This week's data scan flagged a pattern that's getting harder for the market to ignore. Using the FMP Earnings Surprises Bulk API, five companies surfaced with sustained earnings outperformance across multiple quarters—spanning different sectors, but exhibiting the same underlying signal: expectations are still lagging execution.
What begins as isolated upside surprises starts to look more structural when it repeats. In this article, we break down those names and walk through how the FMP Earnings Surprises Bulk API can be used to systematically identify—and validate—earnings beat streaks at scale.
Five Companies With Long Earnings Beat Streaks
RingCentral, Inc. (RNG)
Beat Streak: 39 quarters.
Next quarterly report: May 14 — EPS: $1.14; Revenue: $642.7M (consensus).
A 39-quarter streak in a mature SaaS category stands out less for the magnitude of individual beats and more for the consistency of execution through multiple demand environments. RingCentral operates in a segment—unified communications—where growth narratives have cooled alongside broader enterprise software repricing. Yet the persistence of earnings outperformance suggests a tighter alignment between internal forecasting and operating discipline than what external expectations have implied.
What makes this signal more instructive is the shift in how software companies are being evaluated. The market has increasingly emphasized profitability, cash flow durability, and cost structure efficiency over pure top-line expansion. In that context, repeated EPS beats can reflect margin management and disciplined expense control rather than accelerating demand. Reviewing income statement trends alongside operating margin progression would help clarify whether the streak is being driven by structural efficiency gains or by conservative guidance practices.
The next data point to watch is not simply whether the company extends the streak, but how the spread between actual and estimated EPS evolves. A narrowing gap, even with continued beats, would indicate that expectations are catching up—an important transition point in how the signal is interpreted.
RTX Corporation (RTX)
Beat Streak: 36 quarters.
Next quarterly report: April 21 — EPS: $1.51; Revenue: $21.37B (consensus).
RTX's 36-quarter earnings beat streak spans multiple cycles in aerospace and defense, including periods of supply chain disruption, commercial aviation recovery, and shifting defense budgets. That continuity points to a different type of signal than typical cyclical outperformers: not acceleration, but operational predictability in a complex industrial environment.
The key driver here is visibility. Defense contracts, long-cycle aerospace programs, and backlog dynamics tend to anchor expectations more firmly than in most sectors. However, persistent beats suggest that even within that structured framework, internal execution has exceeded modeled assumptions. The signal is less about upside surprises in demand and more about cost control, program execution, and backlog conversion efficiency. Examining segment-level revenue breakdowns and backlog disclosures would provide additional clarity on where that consistency is originating.
Recent industry context adds another layer. Supply chain normalization and ongoing defense spending have stabilized the operating environment, but also reduced the margin for unexpected upside. If the beat streak continues under these conditions, it reinforces the interpretation that the signal is tied to execution rather than external volatility. Monitoring analyst estimate revisions alongside reported results can help determine whether consensus is beginning to internalize that pattern.
Carnival Corporation & plc (CCL)
Beat Streak: 14 quarters.
Next quarterly report: June 23 — EPS: $0.40; Revenue: $6.68B (consensus).
Carnival's 14-quarter streak sits within a post-recovery phase rather than a steady-state environment, which changes how the signal should be read. Following the pandemic-driven disruption, the company has been operating in a demand rebound cycle marked by pricing power, improving occupancy, and balance sheet repair. In that context, repeated earnings beats reflect both operational recovery and the gradual recalibration of expectations.
The more nuanced takeaway lies in how quickly consensus has adjusted relative to underlying performance. Early in the recovery, estimates tended to lag materially, allowing for larger positive surprises. As the cycle matures, the persistence of beats suggests that either demand resilience has remained stronger than modeled or cost normalization has progressed more efficiently than expected. Reviewing revenue per passenger metrics and yield data alongside debt and interest expense trends would help isolate which component is driving the outperformance.
From a signal perspective, the focus shifts to whether the nature of the beats is changing. Large, recovery-driven surprises typically compress over time. If Carnival continues to exceed expectations as comparisons normalize, that would indicate a transition from cyclical rebound to more stable operating performance—an important distinction when evaluating the durability of the streak.
