Signals Desk Weekly Take via FMP API | Five Companies With Persistent Earnings Beats (Aug 31-Sept 4)

The longest streaks this scan produced belong to companies that were structurally rearranged while the streak was running. GE Aerospace has been separated twice inside its twenty-two quarters. Broadcom's per-share history crosses a ten-for-one split. Microsoft is deliberately moving revenue from per-seat licensing toward consumption billing. A beat streak measures the distance between what a company delivers and what consensus expected, and that distance is widest when the business is changing faster than the models tracking it.

This edition uses FMP's Earnings Surprises Bulk API to identify the streaks, then works through what each one is actually measuring, because five records of similar length turn out to have five different causes.

Key Takeaways

• The five streaks run from ten to twenty-two consecutive quarters, and none of the five is a straightforward story about forecasting discipline.
• Two of them, Broadcom and Moody's, are largely streaks of forecast difficulty: revenue nearly doubling in a quarter and issuance activity that management said was pulled forward are not inputs anyone models precisely.
• Intuitive Surgical is the outlier, and the most interesting case: its inputs are unusually observable, which makes fourteen straight beats a statement about consensus underestimating a compounding recurring base.
• Broadcom's recorded run stops at seventeen quarters purely because one pre-split estimate was never adjusted for its ten-for-one split. Any streak screen run at scale needs a restatement check or it will truncate the longest records it exists to find.

The Five Longest Runs on This Week's Scan

GE Aerospace (GE)

Beat Streak: 22 quarters.
Next quarterly report: Oct. 20 — EPS: $1.99; Revenue: $12.83B (consensus).

GE Aerospace has cleared consensus every quarter since an exactly in-line print in the fourth quarter of 2020, and the run has survived being taken apart twice: the GE HealthCare separation in early 2023 and the GE Vernova separation in 2024. The earnings base changed underneath the streak on both occasions, and each quarter still beat the estimate standing at the time. That is a more demanding record than the raw number suggests, because a restructuring normally resets analyst models in the company's favour for a quarter or two and against it thereafter.

The second quarter, reported on 16 July, extended the pattern comfortably. Orders rose 17% to $16.5 billion, adjusted revenue grew 24% to $12.6 billion, adjusted earnings per share increased 22% to $2.02, and free cash flow climbed 43% to $3.0 billion. Commercial Engines and Services carried most of it, with revenue up 27% to $9.7 billion, services revenue up 32% year to date and LEAP deliveries up 41%. Management raised full-year guidance for that segment twice over, lifting revenue growth to around 20% from mid-teens and operating profit to $10.25 billion to $10.35 billion from a prior $9.6 billion to $9.9 billion range.

The structural reason the streak persists is worth naming. An aftermarket-heavy engine business earns most of its profit from shop visits on an installed base, and the manufacturer sees that schedule earlier and more precisely than anyone modelling it from outside. The signal is therefore not that GE beats; it is that consensus repeatedly under-models services conversion. FMP's Revenue Product Segmentation API is the natural place to test that, since the equipment-versus-services split shows whether the beats are coming from deliveries or from the aftermarket. Shop visit output, spare parts mix, and whether the certified LEAP durability kit changes time-on-wing assumptions are the items that would alter the model rather than the quarter.

Broadcom Inc. (AVGO)

Beat Streak: 17 quarters.
Next quarterly report: Dec. 10 — EPS: $3.83; Revenue: $34.72B (consensus).

Broadcom reported on 2 September, inside the week this screen covers, and the numbers were not close. Revenue rose 86% to $29.6 billion. AI semiconductor revenue reached $16.7 billion, up 221% year over year. Infrastructure software added $8.8 billion, up 29%. Non-GAAP operating income nearly doubled to $20.1 billion, non-GAAP earnings per share rose 96% to $3.32, and free cash flow grew 95% to $13.7 billion. Fourth-quarter guidance points to roughly $34.8 billion of revenue with AI semiconductor revenue near $21.7 billion.

Two things follow from that. The first is a data caveat with a general lesson attached. The recorded streak stops at seventeen quarters only because the surprise history contains a May-2022 estimate of $8.71 against a $0.91 actual, an artefact of the ten-for-one split that was never applied to the pre-split estimate. Adjusted, that quarter also cleared, and the true run is longer. A mechanical screen reads it as a miss.

