Signals Desk Weekly Take via FMP API | Five Companies With Persistent Earnings Beats (Aug 24-28)

Roper Technologies has not missed a quarterly EPS estimate since July 2016. Forty consecutive quarters is long enough that the streak stops describing one company and starts describing how the sell side models it. This week's scan surfaced four more of the same shape across four unrelated sectors: Cadence Design Systems at 34 quarters, Mastercard at 23, Ross Stores at 17, and IDEXX Laboratories at 16.

The list was built from FMP's Earnings Surprises Bulk API, then verified by walking each company's full reported history so the streak count reflects actual sequences rather than a headline figure. What follows is the five names, and then how that same API supports a repeatable screen for earnings consistency.

Key Takeaways

• Four of these five streaks predate the 2022 rate cycle, so they have already survived one complete reset in how analysts model cost inflation, demand and discount rates.
• The sector spread is the interesting part. Vertical software, chip design, payments, off-price retail and veterinary diagnostics share almost no demand driver, which locates the common factor in the forecasting process rather than the end market.
• Length raises the bar for reading any single print. Ross Stores' August quarter was flattered by roughly $253 million in one-off tariff refunds, exactly the kind of item that keeps a streak intact while saying little about the business.
• Streak length is an opening filter, not a conclusion. It becomes informative once surprise history is read against margins, cash conversion and the direction of estimate revisions.

Five Streaks Long Enough to Span a Full Cycle

Roper Technologies, Inc. (ROP)

Beat Streak: 40 quarters.
Next quarterly report: Oct. 22 — EPS: $5.79; Revenue: $2.17B (consensus).

Ten unbroken years of clearing consensus is less a statement about surprise than about business model. Roper is a portfolio of vertical software franchises selling into insurance brokers, government contractors, law firms and freight brokerages, where revenue is contracted, renewal rates are high and the visible order book extends well past the quarter being modelled. That structure makes management guidance tractable, and a company able to guide tightly is a company able to guide slightly below what it can deliver.

The June quarter fit the pattern, with revenue of $2.11 billion, organic growth of 5% and adjusted EPS of $5.38, alongside a full-year guidance raise. The more revealing detail sits outside operations. A serial acquirer announced no acquisition through the first eight months of 2026 and instead retired roughly 9.0 million shares over three quarters, more than 8% of the count, lifting net leverage toward 3.4 times. Reported earnings also carried an $828.6 million fair-value gain on the 43.4% Indicor stake. Neither item is a defect, but both change the composition of the number that clears the estimate.

That is the distinction worth testing before the October print. Pairing the surprise record with FMP's Cash Flow Statement API separates cash generated by the software franchises from the arithmetic of a shrinking share count, and the Enterprise Values API tracks whether rising net debt is being absorbed by the operating base or simply carried. Agentic pricing tiers launched across Vertafore, Deltek and Aderant carry no material revenue this year, so their adoption curve is the next real variable rather than the current one.

Cadence Design Systems, Inc. (CDNS)

Beat Streak: 34 quarters.
Next quarterly report: Oct. 26 — EPS: $2.04; Revenue: $1.61B (consensus).

Cadence has cleared every quarter since early 2018, a run that covers the entire modern AI design cycle. The June quarter was the strongest of it: revenue up 24%, non-GAAP operating margin of 45.5%, and record backlog of $8.1 billion with roughly $4.2 billion expected to convert inside twelve months. Full-year guidance moved up on the back of it.

Composition is where the analytical work sits. Three distinct things are pushing the same result. The Hexagon design and engineering business, acquired for roughly $3.2 billion, now contributes around four points of recurring growth. Intellectual property revenue grew more than 40%, which is lumpier than the licence base. And hardware set a record while being described as supply-constrained rather than demand-constrained, meaning shipments reflected what could be built. A guidance framework that assumes export rules stay substantially similar for the rest of the year sits underneath all of it.

Splitting acquired revenue from organic through FMP's Revenue Product Segmentation API is what makes the 24% headline legible, because a beat carried by front-loaded hardware and a newly consolidated business behaves differently in the next comparison than one carried by licence renewals. The Intel Foundry collaboration announced on 8 June is weighted to later years and carries $20 million to $25 million of incremental second-half investment, so it pressures near-term margin before it contributes anything.

Mastercard Incorporated (MA)

Beat Streak: 23 quarters.
Next quarterly report: Oct. 29 — EPS: $5.12; Revenue: $9.65B (consensus).

Mastercard's run begins in January 2021 and therefore spans the entire travel recovery, the rate cycle and the inflation shock, which is a demanding stress test for a business levered to consumer volume. Net revenue rose 14% in the June quarter, cross-border volume grew 12%, and adjusted operating margin expanded roughly 120 basis points to 61.1%. The part that explains the consistency is value-added services, up 20% and growing at close to twice the pace of the payment network itself.

Two developments now sit against that record. In June the court approved the merchant interchange settlement, reducing rates by 10 basis points for five years, capping the standard consumer rate at 1.25% for eight, and loosening surcharging rules, with trade groups signalling an appeal. Separately, the acquisition of stablecoin infrastructure provider BVNK closed on 3 August, the same day Ling Hai took over as chief financial officer. A company absorbing a fee reset, a new settlement rail and a finance leadership change in one quarter is a company whose estimate dispersion should widen.

