Not every improvement in corporate performance starts with accelerating sales. In many cases, the more important shift happens beneath the revenue line, where operating leverage begins to compound and profitability expands at a faster rate than top-line growth. When that pattern emerges across multiple companies in different industries at the same time, it often signals a broader change in business efficiency rather than a company-specific event.
This week, a screen built using FMP's Income Statement API surfaced five companies where five-year EBITDA growth materially outpaced revenue CAGR. The names span mining, aerospace, refining, and energy, yet each exhibited the same underlying characteristic: earnings growth accelerating faster than sales over a multi-year period. In this article, we examine those results, explore what the divergence between revenue and EBITDA growth may be signaling, and show how the same screening process can be replicated using FMP's Income Statement API.
Key Takeaways
- Five companies from four different industries exhibited the same underlying pattern: EBITDA growth compounded materially faster than revenue growth over a five-year period, suggesting improving operating leverage beneath the surface.
- The largest gaps between revenue CAGR and EBITDA CAGR appeared in HF Sinclair and Talos Energy, highlighting how changes in profitability can significantly outpace top-line expansion under the right operating conditions.
- Revenue growth alone does not fully explain the results. In each case, the data points toward a combination of margin expansion, cost discipline, portfolio optimization, or improved asset-level economics contributing to earnings growth.
- Using FMP's Income Statement API, the screen demonstrates how a simple CAGR framework can identify companies where business efficiency appears to be improving faster than sales growth, creating a useful starting point for deeper fundamental analysis.
Five Companies Showing a Clear Profitability Inflection
Newmont Corporation (NEM)
5-Year Revenue CAGR: 16.18%
5-Year EBITDA CAGR: 33.95%
The gap between Newmont's revenue growth and EBITDA growth stands out because it suggests that earnings expansion has not been driven solely by higher gold prices. While commodity pricing has undoubtedly provided a tailwind, the magnitude of the EBITDA CAGR indicates that operational factors have also contributed to profitability gains. When EBITDA compounds at more than double the pace of revenue over a multi-year period, it often points toward a combination of portfolio optimization, cost discipline, and improved asset-level economics.
That interpretation aligns with several developments inside the business over the past two years. Following the acquisition of Newcrest and the subsequent divestiture of non-core assets, management has focused on concentrating capital around higher-quality operations while reducing balance-sheet complexity. More recently, Newmont has reported strong free cash flow generation despite periods of lower production volume, highlighting the degree to which realized pricing and operating efficiency have supported earnings performance. Recent quarterly results also showed continued attention to cost management and capital allocation even as production fluctuated across several mining regions.
For investors analyzing whether this operating leverage signal remains intact, the income statement alone tells only part of the story. Production metrics, realized commodity pricing, all-in sustaining costs, and capital expenditure trends provide additional context around whether EBITDA expansion is coming from stronger asset quality, favorable pricing conditions, or a combination of both. Looking beyond revenue growth and into cost structure data often provides the clearest picture of what is actually driving profitability.
RTX Corporation (RTX)
5-Year Revenue CAGR: 10.81%
5-Year EBITDA CAGR: 22.60%
RTX presents a different version of the same signal. Unlike commodity-linked businesses where earnings can be heavily influenced by pricing cycles, aerospace and defense profitability tends to reflect execution, backlog conversion, manufacturing efficiency, and program mix. Revenue has grown at a healthy pace over the last five years, but EBITDA has expanded more than twice as fast, suggesting that incremental sales have become increasingly profitable.
What makes the spread noteworthy is the environment in which it occurred. RTX has spent the past several years navigating supply-chain disruptions, engine-related challenges, and shifting defense procurement cycles. Despite those pressures, profitability growth has outpaced top-line expansion, indicating that operating improvements have offset a portion of those headwinds. In large industrial businesses, sustained divergence between revenue growth and EBITDA growth often reflects a business becoming structurally more efficient rather than simply benefiting from a strong sales cycle.
For readers evaluating this trend, backlog data, segment-level operating margins, and cash flow statements may offer more insight than revenue figures alone. Aerospace businesses frequently report strong sales while margins remain under pressure; the opposite pattern is often more informative. When EBITDA compounds materially faster than revenue over an extended period, it suggests that operating performance deserves as much attention as order growth.
Gold Fields Limited (GFI)
5-Year Revenue CAGR: 16.54%
5-Year EBITDA CAGR: 23.08%
Gold Fields generated a narrower spread between revenue and EBITDA growth than some of the other companies in this screen, but the signal remains meaningful. Revenue compounded at a robust rate over five years, while EBITDA expanded even faster, indicating that earnings growth was not entirely dependent on higher commodity prices. In mining businesses, maintaining profitability growth ahead of revenue growth often reflects improvements in mine planning, cost management, production mix, or operational consistency across the portfolio.
The distinction matters because gold producers frequently face inflationary pressures from labor, energy, equipment, and sustaining capital requirements. In many cases, rising gold prices lift revenue while margins remain constrained by higher operating costs. Gold Fields' EBITDA trajectory suggests that at least part of the value created over the period came from managing those pressures rather than merely benefiting from the commodity environment.
Additional context typically comes from examining production costs, reserve replacement metrics, and mine-level operating performance alongside financial results. Revenue growth may show how favorable the environment has been for the sector, but EBITDA growth often reveals which operators have translated that backdrop into stronger business economics. That difference is particularly important when comparing companies operating within the same commodity group.