Urban Outfitters, Inc. (URBN)
Beat Streak: 8 quarters.
Next quarterly report: May 20 — EPS: $1.12; Revenue: $1.45B (consensus).
An eight-quarter beat streak in specialty retail carries a different implication than in software or industrials. Retail earnings are inherently more volatile, shaped by inventory cycles, promotional intensity, and shifting consumer demand. Sustained outperformance in this segment often points to tighter merchandising discipline and inventory management rather than broad-based demand strength.
Urban Outfitters' recent performance aligns with that framework. The company has navigated a period where many apparel retailers faced margin pressure from excess inventory and discounting. Consistent earnings beats suggest that URBN has managed assortments and pricing more effectively than peers, allowing it to protect margins even as the broader environment remained uneven. Looking at gross margin trends and inventory turnover data would provide a clearer view into whether that advantage is operational or timing-related.
What stands out is the consistency across quarters rather than the magnitude of any single beat. In retail, where outcomes can swing quickly, a multi-quarter pattern signals a repeatable process. The next phase to monitor is whether that process holds as consumer demand patterns shift again—particularly if promotional activity increases across the sector.
Credo Technology Group Holding Ltd (CRDO)
Beat Streak: 6 quarters.
Next quarterly report: June 1 — EPS: $1.03; Revenue: $430.5M (consensus).
Credo's six-quarter streak emerges in a segment closely tied to one of the market's dominant themes: data infrastructure and high-speed connectivity. Compared to the longer streaks in this group, the signal here is earlier-stage, but it sits within a rapidly evolving demand environment driven by data center expansion and AI-related workloads.
In this context, repeated earnings beats often reflect a combination of strong end-market demand and conservative modeling of that demand by analysts. However, shorter streaks require more careful interpretation. They can represent the early phase of a durable trend—or simply a period where estimates have not yet stabilized around a new demand baseline. Evaluating revenue concentration, customer exposure, and sequential growth trends would help determine whether the signal is broad-based or tied to a narrower set of drivers.
The broader semiconductor and connectivity landscape has been characterized by sharp estimate revisions in recent quarters. If Credo continues to outperform as consensus adjusts upward, it suggests that execution is keeping pace with—or exceeding—a rapidly moving demand curve. Tracking analyst estimate dispersion alongside reported results can provide additional context on whether the market is converging around a clearer view of the company's earnings profile.
What Persistent Outperformance Signals Beneath the Surface
Across enterprise software, defense, travel, retail, and semiconductors, the shared signal is not sector strength—it's the persistence of expectation gaps. When companies repeatedly outperform consensus, the focus shifts away from individual quarters toward how forecasts themselves are constructed—and why they continue to lag observable execution.
An earnings beat is inherently backward-looking, but a streak changes the frame. It highlights a pattern in expectation-setting, where consensus adjusts incrementally even as underlying performance proves more stable or efficient than modeled. This tends to emerge in periods of uneven visibility—cost structures in transition, demand signals that are harder to anchor, or capital allocation shifting beneath the surface. Over time, the result is a consistent lag: estimates catching up quarter by quarter rather than resetting in step with the data.
Understanding what drives that gap requires moving beyond the headline surprise. Revenue-driven beats carry different implications than those supported by margin expansion or cost control. Pairing surprise data with income statement trends—such as through FMP's Income Statement API—helps separate growth-led outperformance from operational discipline. Adding cash flow analysis sharpens that view further: when earnings beats are reinforced by steady free cash flow conversion, the signal points to structural durability rather than timing effects. This type of cross-dataset analysis sits at the core of how Financial Modeling Prep frames earnings data within a broader fundamental context.
There is also a behavioral layer in how the market absorbs repeated outperformance. When multiple beats do not translate into proportional upward revisions in estimates or price targets, it suggests a slower recalibration process rather than immediate recognition. Tracking that divergence—between realized performance and consensus adjustment—helps quantify where skepticism or uncertainty still anchors expectations.
Viewed together, persistent outperformance is less about identifying outperformers and more about locating friction in the system. It reflects a mismatch between execution and interpretation—one that, when sustained across sectors and cycles, becomes a signal in its own right.
