Paychex beat consensus on Wednesday for a fifth straight quarter, held its full-year guidance and expanded its operating margin. The stock fell more than 7% that day and closed the week down almost 13%. The gap between the result and the reaction is the most useful lens for this week's screen, because it shows what a beat streak measures and what it does not: it tracks execution against a quarterly estimate, not whether that estimate is still the number the market cares about.
This edition uses FMP's Earnings Surprises Bulk API to profile five companies with unbroken EPS beat runs, ranging from AMETEK's 38 quarters to Paychex's five, and then walks through how the API supports a repeatable screen for earnings consistency.
Key Takeaways
- AMETEK and Cisco each carry streaks of more than 35 quarters, and both runs began immediately after a quarter in which EPS matched the estimate exactly, a reminder that strict definitions shape how long any streak appears.
- Cardinal Health's latest beat would have cleared consensus even without a one-time tariff refund, which makes its 16-quarter streak more robust than the headline surprise alone implies.
- TD SYNNEX and Paychex both extended their streaks this week and both sold off, one on margin and cash flow concerns, the other on slower growth in its core segment.
- Across the group, the quality of each beat depends on datasets beyond EPS: segment mix, cash conversion, margin trends and whether forward estimates move after the print.
Five Streaks Holding Up Through the Latest Quarter
AMETEK, Inc. (AME)
Beat Streak: 38 quarters.
Next quarterly report: Oct. 29 — EPS: $2.10; Revenue: $2.10B (consensus).
AMETEK's run stretches back to its May 2017 report, and the most recent result explains why consensus has struggled to catch up. Second-quarter sales reached a record, orders grew by more than a quarter for the second consecutive period, operating margins widened in both of its main segments, and management raised full-year EPS guidance. Free cash flow conversion exceeded 100% of net income, so the earnings beat was backed by cash rather than accounting timing.
The streak reflects a model built on a portfolio of niche instrument and electromechanical businesses, supplemented by a steady flow of acquisitions. That structure tends to produce conservative guidance and incremental upside. The key question is how much of the growth is organic. FMP's Revenue Product Segmentation API separates the Electronic Instruments and Electromechanical groups, which helps show whether the order strength is broad across the portfolio or concentrated where recent acquisitions have added scale.
Cisco Systems, Inc. (CSCO)
Beat Streak: 36 quarters.
Next quarterly report: Nov. 11 — EPS: $1.32; Revenue: $18.11B (consensus).
Cisco's streak began after a tie in August 2017 and has since run through a full product transition toward software, subscriptions and, most recently, AI infrastructure. The fiscal fourth quarter delivered record revenue and a large increase in AI-related orders from hyperscale customers, and management guided fiscal 2027 revenue growth of around 15%. Earnings rose faster than revenue, extending the beat to a 36th quarter.
The shares nonetheless fell after the report because gross margin contracted as hardware made up a larger share of the mix, and the guide for the current quarter implied continued pressure. That is the tension to watch: a streak supported by rising volume while each dollar of revenue carries less gross profit. FMP's Income Statement API makes it straightforward to track gross margin quarter by quarter alongside revenue, and the relationship between the two will show whether pricing actions and software attach rates are offsetting the mix shift.
Cardinal Health, Inc. (CAH)
Beat Streak: 16 quarters.
Next quarterly report: Oct. 29 — EPS: $2.92; Revenue: $66.71B (consensus).
Cardinal Health's streak started after a miss in mid-2022 and has been one of the steadier runs in healthcare distribution since. The fiscal fourth quarter produced one of the largest surprises in the group, with adjusted EPS well ahead of consensus as the pharmaceutical and specialty segment grew profit faster than revenue and nuclear and precision health continued to expand. The company also added a new share repurchase authorization.
The beat needs one adjustment. Part of the quarter's EPS came from a one-time benefit tied to tariff refunds. Removing that benefit still leaves the result above consensus, which suggests the streak does not depend on it, but it does mean the quarter overstates the underlying run rate. Fiscal 2027 guidance also points to slower pharmaceutical revenue growth and ongoing cost pressure in medical products. FMP's Earnings Transcript API is useful here for following how management characterizes tariff costs and segment margins from one call to the next, since those comments often move ahead of the reported figures.
