This week's earnings scan produced five streaks of very different vintage: Eaton at 26 consecutive quarters, Nvidia at 14, TJX and Chubb at 13 each, and Intercontinental Exchange at 6. Lined up that way, the obvious reading is that longer is better. The quarters behind the numbers suggest something closer to the opposite, because the longest streaks are also the ones where consensus has had the most time to learn the pattern.
Using FMP's Earnings Surprises Bulk API, this article identifies the streaks, then examines what each most recent beat was actually made of.
Key Takeaways
- Streak length and beat quality are independent. Eaton's twenty-sixth beat cleared its own guidance midpoint by ten cents, while the sixth in Intercontinental Exchange's run was two cents on in-line revenue.
- Nvidia's case shows the natural end state of a long streak: consensus for its next quarter now sits above the company's own revenue guidance, so the estimate set has already absorbed the pattern.
- Chubb's streak is currently margin-driven rather than volume-driven, with underwriting income growing several times faster than premiums written.
Five Streaks That Have Outlasted the Estimates
Eaton Corporation plc (ETN)
Beat Streak: 26 quarters. Next quarterly report: Nov. 3 — EPS: $3.53; Revenue: $8.44B (consensus).
Twenty-six consecutive quarters covers a pandemic, an inflation shock and a full rate cycle, which is long enough that the interesting question stops being whether Eaton clears the estimate and becomes what keeps producing the gap. The latest quarter answers that fairly directly. Revenue of $8.5 billion grew 21%, organic growth of 14% came in above the guided range, and adjusted earnings per share of $3.15 beat the company's own midpoint by ten cents. Management raised full-year adjusted EPS guidance to $13.50 and lifted the organic growth outlook by 200 basis points.
The mechanism sits in the order book rather than the income statement. Electrical Americas produced a book-to-bill ratio of 1.3 with rolling twelve-month orders up 41%, which means the backlog is being refilled faster than it is being shipped even while shipments are growing strongly. Data centre revenue within that segment rose roughly 65%, and management put the US data centre pipeline at 307 gigawatts, a figure they framed as about fifteen years of construction at 2025 build rates. Segment margins expanded 190 basis points sequentially to 27.5% at the same time, so volume and profitability moved together rather than trading against each other.
The variable to watch is what happens as capacity arrives. Eaton is deploying more than $1 billion across roughly two dozen projects in Electrical Americas alone, and capacity investment of that scale typically weighs on margins before it contributes to them. A streak this long also creates its own hazard: after twenty-six quarters, an estimate stops being an independent forecast and starts becoming an echo of company guidance. FMP's Financial Statement Growth API is the useful reference here because it puts revenue, operating income and cash flow growth on the same multi-year footing, which is what shows whether margin expansion survives the build-out phase.
NVIDIA Corporation (NVDA)
Beat Streak: 14 quarters. Next quarterly report: Aug. 26 — EPS: $2.08; Revenue: $91.91B (consensus).
Nvidia's fourteen-quarter run has occurred in the most heavily modelled stock in the market, which makes its current position the most instructive detail in this week's screen. The company guided to $91.0 billion of revenue for the quarter it reports on Aug. 26, plus or minus 2%. Consensus sits at $91.91 billion. Analysts have collectively moved above the company's own midpoint, which is what happens when a beat streak becomes an assumption: the surprise has been priced into the estimate rather than removed from the business.
The quarter that produced the fourteenth beat was not marginal. Revenue reached $81.6 billion, up 20% sequentially and 85% year over year, with data centre revenue of $75.2 billion growing 92%. Gross margin held at 75% on a non-GAAP basis. The less-discussed figure is the composition: compute revenue grew 77% while networking grew 199%, so the fastest-growing part of the business is now the interconnect layer rather than the accelerators themselves. Capital returns scaled alongside, with roughly $20.0 billion returned in the quarter, a further $80.0 billion repurchase authorisation approved, and the quarterly dividend raised from $0.01 to $0.25.
Shares fell after that report regardless, which is consistent with a market that has stopped rewarding the beat and started underwriting the next one. That is the analytically relevant condition rather than a judgement about the business. When estimates sit above guidance, a result that lands in line with what the company told the market to expect registers as a shortfall against the number analysts published. FMP's Financial Estimates API is where that gap is visible, since it shows whether forward revenue and EPS estimates are still trailing the company's own outlook or have moved ahead of it.
