From Periodic Screens to an Ongoing Operating Read
What begins as a periodic screen can evolve into a continuous read on operating performance when the same logic is applied consistently over time. With a structured pull from the Financial Modeling Prep Income Statement API and Income Statement Bulk API, the signal shifts from a snapshot into a repeatable way to track how efficiently companies are converting growth into operating results as new data comes in.
If you found this useful, you might also like: Weekly Signals Desk | Four Dividend Moves Flagged by the FMP API (March 9-13)
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.