TD SYNNEX Corporation (SNX)
Beat Streak: 6 quarters.
Next quarterly report: Jan. 14 — EPS: $4.72; Revenue: $21.65B (consensus).
TD SYNNEX reported on Thursday and extended its streak to six quarters with the widest surprise in this screen, as gross billings and revenue rose sharply on demand for servers and storage tied to AI infrastructure. Its Hyve business, which builds systems for hyperscale customers, more than doubled billings. Management's guidance for the next quarter also sits above the consensus figure recorded above, which suggests estimates have yet to fully adjust.
The shares fell on the day regardless, and the reasons are specific. Gross margin narrowed as lower-margin AI hardware made up more of the mix, and trailing free cash flow turned negative as the company funded inventory and receivables for large programs. A distributor growing this quickly can report strong earnings while consuming cash, and the streak alone will not reveal that. FMP's Cash Flow Statement API tracks operating cash flow and working capital changes by quarter, which is the most direct way to check whether management's expectation of improving cash generation in fiscal 2027 is materializing.
Paychex, Inc. (PAYX)
Beat Streak: 5 quarters.
Next quarterly report: Dec. 18 — EPS: $1.32; Revenue: $1.62B (consensus).
Paychex has the shortest streak in the group, and it began after a quarter in mid-2025 in which EPS exactly matched the estimate. This week's fiscal first-quarter beat was narrow. Management Solutions, the core payroll and HR software segment, grew more slowly than the company had expected, while the PEO and insurance business grew double digits and outperformed. Full-year guidance for revenue and earnings growth was left unchanged.
The market reaction suggests investors were looking for evidence of accelerating growth after the Paycor integration and did not find it in the core segment. The beat itself is less informative than what follows it. FMP's Financial Estimates API shows whether analysts revise forward revenue and EPS after the report; if estimates hold steady while the stock resets lower, the streak may continue from a lower valuation base, whereas downward revisions would indicate the market is anticipating slower growth ahead.
What a Beat Streak Measures When the Market Looks Past It
The five streaks share a surface feature and diverge underneath. AMETEK and Cardinal Health are beating on margin expansion and cash generation that support the earnings number. Cisco and TD SYNNEX are beating on volume while margins narrow, which means their streaks rest on how long demand can outpace mix pressure. Paychex is beating by small amounts on a stable business whose growth rate is now the market's primary concern. The EPS record looks similar across all five; the quality of the beat does not.
This week's price reaction adds a second observation. Three of the five saw their shares decline after results that cleared consensus. That pattern tends to appear when estimates have become a floor rather than a hurdle, and attention shifts to guidance, margins and cash flow. It does not make a streak less valid, but it narrows what the streak can tell you on its own.
Building that fuller picture is a data-joining exercise. On the FMP platform, the Earnings Surprises Bulk API identifies the beat, and the Earnings Report API supplies the full quarterly history needed to confirm where each streak actually started. From there, the Income Statement Growth API shows whether revenue and operating income are growing at the pace the EPS record implies, while the Cash Flow Statement API tests whether those earnings are converting into cash.
Expectations complete the loop. The Financial Estimates API shows whether forecasts are rising after each beat or staying flat, and the Historical Stock Grades API captures whether analyst sentiment is moving in the same direction. When a long streak coincides with rising estimates, expanding margins and positive cash conversion, the signal is at its strongest. When any of those diverge, as they do this week for Cisco, TD SYNNEX and Paychex in different ways, the streak becomes a starting point for further work rather than a conclusion.
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 Earnings Consistency Beyond the Headline Beat
A streak records that a company has cleared the bar; it does not record how high the bar was or what the market expected beyond it. Tracked through FMP's Earnings Surprises Bulk API and read alongside margins, cash flow and revisions, each new quarter shows whether that consistency is still carrying information.
Want more? Explore our earlier article: Weekly Signals Desk | Concentrated Analyst Revisions via the FMP API (Sept 14-18)
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.
