Fair Isaac lost close to a quarter of its market value this week after a regulatory decision opened mortgage pricing to a rival credit score. Its five-year income statement explains why the reaction was so sharp: EBITDA has compounded at well over twice the pace of revenue, and margins have nearly doubled, largely on the strength of pricing in the very product now under pressure. That is the core question this screen asks of every company it surfaces, namely what is behind the gap between sales growth and profit growth, and how secure that source is.
This edition uses FMP's Income Statement API to compare five-year revenue and EBITDA growth for ServiceNow, First Solar, Fair Isaac, Tenet Healthcare and Monolithic Power Systems, and walks through how the same API can support a repeatable screen for operating leverage across a broader universe.
Key Takeaways
- All five companies grew EBITDA faster than revenue over five years, but the mechanisms range from software scale and pricing power to tax incentives and a deliberate shift in business mix.
- Fair Isaac and First Solar show how dependent a margin story can be on a single external factor, a pricing advantage in one case and federal manufacturing credits in the other.
- Tenet's spread came from changing what the company is, with its ambulatory surgery business growing in weight while hospitals were sold, rather than from faster growth.
- The strongest form of this signal is margin expansion that also shows up in cash flow and survives a change in the conditions that produced it, which is where the next layer of analysis should focus.
Five Companies Where Profit Growth Outpaced the Top Line
ServiceNow, Inc. (NOW)
5-Year Revenue CAGR: 24.05%
5-Year EBITDA CAGR: 45.94%
ServiceNow produced the widest spread in the screen, with EBITDA compounding at nearly twice the rate of an already strong revenue line. Part of that reflects genuine operating scale: a subscription software platform with high renewal rates spreads its sales, research and infrastructure costs across a rapidly expanding customer base, and its EBITDA margin has more than doubled over the period. ServiceNow last appeared in this series in August, and the pattern has held.
The size of the spread also reflects a starting point worth understanding. On a reported basis, ServiceNow's EBITDA in 2020 was low relative to revenue because stock-based compensation, a large expense for software companies, absorbed much of its gross profit. As revenue grew, that expense shrank as a share of sales, which mechanically lifts reported EBITDA. FMP's Cash Flow Statement API reports stock-based compensation as a separate line, which makes it possible to separate improvement driven by scale from improvement that comes from equity compensation growing more slowly than revenue.
First Solar, Inc. (FSLR)
5-Year Revenue CAGR: 14.00%
5-Year EBITDA CAGR: 31.39%
First Solar's EBITDA margin roughly doubled over five years, the result of rising U.S. module prices, a growing domestic manufacturing base and, most significantly, the advanced manufacturing production credit introduced in 2022. Those credits are recognized as a reduction in cost of sales, so they flow directly into EBITDA. The company has also been selling some of these credits to third parties, turning them into cash ahead of its own tax obligations.
That makes the composition of the spread as important as its size. A meaningful part of First Solar's margin expansion rests on a federal policy framework rather than on pricing or productivity alone, and changes to that framework would affect earnings directly. FMP's As Reported Income Statements API presents the company's line items as filed, which helps identify how the credits are reflected in cost of sales and how much of the margin improvement would remain without them. That separation is the clearest way to judge how durable the current margin level is.
Fair Isaac Corporation (FICO)
5-Year Revenue CAGR: 8.99%
5-Year EBITDA CAGR: 23.61%
Fair Isaac combined modest revenue growth with one of the fastest rates of EBITDA expansion in the group, and its margin nearly doubled over the period. The driver is well known: the company raised prices on its credit scores, particularly those used in mortgage origination, where its score had been effectively required. Because the scores business carries minimal incremental cost, those increases translated almost entirely into operating profit.
This week's regulatory decision to include a competing score in how mortgages are priced is a direct test of that model. Fair Isaac's five-year record shows exceptional operating leverage, but it also shows how concentrated the source of that leverage has been. FMP's Financial Ratios API tracks operating and EBITDA margins year by year, and following those margins as the new framework takes effect will show whether pricing power erodes gradually, holds, or is offset by growth in the software business.