HF Sinclair Corporation (DINO)
5-Year Revenue CAGR: 23.48%
5-Year EBITDA CAGR: 92.26%
HF Sinclair produced one of the widest spreads in the entire screen. Revenue grew at an impressive annualized rate over five years, yet EBITDA expanded nearly four times faster. When profitability compounds at that pace relative to sales, the data typically points toward a business that experienced a significant shift in margin structure rather than simply an increase in activity levels.
Refining businesses are especially interesting through this lens because revenue can often be a misleading indicator of underlying performance. Refiners process large volumes of product where profitability is heavily influenced by crack spreads, feedstock costs, utilization rates, and operational efficiency. As a result, modest changes in refining economics can create disproportionately large movements in EBITDA. The scale of the spread observed here suggests that margin dynamics likely played a larger role than top-line growth alone.
For investors studying whether the signal remains durable, refining margin trends, utilization rates, and segment-level operating income are often more informative than aggregate revenue figures. Income statement data highlights the outcome, but industry-specific operating datasets help explain why the outcome occurred. The relationship between those metrics often reveals whether profitability gains stem from cyclical conditions, operational improvements, or both.
Talos Energy Inc. (TALO)
5-Year Revenue CAGR: 27.99%
5-Year EBITDA CAGR: 101.77%
Talos Energy recorded the strongest EBITDA growth rate in this group by a considerable margin. Revenue compounded at nearly 28% annually over five years, yet EBITDA expanded at more than 100% annually over the same period. Few screens capture operating leverage more clearly than that type of divergence. The numbers indicate that profitability improved dramatically relative to the pace of sales growth.
In upstream energy businesses, this pattern can emerge when production growth, asset optimization, acquisition integration, and commodity pricing align over the same period. The key observation is not simply that EBITDA increased; it is that earnings growth accelerated much faster than revenue growth. That suggests a substantial change in how efficiently the company converted revenue into operating profit over time.
The next layer of analysis typically involves production volumes, realized commodity prices, lifting costs, and reserve development activity. Revenue trends can show how favorable the operating environment has been, but EBITDA trends often reveal how effectively management has translated that environment into financial performance. When the spread becomes this large, examining operational datasets alongside the income statement helps determine whether the improvement reflects temporary market conditions, structural cost changes, or a combination of both.
The Signal Beneath the Growth Numbers
The common thread across these five companies is not industry exposure, commodity sensitivity, or market capitalization. A gold miner, a defense contractor, a refiner, and an independent energy producer rarely appear in the same screen for the same reason. What connects them here is a measurable improvement in how efficiently revenue has been converted into operating earnings over time.
That distinction matters because revenue growth is often the metric that attracts the most attention, yet it rarely tells the full story. Two companies can generate identical top-line growth rates while producing dramatically different financial outcomes underneath. The spread between revenue CAGR and EBITDA CAGR helps isolate that difference. When EBITDA compounds materially faster than sales over multiple years, it suggests that some combination of operating leverage, cost discipline, portfolio optimization, asset quality improvements, or margin expansion has been contributing to results.
Importantly, this is not automatically a bullish signal, nor does it imply that the same relationship will persist indefinitely. Rather, it serves as a starting point for deeper investigation. The screen identifies where operating performance has improved faster than business volume. The more useful question is what underlying factors are responsible for that divergence and whether the improvement is showing up consistently across other datasets.
That is where combining multiple sources of information becomes valuable. Historical income statement data can identify the initial signal, but understanding its source often requires examining additional layers of the business. Comparing EBITDA expansion against free cash flow trends, balance sheet changes, and margin development can help distinguish between profitability driven by genuine operational improvement and profitability driven primarily by external conditions. In practice, many research workflows begin with financial statement data and then expand outward, using platforms such as Financial Modeling Prep to connect multiple datasets into a broader analytical framework.
For example, a company showing unusually strong EBITDA growth may warrant a review of cash flow statements to determine whether earnings gains are translating into cash generation. Balance sheet data can reveal whether profitability improvements are occurring alongside rising leverage or strengthening financial flexibility. Analyst estimate revisions and price target changes add another layer of context, helping determine whether improving business economics are already reflected in consensus expectations or remain primarily visible in the underlying financial statements. Insider trading activity can provide further perspective when evaluated alongside operating performance rather than viewed in isolation.
Viewed together, these datasets often tell a more complete story than any individual metric. The objective is not simply to identify companies with the highest growth rates, but to find situations where multiple sources of evidence point toward improving business economics. Revenue growth may capture attention, but the relationship between revenue growth and profitability growth frequently reveals more about the quality of that expansion.
That broader perspective is what makes the five companies in this screen noteworthy. The signal is not that they operate in the same sector or face the same market conditions. It is that each has demonstrated a period where profitability expanded materially faster than revenue. Whether driven by operational execution, portfolio restructuring, commodity cycles, or margin improvement, the pattern highlights a group of businesses where earnings power has strengthened at a faster pace than top-line growth alone would suggest. For analysts searching for evidence of improving operating efficiency, that divergence is often one of the first places worth investigating.
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 single screen can highlight where operating leverage has already emerged, but the more useful exercise is tracking how those relationships evolve over time. By regularly monitoring the same profitability and growth metrics through the Income Statement API and Income Statement Bulk API, what begins as a static snapshot becomes an ongoing framework for identifying shifts in business efficiency as they develop.
If you found this useful, you might also like: Weekly Signals Desk | Five Notable Valuation Disconnects from the FMP API (May 25-29)
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.
