This week's screen surfaced five companies that all reported on Wednesday or Thursday, and all of them extended an unbroken run of EPS beats. Jabil and Acuity each reached 26 consecutive quarters, Micron added a 14th, Nike a 13th and Accenture a ninth. The beats were consistent; the reactions were not. Several of these stocks traded lower after their reports, and in almost every case the explanation sat in the outlook rather than the quarter just completed.
This edition draws on FMP's Earnings Surprises Bulk API to verify each streak and identify where it began, then explains how the API anchors a repeatable screen for earnings consistency that holds up when the market's attention shifts to guidance.
Key Takeaways
- Jabil and Acuity share a 26-quarter streak dating from mid-2020, yet one is scaling rapidly on AI infrastructure demand while the other is managing a flat core business with a faster-growing technology segment.
- Micron's run includes several early quarters where the beat came from a smaller-than-expected loss, so its recent record is stronger evidence of execution than the full streak count suggests.
- Nike and Accenture both beat EPS estimates while guiding to weaker growth for the year ahead, a reminder that a beat streak measures accuracy against a quarterly bar rather than the direction of the business.
- The most informative comparison this week was between each company's beat and its forward guidance, which is where estimate revisions are likely to concentrate.
Five Streaks Extended in This Week's Reporting Window
Jabil Inc. (JBL)
Beat Streak: 26 quarters.
Next quarterly report: Dec. 16 — EPS: $4.05; Revenue: $11.01B (consensus).
Jabil's streak began in mid-2020, immediately after a pandemic-hit quarter, and has continued through a period in which the contract manufacturer reshaped its business toward data center infrastructure. The fiscal fourth quarter brought record revenue and another beat, and management guided fiscal 2027 to substantially higher revenue and core EPS, with AI-related revenue expected to grow by roughly half again.
The share price still fell after the report. The guidance is weighted toward the back half of the year, and the company plans significant capital spending to add manufacturing capacity across several regions, while inventory days are running above management's target. That combination raises the stakes on execution: the streak shows Jabil has consistently managed expectations well, but the next phase depends on capacity coming online on schedule and converting into revenue. FMP's Balance Sheet Statement API tracks inventory and property, plant and equipment quarter by quarter, which shows whether the build-out is translating into output or accumulating ahead of demand.
Acuity Inc. (AYI)
Beat Streak: 26 quarters.
Next quarterly report: Jan. 14 — EPS: $5.07; Revenue: $1.18B (consensus).
Acuity matched Jabil's 26 quarters, with its streak beginning after a narrow miss in April 2020. The two businesses inside the company are moving at different speeds. The lighting segment, still the larger of the two, posted roughly flat sales with lower margins, while the Intelligent Spaces segment, which includes building management and control software, grew double digits and expanded its margin meaningfully. Cash generation for the year was strong, supporting both share repurchases and debt reduction.
The quality question is how the beat was achieved. A one-time tariff refund lifted reported operating margin, while adjusted margin was essentially unchanged from a year earlier. That suggests the consistency of Acuity's streak rests increasingly on mix and cost control rather than top-line growth in its core market. FMP's Revenue Product Segmentation API separates the lighting and Intelligent Spaces segments, and following their relative growth over the next several quarters will show whether the faster-growing business becomes large enough to carry the overall earnings trend.
Micron Technology, Inc. (MU)
Beat Streak: 14 quarters.
Next quarterly report: Dec. 23 — EPS: $35.41; Revenue: $61.25B (consensus).
Micron reported record fiscal fourth-quarter revenue, several times its level a year earlier, with data center revenue up dramatically on demand for high-bandwidth memory used in AI accelerators. It beat EPS estimates again and guided the next quarter well above prior consensus, citing tight supply across its product lines.
The streak count requires context. It began in mid-2023, during the last memory downturn, and the first four links were quarters in which Micron either lost less than expected or reported a small profit against a forecast loss. Those are legitimate beats by definition, but they carry less information than the ten clean quarters that followed, which span the full upswing in pricing and volume. FMP's Earnings Report API provides the full quarterly sequence of actual and estimated EPS, making it easy to see where loss-year beats end and positive-earnings beats begin. That distinction matters when comparing Micron's streak with those of less cyclical companies.