The TJX Companies, Inc. (TJX)
Beat Streak: 13 quarters. Next quarterly report: Aug. 19 — EPS: $1.19; Revenue: $15.19B (consensus).
TJX has cleared consensus for thirteen straight quarters in a retail environment that has been anything but uniform, and the structural reason is worth stating because it explains the persistence. Off-price retail draws its inventory from other retailers' misjudgements, so the conditions that compress margins across full-price apparel tend to improve TJX's buying opportunity. The streak is partly a function of a business model that is countercyclical to its own sector rather than of consistently accurate forecasting.
The most recent quarter showed both halves of the model working. Comparable sales grew 6%, pretax profit margin reached 12.0%, and diluted earnings per share of $1.19 rose 29% year over year, with management describing all three as well above plan. Revenue of $14.3 billion supported net income of roughly $1.3 billion. The company then raised full-year guidance across comparable sales growth, pretax margin, earnings per share and share repurchases simultaneously, which is a broader revision than a single strong quarter usually justifies.
The tension in a streak built on comparable sales is arithmetic. Comps compound, so each successive 6% is measured against a larger base, and the margin gains that accompanied this quarter make the following year's comparison harder rather than easier. Consensus for the Aug. 19 report sits at $1.19, matching the quarter just reported, which implies analysts expect the earnings level to hold rather than extend. FMP's Income Statement API is the cleanest way to follow this, since the question is whether pretax margin holds at the raised level once the comparison base resets, and margin is where a streak like this usually ends.
Chubb Limited (CB)
Beat Streak: 13 quarters. Next quarterly report: Oct. 27 — EPS: $6.31; Revenue: $15.56B (consensus).
Chubb's thirteen-quarter run reflects an underwriting result that has been running well ahead of the industry's long-run average. Second-quarter core operating income per share of $7.26 rose 18.2%, net income per share reached $7.30, and the property and casualty combined ratio came in at 83.8%, meaning roughly sixteen cents of underwriting profit on every premium dollar before investment income is counted. Underwriting income rose 18.8% to $1.94 billion.
The composition is where the caution belongs. Consolidated net premiums written grew 3.6%, with property and casualty up 3.0% and life up 7.5%, so underwriting income grew at more than five times the rate of the premium base. That gap is margin, not volume, and margin of that kind is a function of loss experience and pricing that were both favourable in the period. Neither is a variable the company fully controls, and property and casualty results carry catastrophe exposure that is lumpy by nature rather than trending.
That does not diminish the record, but it does define what the streak is currently resting on. A combined ratio in the low eighties is difficult to improve from, so continued beats would need either premium growth to accelerate or investment income to carry more of the result. The consensus for Oct. 27 of $6.31 sits well below the $7.26 just delivered, which suggests analysts are already assuming some normalisation rather than extrapolating the quarter. FMP's Cash Flow Statement API is a useful complement to the underwriting figures, because it shows whether operating cash generation is tracking reported earnings or diverging from it as reserves and claim payments move.
Intercontinental Exchange, Inc. (ICE)
Beat Streak: 6 quarters. Next quarterly report: Oct. 29 — EPS: $1.89; Revenue: $2.69B (consensus).
Intercontinental Exchange has the shortest streak in the group and the thinnest most recent beat, which makes it the right control case for the whole screen. Adjusted earnings per share of $1.90 cleared the $1.88 estimate by two cents on net revenue of $2.67 billion that came in line with expectations.
The underlying business explains why small, regular beats are plausible here. Recurring revenue reached a record $1.35 billion, up 8% in constant currency, and the segments each contributed: exchange data and connectivity hit a record $287 million on 12% growth, fixed income and data services grew 8% with its adjusted operating margin expanding two points to 46%, and mortgage technology rose 5% to $557 million with closing solutions up 14%. A revenue base that recurring is inherently more forecastable, which produces reliable but modest surprises rather than large ones.
