Signals Desk Weekly Take via FMP API | Five Companies With Persistent Earnings Beats (Sept 7-11)

Veeva Systems has never reported a quarter below consensus. Not once since it listed. That is the outer edge of this week's scan, and it sits alongside four other companies whose streaks reach back far enough to cover a pandemic, an inflation shock, two rate cycles and a full rotation in market leadership. Motorola Solutions, Parker Hannifin, Valero Energy and Comcast complete the set, with runs of 48, 44, 41 and 39 consecutive quarters.

Using FMP's Earnings Surprises Bulk API, this article identifies those streaks and then asks the more useful question: what has to be true about a forecasting process for a company to clear consensus more than forty times in a row, and where does that pattern stop being informative. The screen applies a strict definition throughout, in which a tie with consensus ends a streak rather than extending it.

Key Takeaways

• Streak length maps to forecastability, not to quality. The longest runs belong to businesses with contracted or recurring revenue that analysts can model closely, and the shortest belongs to a company whose reported earnings are falling.
• Valero is the outlier that proves the rule. A refiner clearing consensus 41 straight times says more about how slowly estimates chase refining margins than about the cyclicality of the business, which has not gone anywhere.
• Comcast is the clearest case of a streak diverging from fundamentals: it beat again last quarter while adjusted EBITDA fell by double digits and buybacks were suspended ahead of a separation.
• A strict tie rule changes the ranking materially. Several well-known compounders end their streaks on a quarter that matched consensus exactly rather than on a miss, which is a reporting artifact worth knowing about before comparing streaks across sources.

Five Streaks That Have Outlasted the Estimates

Veeva Systems Inc. (VEEV)

Beat Streak: 52 quarters.
Next quarterly report: Nov. 25 — EPS: $2.33; Revenue: $935.01M (consensus).

Fifty-two quarters is Veeva's entire life as a public company. There is no non-beat anywhere in the available history, which makes this less a streak than a structural property of how the business is modelled. Subscription software sold into life sciences produces revenue that is contracted, renewed on schedule and visible well in advance, and management has consistently guided to figures the company can clear. The interesting question is not whether the run continues but why consensus has never once landed above it.

The most recent quarter shows the machinery working. Revenue grew 18% to $928 million with subscription revenue up 16%, non-GAAP operating margin held near 45%, and full-year guidance was raised. Underneath that, the commercial franchise reached a genuine milestone: Vault CRM recorded its strongest quarter since launch, with commitments now secured from twelve of the twenty largest biopharma companies and five of them live. That migration is the substantive story, because it is the part of the business that faced a credible competitive challenge.

The forward risk is not an earnings miss. It is that guidance conservatism of this consistency eventually gets priced into the estimates themselves, at which point the beat magnitude compresses toward zero even as execution stays intact. FMP's Financial Estimates API is where that compression becomes visible, by tracking whether the spread between consensus and reported results is narrowing across quarters rather than simply recording another beat.

Motorola Solutions, Inc. (MSI)

Beat Streak: 48 quarters.
Next quarterly report: Oct. 29 — EPS: $4.41; Revenue: $3.25B (consensus).

Twelve unbroken years puts Motorola Solutions in rare company, and the underlying reason is visible in its order book rather than its income statement. Ending backlog reached a record $15.6 billion last quarter, up 11%, with roughly three quarters of that sitting in software and services. Public safety and government communications spending moves on procurement cycles measured in years, not quarters, which gives both management and the sell side an unusually firm base to forecast from.

The most recent print was not a marginal clear. Revenue grew 13% to $3.13 billion, non-GAAP operating margin expanded 330 basis points, earnings per share rose 24%, and full-year guidance moved up on both revenue and EPS. Products and systems integration grew faster than software and services for once, helped by the Silvus acquisition, which is expected to contribute around $100 million this year and is being scaled aggressively through a doubled sales force.

That mix shift is what makes the next report worth watching more closely than the streak itself. Hardware-led growth carries different margin behaviour and different forecast error than recurring software revenue, and a company that has beaten 48 times running has done so largely on the strength of the predictable half. FMP's Revenue Product Segmentation API separates the two revenue bases so the composition of any future beat can be attributed rather than assumed.

Parker Hannifin Corporation (PH)

Beat Streak: 44 quarters.
Next quarterly report: Nov. 5 — EPS: $8.16; Revenue: $5.53B (consensus).

Parker Hannifin's eleven-year run is the most impressive of the five on a degree-of-difficulty basis, because diversified industrial demand is genuinely cyclical and the company has absorbed a pandemic, a supply-chain breakdown and an inflation cycle inside this streak. It closed fiscal 2026 with sales above $21.5 billion, up 8.3% reported and 6.6% organic, adjusted earnings per share up 18% for the year, and free cash flow at 20.3% of sales.

The composition matters. Aerospace grew organically at 13.4% against 3.4% for the diversified industrial segment, and record backlog of $12.8 billion includes $8.5 billion of aerospace work. In practice this has become a company whose earnings surprise is increasingly driven by one long-cycle segment while the broader industrial base runs at a much slower pace. Management also reached its margin targets three years ahead of schedule and reset them higher, alongside a 70-year run of dividend increases and pending acquisitions in filtration and defence aerospace.

Two things complicate the next print. Fiscal first-quarter consensus of $8.16 sits below the $9.27 just delivered, which reflects normal seasonality rather than deterioration, and the acquisition pipeline will change the comparison base as deals close. FMP's Key Metrics API is the appropriate tool for holding those effects apart, since free cash flow margin and return on invested capital are the measures that reveal whether acquired scale is improving the business or merely enlarging it.