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.
Broadening the Universe as Coverage Scales
Scaling a repeatability screen works best when the dataset expands in stages. The goal isn't simply to analyze more companies—it's to verify that the screening logic continues to hold as the environment becomes less predictable. A methodology designed to detect persistent earnings beats should first demonstrate stability where information is most complete, before being applied to parts of the market where analyst coverage is thinner and estimates carry wider dispersion.
The logical starting point is the Free plan, where the dataset is largely composed of widely followed large-cap companies such as AAPL, GOOGL, and JPM. These names sit in the most transparent part of the market: analyst coverage is dense, consensus estimates cluster tightly, and earnings expectations tend to be well modeled. If a streak-based screen produces coherent results under those conditions, it suggests the framework itself is sound rather than reacting to data gaps or irregular coverage.
From there, moving into the Starter plan broadens the U.S. universe to include smaller and more specialized companies. In that environment, fewer analysts typically follow each ticker, which means consensus estimates can vary more widely from quarter to quarter. That variability effectively stress-tests the screen. If the same filtering rules still surface repeatable earnings beats, the signal is less likely to be an artifact of tightly modeled large-cap forecasts and more likely to reflect genuine operational consistency.
The next layer comes from geographic expansion through the Premium plan, which introduces additional markets such as the U.K. and Canada. The mechanics of the screen remain unchanged, but the analytical context becomes more complex. Differences in accounting conventions, industry composition, and reporting practices introduce additional variability. Applying identical screening criteria across these regions ensures the methodology is evaluating the same concept of earnings repeatability, regardless of where the company reports.
Viewed as a whole, the staged expansion serves as a practical validation framework. Begin where coverage is deepest, introduce variability through smaller-cap companies, and then extend the analysis internationally. When the signal continues to appear at each stage without requiring constant recalibration, the likelihood increases that the screen is identifying a genuine pattern rather than a quirk in a particular dataset.
From Individual Workflow to Firmwide Analytical Standard
When a screening workflow consistently holds up across several earnings cycles, its relevance naturally extends beyond the analyst who built it. What begins as a desk-level process for identifying repeatable earnings beats often evolves into something broader: a framework the entire research organization can use to evaluate earnings consistency. At that point, the question shifts from “Does this help my coverage universe?” to “Should this become part of how the firm measures operational reliability across sectors?”
In practice, those transitions are rarely initiated from the top down. They tend to emerge from analysts who have worked closest with the data—refining filters, resolving edge cases, and pressure-testing assumptions across multiple reporting seasons. As those definitions stabilize, they offer a path away from fragmented workflows: separate spreadsheets, slightly different sector methodologies, and competing interpretations of the same concept. A shared screening framework replaces that fragmentation with a common analytical baseline that teams can apply consistently across coverage groups.
Standardizing the workflow changes the mechanics of internal research. Shared dashboards replace isolated models maintained by individual desks. Adjustments to thresholds or definitions become visible and reviewable rather than embedded in personal files. Auditability improves because the inputs, filters, and calculations are explicit rather than implied. Governance follows naturally from that transparency. Instead of spending time reconciling why two teams arrived at different numbers, analysts can focus on interpreting what the data actually signals.
Once a workflow reaches that stage, scaling it becomes less about convenience and more about maintaining methodological integrity. Centralized infrastructure—such as the Enterprise plan—allows a process already validated at the desk level to operate across teams with consistent data access, version control, and shared visibility. The objective is not to change the analysis itself, but to ensure that as adoption grows, the logic behind the screen remains consistent across the entire research organization.
Keeping Earnings Consistency in Motion
Earnings consistency is not static—it evolves as expectations catch up and the signal either compresses or persists. Continuing to track that dynamic through the FMP Earnings Surprises Bulk API allows the focus to shift from isolated beats to how expectation gaps develop over time. That transition—between surprise and normalization—is where the signal becomes most informative.
Want more? Explore our earlier article: Weekly Signals Desk | Five Insider Trades That Matter - Tracked via the FMP API
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.