The second is that a streak means less when growth is this violent. Beating an estimate by 3% on a revenue base that nearly doubled is a much weaker claim about forecasting discipline than beating by 3% on a flat base, because nobody models an 86% revenue increase precisely enough for the residual to be informative. The streak here is substantially a byproduct of the growth rate rather than evidence that management guides conservatively. FMP's Income Statement Growth API frames that correctly, placing revenue, operating income and free cash flow growth side by side so the size of the beat can be read against the size of the move it sits inside. The measure that would change the interpretation is a quarter where growth decelerates and the beat survives anyway.

Microsoft Corporation (MSFT)

Beat Streak: 16 quarters.
Next quarterly report: Oct. 28 — EPS: $4.67; Revenue: $90.59B (consensus).

Microsoft's run began after the June-2022 quarter, when $2.23 came in against a $2.29 estimate, and has held through sixteen consecutive reports since. The fiscal fourth quarter, released on 29 July, delivered roughly $90 billion of revenue and $4.74 of adjusted earnings per share against a $4.24 consensus, closing a year at $331 billion of revenue, up 18%, with Microsoft Cloud at $214 billion, up 27%, and operating income above $155 billion. Azure crossed $100 billion in annual revenue and accelerated to 43% growth in the quarter, with management attributing part of that to efficiency gains across the CPU and GPU fleet rather than to added capacity.

Not everything moved the same way. Xbox content and services revenue fell 10%, Windows OEM revenue declined 5%, and management guided OEM and Devices down in the high teens for fiscal 2027 on softer PC demand and higher component costs. Gross margin was lower year over year. Those offsets matter to the streak because they are the predictable parts of the business; the growth is concentrated in the parts that are not.

The most analytically interesting development is the billing shift. Microsoft 365 Copilot passed 30 million paid seats with net additions more than doubling sequentially, and the company is layering usage-based billing on top of per-seat licensing. Consumption revenue is materially harder to forecast one quarter ahead than a subscription base, so a company moving in that direction is deliberately making its own results less predictable. Sixteen quarters of beats through that transition indicates the forecasting error has been consistently one-directional. FMP's Financial Estimates API is where that gets tested, since it shows whether forward revenue and EPS estimates have begun to catch up or continue to trail the delivered numbers.

Intuitive Surgical, Inc. (ISRG)

Beat Streak: 14 quarters.
Next quarterly report: Oct. 20 — EPS: $2.61; Revenue: $2.91B (consensus).

Intuitive Surgical is the outlier in this group and, for that reason, the most instructive entry. Its inputs are about as observable as a public company's get. The installed base is disclosed and grew 12% to 11,710 da Vinci systems, with Ion up 21% to 1,096. Procedure volumes are reported, with worldwide procedures up 16% in the second quarter, da Vinci up 15% and Ion up 36%. Recurring revenue dominates the mix: instruments and accessories contributed $1.73 billion, up 18%, and services $472 million, up 21%, against $685 million of systems revenue. Total revenue rose 19% to $2.89 billion, non-GAAP earnings per share rose 28% to $2.80, and GAAP gross margin improved 150 basis points to 67.8%.

Fourteen consecutive beats in a business with that much visible structure is harder to explain away than the same record at Broadcom. The likeliest reading is that consensus keeps underestimating the second derivative rather than the level. Every system placed, and 468 da Vinci units went out in the quarter including 246 of the newer da Vinci 5, adds a machine that generates procedures and consumables for years, so the recurring base compounds faster than the placement count on its own implies. Models anchored on unit shipments will systematically trail models anchored on installed-base utilisation.

The near-term complication is cost rather than demand. Full-year guidance calls for da Vinci procedure growth of roughly 13.5% to 15.5% and non-GAAP gross margin of 68.0% to 69.0%, with about a point of that absorbed by tariffs. FMP's Financial Ratios API tracks exactly that pressure, since a multi-quarter gross and operating margin series will show whether the tariff effect is being offset by mix and scale or is settling into the cost base. Placement mix toward da Vinci 5, procedure growth by geography, and lease versus purchase structure are the variables underneath it.

Moody's Corporation (MCO)

Beat Streak: 10 quarters.
Next quarterly report: Oct. 28 — EPS: $4.26; Revenue: $2.07B (consensus).

