This week's earnings scan wasn't about surprise magnitude—it was about persistence. As capital rotates toward operators with tighter execution and forecasting discipline, repeat earnings beats are starting to matter more than one-off upside. Using the FMP Earnings Surprises Bulk API, we ran a broad, assumption-free sweep of reported results and surfaced five companies where beating expectations has become a pattern rather than an exception.

The takeaway isn't headline shock—it's signal durability. When the same names continue to clear estimates across multiple cycles, that consistency tends to show up before sentiment fully reprices it. Below, we break down how this screen was built with the FMP Earnings Surprises Bulk API and why these repeat beats are worth paying attention to right now.

Five Companies With Long Earnings Beat Streaks

RTX RTX Corporation

Beat Streak: 35 quarters.
Next quarterly report: Jan. 27 — EPS: $1.46; Revenue: $22.7B (consensus).

A 35-quarter beat streak places RTX in a category where earnings outcomes are less about quarter-to-quarter volatility and more about institutional process. Across defense and commercial aerospace, reporting consistency at this scale typically reflects tight cost controls, long-cycle contract visibility, and conservative internal forecasting. The signal here isn't acceleration—it's reliability. RTX's results have repeatedly cleared estimates through multiple macro regimes, including supply-chain stress, rate cycles, and shifting defense budgets.

What makes this streak analytically useful is how it reframes expectations risk. When consensus is routinely set below realized outcomes for nearly nine years, it raises questions about how external models are handling backlog conversion, margin normalization, and segment-level cadence. To contextualize this further, segment income statements and backlog disclosures—alongside cash flow statements—are the datasets that best explain how execution is translating into repeatable earnings outcomes rather than episodic beats.

CCL Carnival Corporation

Beat Streak: 13 quarters.
Next quarterly report: March 20 — EPS: $0.17; Revenue: $6.13B (consensus).

Carnival's 13-quarter beat streak stands out precisely because it occurred during an extended recovery phase. In cyclical industries like leisure and travel, consistency often matters more than absolute growth rates. Repeated beats here suggest that internal assumptions around pricing, onboard spending, and capacity utilization have been more disciplined than the market's expectations, even as headline narratives around demand normalization have shifted quarter to quarter.

The analytical value lies in separating operational execution from macro noise. Earnings surprises in this context tend to be driven less by demand spikes and more by incremental cost management and yield optimization. To deepen the read, operating margin trends, debt maturity schedules, and forward booking data are the most relevant datasets—not to forecast outcomes, but to understand whether estimate gaps are being driven by structural modeling conservatism or transitory recovery dynamics.

TSEM Tower Semiconductor Ltd.

Beat Streak: 9 quarters.
Next quarterly report: Feb. 11 — EPS: $0.67; Revenue: $439.8M (consensus).

Tower's nine-quarter streak is notable in a segment where revenue visibility is often narrower and customer concentration higher. Specialty foundries operate on different economics than leading-edge logic players, with longer product lifecycles and more stable end markets. Repeated earnings beats in this environment suggest forecasting precision around utilization rates and contract pricing rather than cyclical upside.

From a signal perspective, this pattern points to alignment between internal planning and customer demand across analog, power, and RF processes. Analysts looking to contextualize the streak would benefit most from examining quarterly revenue by end market, capacity utilization disclosures, and customer concentration data. These datasets help determine whether estimate dispersion reflects genuine uncertainty—or simply underappreciated stability in Tower's operating model.

ADI Analog Devices, Inc.

Beat Streak: 8 quarters.
Next quarterly report: Feb. 18 — EPS: $2.31; Revenue: $3.10B (consensus).

Analog Devices' eight-quarter beat streak spans a period of uneven demand across industrial, automotive, and communications end markets. In analog semiconductors, earnings consistency is often tied to product mix discipline and inventory management rather than volume growth alone. ADI's repeated beats suggest that margin resilience has been more durable than consensus models implied, even as customers adjusted ordering patterns.

The interpretive value here lies in margins rather than revenue growth. Gross margin trajectories, inventory levels, and regional revenue breakdowns are the datasets that best explain how ADI has maintained earnings outperformance amid sector-wide normalization. The streak doesn't signal immunity to cycles—but it does indicate that internal cost and mix assumptions have remained conservative relative to realized outcomes.

TTMI TTM Technologies, Inc.

Beat Streak: 4 quarters.
Next quarterly report: Feb. 4 — EPS: $0.68; Revenue: $752.9M (consensus).

TTM's four-quarter streak is shorter, but it carries weight given the operational leverage embedded in PCB manufacturing. In businesses with thinner margins and higher exposure to volume swings, earnings beats often emerge from incremental execution gains rather than top-line surprises. This makes early streaks analytically useful as potential indicators of stabilization rather than momentum.

What stands out is the transition from sporadic results to repeatable delivery. To assess whether this pattern reflects improved forecasting or temporary tailwinds, investors typically look to segment revenue mix, defense versus commercial exposure, and capital expenditure trends. Income statements paired with order backlog data provide the clearest lens into whether estimate gaps are narrowing because fundamentals are stabilizing—or because expectations remain anchored to older operating assumptions.