The material change is the announced acquisition of MarketAxess at $167 per share in cash, a $5.7 billion enterprise value financed entirely with new debt. That takes gross leverage to roughly 3.4 times at close, against a stated intention to return to 3.0 times or below within eighteen to twenty-four months, with around $100 million of run-rate cost synergies targeted over three years. Shares declined on the print as investors weighed the spending, even with year-to-date adjusted free cash flow up 28% to $2.6 billion. FMP's Revenue Product Segmentation API is the relevant dataset going forward, since integration will change the segment mix and a consolidated revenue line will not show whether the recurring base that supports the streak is still growing on its own terms.
What a Streak Measures, and What It Doesn't
Counting consecutive beats treats every quarter as one unit, which is exactly what makes the metric easy to compute and easy to over-read. This week's five make the point without needing much interpretation. Eaton's twenty-sixth beat cleared the company's own guidance midpoint by ten cents on organic growth that exceeded the guided range. Intercontinental Exchange's sixth was two cents on revenue that landed in line. Both increment the same counter. Only one describes a business outrunning its own plan.
The correction is to measure the beat against guidance rather than against consensus. Consensus is a moving target that adjusts toward whatever a company has recently done, so a beat against consensus can mean the business improved or simply that estimates lagged. Guidance is the company's own statement of what it expected, which makes the gap to guidance a cleaner read on operating outperformance. Nvidia illustrates why this matters at the far end of a streak: with consensus for the next quarter sitting above the company's guidance midpoint, the estimate set has already absorbed the pattern, and a result exactly in line with plan would be recorded as a miss. Streaks do not usually end because a business deteriorates. They end because expectations converge on the behaviour.
The second question is where in the statements the beat originated, and that is answerable with reported data rather than inference. The Income Statement API and Cash Flow Statement API together separate an operating beat from one produced by tax rate, share count or expense timing, which is the distinction that determines whether the run is likely to continue. The Financial Statement Growth API adds the longer view needed to tell a genuine multi-year trajectory from a favourable comparison base, which is the specific check Eaton's twenty-six-quarter record calls for. Within the broader FMP platform, those pulls resolve against the same timestamp as the surprise history itself, so the comparison does not have to be reconciled across separately maintained sources.
The last layer is whether anyone is adjusting. The Financial Estimates API shows whether forward revenue and EPS forecasts are still trailing reported results or have moved past them, and the Historical Stock Grades API and Price Target Summary API show whether the research community is revising in the same direction. The informative case is divergence: a company can keep clearing quarterly estimates while targets flatten and ratings soften, which usually means the market has already repriced the consistency and is now underwriting something further out. A beat streak is best treated as a question about how fast expectations learn, not as evidence that they have not.
Building a Repeatability Screen with FMP Data
Screening for consistent earnings outperformance works best when you start from the entire dataset rather than a curated list of names. Pre-selecting companies introduces bias upfront—especially when the objective is to uncover patterns that may not be obvious within familiar coverage. A cleaner approach is to begin with the full distribution of earnings outcomes and let the filtering process surface repeat performers on its own.
The FMP Earnings Surprises Bulk API is the practical entry point for that process. It standardizes quarterly EPS results against consensus estimates across a broad equity universe, which makes it possible to scan for surprise patterns without needing to stitch together multiple sources. Before running any calls, the only requirement is a valid API key.
1. Pull Bulk Earnings Surprises
Begin by hitting the Earnings Surprises Bulk API, which aggregates every quarterly EPS surprise — positive or negative — for the year you specify:
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https://financialmodelingprep.com/stable/earnings-surprises-bulk?year=2025&apikey=YOUR_API_KEY |
Sample Response:
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[ { "symbol": "AMKYF", "date": "2025-07-09", "epsActual": 0.3631, "epsEstimated": 0.3615, "lastUpdated": "2025-07-09" } ] |
From here, the first cut is mechanical: isolate the entries where epsActual > epsEstimated. That gives you the universe of names that beat expectations at least once during the period — essentially a raw pool before you evaluate whether any of them can deliver that result consistently.
2. Retrieve Company-Level Details
With that universe in hand, the analysis moves from identifying events to evaluating consistency. For each ticker that cleared the first filter, pull its complete quarterly earnings history using the Earnings Report API:
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https://financialmodelingprep.com/stable/earnings?symbol=AAPL&apikey=YOUR_API_KEY |
Looking at the complete sequence of reported quarters makes it possible to evaluate frequency and clustering. This is where judgment enters the workflow. Some analysts require three or more consecutive beats to qualify as a streak; others impose minimum surprise thresholds or remove near-zero deviations. The parameters can be adjusted, but the intent stays the same: separate sustained execution from statistical noise.