Moody's has cleared consensus for ten straight quarters since a December-2023 miss, and the most recent result, reported on 22 July, was the largest of the run. Revenue rose 15% to $2.19 billion, adjusted earnings per share increased 31% to $4.68, and adjusted operating margin expanded 440 basis points to 55.3%. The ratings business did the heavy lifting: Moody's Investors Service revenue grew 25% to $1.26 billion on rated issuance of $2.059 trillion, up 33%, with transaction revenue up 34% and segment margin at 68.3%. Moody's Analytics grew annual recurring revenue 9% to $3.7 billion with 95% customer retention. Management raised full-year adjusted EPS guidance to $16.50 to $17.00, lifted the buyback authorisation by $500 million to $3.0 billion, and guided free cash flow to $2.7 billion to $2.9 billion.

The most useful sentence in that release was the qualification. Management noted that some second-quarter activity had been pulled forward, which is why the full-year revenue outlook was left essentially unchanged despite the size of the quarter. That single observation complicates the streak, because it says a portion of the beat was timing rather than expansion.

The structural point is that Moody's is really two businesses with opposite forecasting profiles. The subscription half grows in single digits with 95% retention and is highly predictable. The transaction half depends on when borrowers choose to come to market, which is rate-dependent and close to untimeable a quarter ahead. A ten-quarter streak in that configuration is mostly a statement about the second half being hard to forecast, not about the first half being sandbagged. FMP's Cash Flow Statement API is the right cross-check here, because a raised buyback authorisation against guided free cash flow shows how much of future earnings-per-share growth is arithmetic rather than operating. Rated issuance against the mid-single-digit full-year assumption is the number that settles it.

What a Long Streak Is Actually Measuring

A beat streak measures the distance between delivery and expectation. It says nothing on its own about the quality of the delivery or the seriousness of the expectation, and read that way these five separate into three distinct kinds of record.

Two of them are streaks of forecast difficulty. Broadcom grew revenue 86% and AI semiconductor revenue 221% in a single quarter; a 3% beat on a base moving that fast carries almost no information about estimate discipline. Moody's transaction revenue turns on issuance timing that management itself described as pulled forward. In both cases the input is genuinely hard to forecast, and the streak partly reflects that rather than any repeatable edge.

Two more are streaks of structural change outrunning the models. GE Aerospace has been separated twice inside its run while its aftermarket converts on a shop-visit schedule visible earlier inside the company than outside it. Microsoft is moving revenue from per-seat licensing toward consumption billing, which reduces the predictability of its own quarters even as the beats continue. In both, the company changed shape faster than the models following it.

Intuitive Surgical is the third kind, and the only one where the streak reads as consensus persistently mis-specifying a well-lit business. That is the version worth acting on, because it points to a modelling error rather than to volatility.

Turning that into a workflow means testing each streak rather than ranking it. Pairing the surprise history with the Financial Estimates API shows whether forecasts are converging on the delivered numbers or still trailing them, which is the difference between a streak that is closing and one that is not. The Income Statement API and Cash Flow Statement API establish whether the beats came from operations or from below the line, since buybacks, tax rates and expense timing all produce clean EPS beats that say nothing about demand, and Moody's enlarged repurchase authorisation is a live reason to run that check. The Financial Statement Growth API places each result in a multi-year frame so a favourable comparison is not mistaken for improvement, while the Price Target Consensus API and Historical Stock Grades API show whether the market has already repriced the consistency or is still treating each beat as news.

One methodological point deserves its own line, because it will affect anyone running this screen at scale. Broadcom's recorded streak stops at seventeen quarters solely because a single pre-split estimate was never restated for a ten-for-one split. The surprise data is correct as recorded and wrong as interpreted. Splits, spin-offs and segment restatements all create the same failure mode, and a streak methodology without a restatement check will quietly truncate the longest and most interesting records in the dataset. Cross-referencing surprise history against corporate actions and reported statements within the broader FMP platform is what keeps the screen from discarding its own best output.

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:

https://financialmodelingprep.com/stable/earnings-surprises-bulk?year=2025&apikey=YOUR_API_KEY

Sample Response:

[ { "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:

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.

Where the Next Prints Test the Record

Four of these five report inside the next eight weeks, with GE Aerospace and Intuitive Surgical landing on the same day and Microsoft and Moody's a week behind them. Feeding those results back through the FMP Earnings Surprises Bulk API is what keeps the record honest, because a streak is only ever as informative as the estimate it is measured against.

Want more? Explore our earlier article: Weekly Signals Desk | Concentrated Analyst Revisions via the FMP API (Aug 24-28)

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.