Tenet Healthcare Corporation (THC)
5-Year Revenue CAGR: 3.85%
5-Year EBITDA CAGR: 12.12%
Tenet's revenue barely grew over five years, yet EBITDA compounded at more than three times that rate. The explanation is a deliberate change in the company's composition. Tenet sold a number of hospitals while expanding United Surgical Partners International, its ambulatory surgery business, which has become the largest operator of its kind in the country. Outpatient surgery carries higher margins and requires less capital than inpatient hospital care, so shifting the mix lifts profitability even when total revenue is flat.
One caution applies to the period in between. Tenet recorded a large gain from hospital sales in 2024, which falls inside the window but not at either endpoint, so it does not distort the CAGR calculation. It does mean that some of the capital supporting expansion came from divestitures rather than from operations. FMP's Revenue Product Segmentation API separates hospital operations from ambulatory care, which is the most direct way to confirm that the higher-margin segment continues to grow its share of revenue as policy changes affect hospital reimbursement.
Monolithic Power Systems, Inc. (MPWR)
5-Year Revenue CAGR: 27.00%
5-Year EBITDA CAGR: 34.47%
Monolithic Power is the one company in the group where the spread sits on top of very rapid revenue growth. Revenue more than tripled over five years as the company expanded from consumer and industrial power chips into automotive and, more recently, power delivery for AI data center hardware. EBITDA grew faster still, indicating that the higher-value products it now sells are lifting margins as well as volumes. Management raised its expectations for enterprise data revenue growth again this summer.
The smaller spread relative to ServiceNow or Fair Isaac reflects the business model. A chip company that designs its own products but relies partly on outside manufacturing has more variable cost than a software company, so margin gains come more gradually. FMP's Key Metrics TTM API provides return on invested capital and margin data on a trailing basis, which helps show whether the shift toward data center products continues to lift returns or whether competition and customer concentration begin to weigh on them.
What the Five Spreads Reveal About Earnings Quality
The five companies share an outcome, EBITDA growing faster than revenue, but they reached it in different ways. ServiceNow and Monolithic Power scaled businesses where incremental revenue costs relatively little to deliver. Fair Isaac used pricing power in a near-monopoly product. First Solar benefited from a policy designed to support domestic manufacturing. Tenet restructured its portfolio toward a higher-margin activity. Each is legitimate operating leverage, but they differ sharply in how dependent they are on conditions that could change.
That is the most useful lens for reading this screen. Fair Isaac's week illustrates how quickly a margin advantage built on a single source can be challenged. First Solar's exposure is similar in structure, while Tenet's improvement is more embedded because it reflects what the company now owns. ServiceNow's spread partly reflects accounting for equity compensation, and Monolithic Power's reflects product mix within a cyclical industry. The CAGR figures identify where margins expanded; the source of the expansion determines how much weight the signal deserves.
Testing that source requires more than an income statement. Within the broader financial data available on the FMP platform, the Income Statement Bulk API surfaces the spread across a wide universe, while the Cash Flow Statement API shows whether EBITDA gains are converting into operating and free cash flow. For companies like ServiceNow, cash flow data also isolates stock-based compensation, which helps separate operating improvement from accounting effects.
Expectations provide the final check. The Income Statement Growth API shows whether the margin trend is still accelerating or has started to flatten in the most recent years, and the Financial Estimates API indicates whether analysts expect it to continue. The Price Target Consensus API adds a sense of how much of the improvement is already reflected in valuations. When those datasets agree, the spread is strong evidence of durable operating leverage; when they diverge, as they may now for Fair Isaac, the historical record becomes a measure of what is at stake rather than a guide to what comes next.
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.
Tracking Where Operating Leverage Comes From Over Time
Five years of faster profit growth tells you a margin expanded; it does not tell you whether the reason for it will still be in place next year. Refreshed through FMP's Income Statement API and Income Statement Bulk API, the same screen shows which of these spreads hold up as conditions around each company change.
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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.