Buybacks of 9.8 million shares for $4.9 billion contributed roughly $0.14 to adjusted EPS growth in the quarter, which is a useful reminder that the beat and the operating result are not the same measurement. FMP's Key Metrics API gives the per-share and return series needed to hold those apart, and services revenue growing at twice network growth is the mix shift to keep in view as interchange economics reprice.

Ross Stores, Inc. (ROST)

Beat Streak: 17 quarters.
Next quarterly report: Nov. 19 — EPS: $1.81; Revenue: $6.19B (consensus).

Ross is the case that argues against reading streaks literally. The 20 August quarter looks extraordinary on its face: sales up 13%, comparable store sales up 10% on traffic rather than ticket, diluted EPS of $2.66 against $1.56, and operating margin higher by 610 basis points. It is also the least representative print in this group.

Of those 610 basis points, 405 came from approximately $253 million in tariff refunds recovered after the February 2026 invalidation of the IEEPA duties, worth about $0.60 per share and not repeatable. Removing it leaves 205 basis points of underlying expansion, which still beat the company's own 130 to 150 basis point plan, so the quarter was genuinely strong. The surprise line simply overstates by a wide margin how strong. Management guided comparable sales to step down to 6% to 7% and then 4% to 5%, and raised the store opening plan to 115 locations.

This is the clearest illustration in the screen of why the surprise field alone is insufficient. Reading the same quarter through FMP's Income Statement API isolates the refund inside the operating line, which converts an apparent 70% earnings beat into a considerably more modest read on trading. The structural question underneath remains open: whether double-digit comparable growth reflects durable share capture from a more conservative pricing stance, or a trade-down cycle that normalises as the tariff distortion lapses.

IDEXX Laboratories, Inc. (IDXX)

Beat Streak: 16 quarters.
Next quarterly report: Nov. 2 — EPS: $3.71; Revenue: $1.19B (consensus).

IDEXX has cleared consensus every quarter since November 2022, and the June result extended it with revenue up 9% organically, EPS up 18%, and gross margin 140 basis points higher at 64.0%. Guidance was raised. On the surface it reads as a straightforward compounder clearing a low bar.

The underlying volume tells a different story, and the divergence is the reason this name is worth attention. United States same-store clinical visits fell 1.3% in the quarter, wellness visits declined more than 3%, and management guides to a further decline of roughly 1.5% in the second half. Growth is therefore coming entirely from diagnostic intensity per visit rather than from more animals through the door. The runway management points to is real, with only about one in ten United States wellness visits currently including bloodwork, but it is a utilisation argument, not a demand argument. Michael Erickson took over as chief executive on 12 May, which adds a guidance-philosophy variable to an already unusual setup.

International recurring revenue grew 14% against 10% in the United States, so the geographic split is doing meaningful work inside a single consolidated number, and FMP's Revenue Geographic Segments API is where that separation becomes visible. Placements of the inVue Dx analyser reached 2,700 in the first half against a full-year target of 5,500, and consumable pull-through from that installed base is the mechanism that would have to keep offsetting a shrinking visit count.

Reading What Sits Underneath a Long Beat Streak

Line these five up and the striking thing is how little they share commercially. Contracted vertical software, semiconductor design tools, a payments toll booth, an off-price apparel chain and a veterinary diagnostics installed base do not respond to the same inputs. What they do share is a forecasting property: revenue that is contracted, recurring or observable far enough ahead that management can set a number it is confident of clearing. Consistency of that kind is as much an artifact of guidance discipline as of operating outperformance, and the two are worth holding apart.

Which is why the more useful question is not whether a streak continues but whether the gap between execution and expectation is narrowing. Estimate revisions pulled from FMP's Financial Estimates API, set against the Price Target Consensus API and the Historical Stock Grades API, show whether analysts are adjusting to a company's trajectory or still anchored to an older model. A name can clear quarterly estimates for years while targets flatten and ratings drift, and that divergence is usually more informative than the beat itself.

The quality test runs in parallel. Roper's Indicor mark and Ross's tariff refund both land inside reported EPS, and both are visible in the statement data published alongside the surprise history on the FMP platform, which turns a judgment call into something testable. Running the Financial Statement Growth API across several years distinguishes steady improvement from a favourable comparison, while the Owner Earnings API and the Cash Flow Statement API establish whether the beat converted into cash or arrived through tax rate, buyback or timing effects.

Combined, that workflow reframes what a streak is for. It is a filter that narrows 899 names to a handful worth the analytical time, and the moment those datasets stop agreeing with one another is usually the moment the story gets interesting.

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.

Carrying the Signal Into the Next Cycle

Ten years is a long time for a model to sit behind a business, and the more instructive part of these five records is not their length but what the next set of prints reveals about which companies are still earning the gap operationally. Reading each new quarter through FMP's Earnings Surprises Bulk API keeps that assessment on identical footing as the sample grows, which is what allows a streak to be treated as evidence rather than as a curiosity.

Want more? Explore our earlier article: Weekly Signals Desk | Five Notable Valuation Disconnects from the FMP API (Aug 17-21)

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.