NIKE, Inc. (NKE)
Beat Streak: 13 quarters.
Next quarterly report: Dec. 17 — EPS: $0.48; Revenue: $11.22B (consensus).
Nike beat EPS estimates for a 13th consecutive quarter, but the beat was overshadowed by nearly everything else in the report. Revenue declined and missed consensus, Greater China sales fell sharply as the company restructures its distribution there, and direct and digital channels continued to shrink. Gross margin improved modestly. Management guided fiscal 2027 to a revenue decline and an EPS range below prior expectations, and the shares reached their lowest level in more than a decade.
This is the clearest example in the group of how a beat streak can coexist with a deteriorating trajectory. Nike's EPS beats have largely reflected cost control and conservative estimates during a multi-year turnaround, rather than demand recovery. The more informative signal is regional. FMP's Revenue Geographic Segments API separates North America, where sales grew modestly, from Greater China and other regions, which is where the company's restructuring plans and the market's concerns are concentrated.
Accenture plc (ACN)
Beat Streak: 9 quarters.
Next quarterly report: Dec. 17 — EPS: $3.92; Revenue: $19.41B (consensus).
Accenture's streak began after a narrow miss in mid-2024 and reached nine quarters with a fiscal fourth quarter that topped the high end of its revenue guidance, delivered balanced growth across all three geographic regions and set a record for large client bookings, including in managed services. The company continues to expand its AI and data workforce and deployed substantial capital on acquisitions during the year.
The outlook was more restrained. Fiscal 2027 guidance implies organic growth well below this year's pace once acquisitions are excluded, and management pointed to pricing pressure and a regional headwind in the Middle East. For a services business, that gap between strong bookings and slower implied organic growth is the key tension to watch. FMP's Financial Estimates API shows whether analyst revenue and EPS forecasts for the coming year move toward the lower organic outlook or stay anchored to the bookings strength, which will set the bar for the next link in the streak.
What Consistent Beats Reveal When Guidance Diverges
Taken together, the five reports show a common pattern in which execution against near-term estimates remained strong while the outlook carried most of the information. Jabil and Micron paired their beats with guidance that implies substantial growth, but Jabil's reaction reflected concerns about capital intensity and timing. Nike and Accenture beat while guiding to weaker growth. Acuity's beat depended partly on a one-time item, leaving its underlying margin flat. In each case, the streak confirms reliable short-term forecasting, but it does not settle whether the business is accelerating.
That distinction has practical value. A beat streak is most informative when it coincides with rising estimates, because it suggests analysts are still behind the company's progress. When estimates are falling while beats continue, as they may for Nike, the streak can reflect a lowering bar rather than improving performance. The same EPS record can therefore indicate very different situations depending on the direction of expectations.
Building that context into a screen means joining surprise data with fundamentals and estimates. On the FMP platform, the Earnings Surprises Bulk API identifies the beats, and the Earnings Report API reveals where each streak began and whether it includes loss-period quarters. The Income Statement Growth API then shows whether revenue and operating income are growing at a pace consistent with the EPS record, and the Cash Flow Statement API tests whether those earnings convert into cash.
Expectations complete the analysis. The Financial Estimates API tracks whether forward forecasts are rising or being cut after each report, and the Historical Stock Grades API shows whether ratings are shifting alongside them. When a long streak lines up with rising estimates and solid cash conversion, the signal is at its strongest. When guidance and estimates point in the opposite direction, as they did for part of this week's group, the streak is better read as evidence of disciplined forecasting than as evidence of momentum.
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.
Keeping Beat Streaks in Proportion to the Outlook
A streak confirms that a company keeps clearing the quarterly bar; this week showed how little that reveals about where the bar is heading. Monitored through FMP's Earnings Surprises Bulk API alongside forward estimates, each new report shows whether consistency is still pointing in the same direction as the business.
Want more? Explore our earlier article: Weekly Signals Desk | Five Notable Valuation Disconnects from the FMP API (Sept 21-25)
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.