By this point, the screen has moved beyond identifying isolated surprises. What began as a broad event scan turns into a structured assessment of earnings reliability, highlighting companies where internal forecasting and operational control have proven more consistent than the market's expectations over time.
Scaling the Screen: Testing the Signal Across Broader Coverage
A repeatability screen earns its credibility not by how many companies it includes, but by how well it holds up as the data environment becomes less predictable. The most effective way to test that is to widen the scope in stages—starting with tightly modeled segments and moving outward into areas where estimates are looser and coverage is thinner. The goal is simple: see if the signal survives as conditions become less controlled.
The first pass sits within the Free plan, where the dataset is anchored in widely covered large-cap names like Apple Inc., Alphabet Inc., and JPMorgan Chase & Co.. This is the most efficiently modeled part of the market—analyst coverage is dense, estimates tend to cluster, and expectations are generally well-defined. If a streak-based approach produces consistent outputs here, it suggests the framework is functioning on stable ground rather than reacting to noise in the data.
Moving into the Starter plan expands the universe to include a wider range of U.S. equities, bringing in smaller-cap and more specialized companies. At this level, estimate dispersion becomes more visible as coverage thins out. That shift acts as a natural pressure test. When the same criteria continue to surface repeat performers, the pattern begins to look less dependent on tightly managed forecasts and more tied to actual operating consistency.
The final step, via the Premium plan, extends the screen across international markets such as the U.K. and Canada. The methodology remains unchanged, but the inputs become more varied. Differences in reporting conventions, sector mix, and estimate reliability introduce additional complexity. Applying the same framework across these regions helps validate that the signal—earnings consistency—remains comparable even as the underlying data becomes more heterogeneous.
From Analyst Workflow to Standardized Research Framework
The transition from individual screening model to firm-wide research infrastructure usually begins quietly. An analyst builds a process to track recurring earnings surprises, another team starts referencing the outputs, and over time the methodology becomes embedded in broader coverage discussions. What starts as a desk-level workflow gradually turns into a standardized framework for evaluating operational consistency across sectors and market regimes.
That evolution is rarely driven by top-down mandates alone. In practice, the strongest push toward standardization tends to come from analysts closest to the data — the people refining filters after each earnings cycle, stress-testing assumptions against new quarters, and identifying where consensus models repeatedly fail to adjust fast enough. Once a workflow demonstrates that it can consistently surface meaningful signals, the inefficiencies of fragmented research processes become harder to ignore.
Most firms already recognize the symptoms: multiple teams maintaining slightly different versions of the same screen, inconsistent threshold definitions across sectors, isolated spreadsheets with limited transparency, and duplicated effort spent reconciling conflicting outputs. Over time, those inconsistencies become operational friction. They slow collaboration, complicate audit trails, and make it difficult to determine whether differences in conclusions come from actual analytical disagreement or simply from variations in methodology.
A standardized framework changes the conversation from “who built the model” to “how the organization defines the signal.” Shared dashboards replace disconnected files. Screening criteria become reviewable rather than buried inside private workflows. Inputs, revisions, and assumptions can be tracked across teams, creating clearer governance around how data-driven conclusions are formed. For research groups operating across multiple sectors or geographies, that consistency becomes increasingly important as workflows expand beyond the analysts who originally designed them.
At that stage, the challenge is no longer discovering the signal — it is preserving methodological consistency as adoption widens across the organization. Centralized infrastructure such as the FMP Enterprise plan becomes relevant not because it changes the analytical framework itself, but because it helps maintain unified data access, version control, and cross-team visibility once the workflow moves beyond a single desk. The objective is continuity: ensuring that the same earnings-consistency signal is being measured the same way across the broader research platform.
Reading the Next Print Against the Right Benchmark
Four of these five report before the end of October, and in each case the more informative comparison will be against what the company guided to rather than against the number consensus settled on. Running the Earnings Surprises Bulk API each cycle keeps the streak count current, which is what makes it possible to see the moment a streak stops being a surprise and starts being an expectation.
Want more? Explore our earlier article: Weekly Signals Desk | Concentrated Analyst Revisions via the FMP API (July 27-31)
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.