Interpreting What Repeatable Beats Are Actually Telling Us

Taken together, these five companies don't form a thematic basket by sector, size, or geography—and that's precisely the point. What links RTX, Carnival, Tower Semiconductor, Analog Devices, and TTM Technologies is not a shared macro tailwind, but a shared pattern of expectation management. Across very different operating models, consensus estimates have repeatedly lagged realized performance. That gap, sustained over multiple reporting cycles, is rarely accidental. It tends to surface when external models struggle to keep pace with how companies internally assess their cost structure, demand visibility, and execution cadence.

From a research perspective, repeatable beats are less about upside surprise and more about informational friction. Markets are generally efficient at absorbing new data, but slower at updating process-level assumptions—how margins normalize, how backlog converts, or how leverage behaves under shifting volume regimes. When that recalibration lags, earnings beats tend to cluster. This is also why consistency needs to be evaluated alongside earnings quality. Patterns that persist quarter after quarter are more informative when tested against the kinds of accounting and cash-flow signals outlined in FMP's discussion on quality of earnings and financial-report red flags, rather than being treated as isolated EPS outcomes.

This is where stitching together multiple datasets becomes essential. Earnings surprise data surfaces the pattern, but it doesn't explain it. To move from signal to interpretation, analysts typically layer operating cash flow and margin trends from income statements against balance sheet structure, segment disclosures, and forward estimate behavior. That kind of multi-angle workflow reflects how institutional research is often built across platforms like the broader Financial Modeling Prep data ecosystem, where surprises, fundamentals, and estimates can be evaluated side by side. When revisions fail to track improvements in cash generation or margin stability, repeated beats become less about forecasting and more about identifying where expectations remain anchored to outdated assumptions.

The practical takeaway isn't that earnings beats predict future outcomes, but that consistency itself is information. It highlights where consensus has been slow to adjust and where analytical focus should shift from “what surprised this quarter” to “why expectations remain sticky.” In that sense, repeatable beats function as a diagnostic lens—directing research effort toward process, discipline, and execution rather than headline-driven narratives.

Building a Repeatability Screen with FMP Data

When the objective is to identify companies that beat earnings expectations consistently—not occasionally—the setup matters as much as the output. The first discipline is avoiding a predefined universe. Starting with a watchlist tends to embed prior beliefs into the screen. A cleaner approach is to begin with the full earnings dataset, capture every reported outcome, and let repetition emerge naturally. That's where FMP's Earnings Surprises Bulk API becomes the foundation, offering a standardized record of quarterly EPS results across a wide coverage set.

As with any automated pull, the first step is simply confirming your API key is active and ready.

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

A repeatability screen is most useful when it's widened with intent, not all at once. In practice, that usually means starting in an environment where estimate behavior is well understood. With the Free plan, coverage is concentrated in heavily followed names like AAPL, GOOGL, and JPM. Consensus estimates in that group tend to be tighter and more stable, which makes it easier to validate streak definitions and confirm that the mechanics of the screen are working as expected before additional noise is introduced.

Once the framework holds up in that setting, expanding through the Starter plan introduces a different texture of data. Broader U.S. equity coverage brings in smaller capitalization companies and more specialized industries, where analyst coverage is thinner and estimate dispersion is wider. That added variability is useful—it helps differentiate between repeatability driven by operational execution and repeatability that simply reflects loose forecasting.

From there, extending coverage via the Premium plan applies the same logic to international markets, including U.K. and Canadian listings. The methodology doesn't change, but the context does. Differences in reporting cadence, margin structure, and market conventions add another layer of interpretation without requiring the screen to be redesigned.

Across each step, the guiding principle is restraint. Coverage expands only after the workflow proves durable at the prior level. That sequencing keeps the signal readable as scale increases, allowing earnings consistency to remain an analytical tool rather than getting lost in added complexity.

From Individual Workflow to Firmwide Analytical Standard

Once a screening workflow proves dependable, its role inside a firm tends to change. What begins as an individual analyst's solution gradually shifts into something more institutional—part of the shared analytical backbone rather than a personal edge. At that point, the emphasis moves away from speed or customization and toward consistency: ensuring that earnings patterns are identified, filtered, and interpreted the same way across teams.

That transition is usually driven from the desk level. Analysts who rely on the workflow in live coverage are the ones who formalize definitions, resolve edge cases, and document assumptions. In doing so, they turn an informal process into a repeatable standard. Instead of parallel spreadsheets and slightly different logic living across sectors, teams begin working from a common framework—one that can be reviewed, challenged, and refined without rebuilding it each time.

The operational benefits show up quickly. Shared dashboards replace one-off models, methodological changes become traceable rather than ad hoc, and governance improves because inputs and rules are explicit. Most importantly, cross-team conversations shift away from reconciling numbers and toward interpreting what the data is signaling. Fragmentation drops, and analytical time is spent where it adds the most value.

At that stage, extending the workflow through a platform-level setup—such as FMP's Enterprise plan—becomes less about feature access and more about durability. It provides a way to support firm-wide usage, auditability, and continuity without diluting the underlying methodology, allowing a proven desk-level process to function as shared research infrastructure rather than an isolated tool.

Keeping Earnings Consistency in Motion

Earnings consistency is not a static signal—it evolves as expectations adjust and operating realities change. Used properly, tools like the FMP Earnings Surprises Bulk API help keep that process dynamic, allowing repeatability to be monitored, challenged, and revalidated as new data comes in. The edge isn't spotting a streak once—it's knowing when it's still telling you something.

Want more? Explore our earlier article: Weekly Signals Desk | Concentrated Analyst Revisions via the FMP API (Jan 12-16)

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.