Valero Energy Corporation (VLO)

Beat Streak: 41 quarters.
Next quarterly report: Oct. 22 — EPS: $17.58; Revenue: $39.29B (consensus).

A refiner with a 41-quarter beat streak is a genuinely odd result, and it deserves scepticism rather than admiration. Refining earnings swing with crack spreads that nobody forecasts reliably, and the streak spans quarters where Valero reported losses. What it actually documents is that consensus estimates for this sector are reset downward faster than margins deteriorate and upward slower than margins recover, so the reported figure clears a lagging bar in both directions.

The scale of that lag is worth being concrete about. Last quarter delivered adjusted earnings of $12.54 per share against a $10.11 estimate, with refining operating income of $4.47 billion, a margin of $23.62 per barrel on 2.95 million barrels a day, and a renewable diesel joint venture that swung from a loss to $717 million of operating income. Two quarters inside this streak carry consensus estimates an order of magnitude below the reported figure, which makes those particular links effectively free and is a caveat that belongs alongside the headline number.

The next report is structurally different from the ones behind it. Consensus of $17.58 implies a further step up from an already exceptional quarter, and the company has closed its Benicia refinery while bringing a fluid catalytic cracking optimization project online. FMP's Owner Earnings API is more useful here than headline EPS, because it adjusts for maintenance capital in a business where turnaround timing moves reported earnings substantially between quarters.

Comcast Corporation (CMCSA)

Beat Streak: 39 quarters.
Next quarterly report: Oct. 29 — EPS: $1.01; Revenue: $29.50B (consensus).

Comcast is the name in this group where the streak and the business are pointing in different directions, and that divergence is the most instructive thing in the screen. The company cleared consensus again last quarter at $1.04 against $0.97. In the same quarter, revenue declined, adjusted earnings per share fell around 17% year over year, adjusted EBITDA dropped more than 13%, and broadband lost another 167,000 subscribers.

Read that sequence carefully and the streak stops looking like execution. It looks like a company whose declines are gradual and well understood, which is precisely the condition under which analysts model accurately and management guides to a clearable number. The genuine operational progress sits elsewhere: wireless added a record 448,000 lines to reach 10.2 million, and Peacock turned its first quarterly profit with EBITDA of $189 million against a loss a year earlier, helped by World Cup and NBA programming. Those are real inflections, but they are not yet large enough to offset connectivity erosion.

The structural item dominates everything else. Buybacks were paused in late June ahead of the planned tax-free separation of the NBCUniversal cable networks, which removes a lever that has supported per-share results and means the entity reporting in October will be on a path to becoming two companies. FMP's Cash Flow Statement API is where that matters most, since free cash flow of $4.6 billion held up while EBITDA fell, and understanding which of those two series the separation follows is more consequential than a fortieth consecutive beat.

What a Forty-Quarter Streak Does and Does Not Prove

Sort these five by streak length and you get something close to a ranking of how forecastable each business is. Veeva sells contracted subscriptions and has never missed. Motorola Solutions converts a multi-year government backlog. Parker Hannifin runs long-cycle aerospace programmes against a slower industrial base. Valero sells a commodity into spot markets. Comcast is in managed decline across its largest segment. Length tracks visibility, and visibility is a property of the revenue model, not evidence of operational excellence.

That reframing changes what the signal is good for. A long streak is a reliable indicator that consensus has been anchored somewhere below reported results for an extended period, which is a statement about the estimate process at least as much as about the company. The most interesting cases are the ones where the two decouple. Comcast's beat arrived in a quarter where every meaningful operating measure moved the wrong way, and Valero's arrived because sector estimates chase margins rather than lead them. In both, the streak is intact and the information content is close to zero. Conversely, Parker Hannifin's beat came with organic growth, margin targets pulled forward by three years and free cash flow above 20% of sales, which is a streak carrying real weight behind it.

Separating those two cases is straightforward once the surprise history is placed alongside other series, and the endpoint coverage on the FMP platform makes the comparison mechanical rather than manual. The first check is whether the beat has an operating source: the Financial Statement Growth API shows whether revenue, operating income and cash flow are moving in the same direction as EPS, and a beat that appears only in the earnings line is usually a tax, buyback or expense-timing effect. The Key Metrics TTM API then normalizes free cash flow yield and return measures across businesses as unlike as a software vendor and a refiner, which raw EPS comparisons cannot do.

The second check concerns the estimates themselves. The Financial Estimates API reveals whether forward revenue and EPS forecasts are rising to meet a repeat beater or remaining stubbornly anchored, and a streak that persists while estimates stay flat is measuring analyst inertia rather than company performance. Comparing that against the Price Target Consensus API and the Historical Stock Grades API completes the picture, because a company can keep clearing quarterly estimates while targets drift lower and ratings soften, which is the shape of a business that is executing against expectations that are themselves declining. Where surprise history, estimate revisions, margins and sentiment all move together, the streak is describing something real. Where they separate, the separation is the finding.

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.

The Next Print Is the Only Real Test

Four of these five report inside five weeks, and each one tests a different part of the pattern: whether a hardware-led quarter changes Motorola's surprise behaviour, whether Valero's estimates have finally caught up to refining margins, and whether Comcast's beat survives a quarter without buybacks. Running each result back through the FMP Earnings Surprises Bulk API keeps the streak and the evidence behind it on the same page.

Want more? Explore our earlier article: Weekly Signals Desk | Five Notable Valuation Disconnects from the FMP API (Aug 31-Sept 4)

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.