This week's earnings scan wasn't about who beat by a penny—it was about who keeps doing it when expectations catch up. As capital rotates toward execution over narrative, repeatable earnings discipline is starting to stand out more clearly than one-off surprises. Running a broad sweep across quarterly results surfaced five companies where beats aren't episodic—they're patterned.

To get there, we leaned on the FMP Earnings Surprises Bulk API as a way to step back from individual headlines and look at earnings performance at scale. This article walks through how that dataset exposes persistence, not prediction—and why streaks are increasingly relevant in the current tape.

Five Companies With Long Earnings Beat Streaks

RingCentral, Inc. (RNG)

Beat Streak: 11 quarters.
Next quarterly report: Feb. 19 — EPS: $1.14; Revenue: $643.45M (consensus).

An 11-quarter beat streak in a maturing cloud communications category is less about acceleration and more about control. RingCentral's results have repeatedly come in ahead of expectations despite a sector backdrop marked by seat optimization, pricing scrutiny, and slower enterprise spend. That consistency suggests the company's forecasting process has converged tightly with its operating reality—an underappreciated signal when growth narratives have cooled.

What stands out is that these beats have persisted even as revenue growth decelerated from earlier-cycle highs. That implies margin discipline and cost structure management are doing more of the work than top-line surprises. For readers tracking durability rather than momentum, this is the type of pattern that tends to show up in companies prioritizing predictability over expansion.

To contextualize the streak, operating margin trends from the income statement help clarify how much of the beat cadence is driven by expense control versus revenue timing. Cash flow statements can further ground whether earnings consistency is translating into balance-sheet flexibility rather than accounting precision alone.

Palantir Technologies Inc. (PLTR)

Beat Streak: 9 quarters.
Next quarterly report: Feb. 2 — EPS: $0.23; Revenue: $1.34B (consensus).

Palantir's nine-quarter run of earnings beats reflects a marked shift from its earlier reputation for uneven profitability. The more recent data points to a company that has narrowed its operating focus, particularly across commercial contracts, while keeping expectations conservative relative to execution. In a name that still draws polarized sentiment, the streak itself functions as a stabilizing counterweight.

Importantly, these beats have occurred as Palantir has emphasized margin expansion and adjusted compensation structures, rather than relying on headline contract wins. That framing matters: repeated beats in this context suggest internal levers—pricing discipline, deployment efficiency, and cost controls—are exerting more influence than deal timing alone.

Analyst estimate revisions and segment-level revenue disclosures are useful datasets to monitor here. They help determine whether consensus is catching up to Palantir's operating cadence or continuing to lag it, which historically has shaped how repeat beats are interpreted rather than simply recorded.

Intuit Inc. (INTU)

Beat Streak: 7 consecutive quarters.
Next quarterly report: Feb. 24 — EPS: $3.65; Revenue: $4.53B (consensus).

Intuit's seven-quarter streak sits in a different category than most earnings-beat narratives. This is a large, widely covered platform business where expectations are already well-informed. Beating consistently at that scale tends to reflect operational fine-tuning rather than informational gaps between the company and the market.

The persistence of beats across multiple fiscal cycles suggests Intuit's monetization cadence—particularly across its ecosystem of tax, accounting, and small-business tools—has been more resilient than near-term macro concerns might imply. That does not point to surprise growth, but rather to steady uptake and retention metrics that are proving easier to model internally than externally.

Deferred revenue trends and segment-level subscription growth data provide additional color on how repeatability is forming. These inputs help separate true execution consistency from timing effects related to tax-seasonality or product rollout schedules.

Five Below, Inc. (FIVE)

Beat Streak: 5 quarters.
Next quarterly report: March 18 — EPS: $3.48; Revenue: $1.60B (consensus).

Five Below's five-quarter streak stands out given the variability typically associated with discretionary retail. Consumer-facing businesses often see earnings outcomes swing with promotions, inventory cycles, and traffic trends, making consecutive beats harder to sustain without structural adjustments.

In this case, the streak suggests a more disciplined balance between inventory management and pricing strategy, particularly as value-oriented retail has drawn incremental traffic in a cautious spending environment. Rather than relying on demand spikes, the data implies steadier throughput and cost alignment quarter to quarter.

Same-store sales metrics and inventory turnover ratios are especially relevant here. They help determine whether earnings consistency is rooted in operational efficiency or supported by temporary demand shifts tied to consumer trade-down behavior.

Delta Air Lines, Inc. (DAL)

Beat Streak: 4 quarters.
Next quarterly report: Jan. 13 — EPS: $1.55; Revenue: $15.76B (consensus).

Airlines are among the most earnings-volatile businesses in public markets, which makes Delta's four-quarter beat streak notable even at a shorter duration. Fuel costs, labor agreements, and capacity adjustments introduce moving variables that often disrupt forecasting accuracy.

Delta's recent pattern points to improved cost visibility and network optimization rather than unusually favorable external conditions. The ability to repeatedly exceed expectations in this environment suggests management's internal assumptions around demand, pricing, and cost pass-through have aligned more closely with realized outcomes.

Unit revenue data, fuel expense disclosures, and capacity utilization metrics are the datasets that best contextualize this streak. Together, they indicate whether earnings consistency is being driven by controllable factors or by transient industry conditions that may be less stable over time.

Interpreting What Repeatable Beats Are Actually Telling Us

Viewed together, these five companies illustrate a pattern that cuts across sectors and market narratives: repeat earnings beats tend to surface where internal operating visibility is tighter than what consensus models reflect. Consistency, in this sense, shows up less in aggressive growth stories and more in environments where variance has been compressed—across costs, cadence, and guidance discipline. That distinction has become more relevant in a market shaped by rotation rather than broad beta, where capital has increasingly gravitated toward outcomes that can be re-anchored quarter after quarter.

What stands out is that the streaks are not driven by a single operating lever. In software and platforms, they often align with expense control and subscription durability. In retail and airlines, they track more closely with inventory management, pricing execution, and cost pass-through. The signal, then, isn't the earnings beat itself, but its persistence across fundamentally different business models—suggesting that internal forecasting frameworks at these companies have stabilized faster than the external models attempting to track them.

That's where combining datasets becomes essential. Earnings surprises can flag consistency, but they rarely explain its source. Pairing streak data with margin trends from income statements helps separate revenue timing from structural efficiency, while balance sheet data clarifies whether repeatability is being reinforced by leverage reduction or working capital discipline. Cash flow analysis adds another layer, particularly when evaluating whether earnings quality is holding up beneath the surface, as outlined in this framework on detecting earnings quality erosion via the cash flow statement.

When those inputs are viewed together—alongside evolving analyst estimates and price target histories—the picture becomes less about upside signals and more about reliability. The FMP platform provides the connective tissue that makes this kind of cross-dataset interpretation practical. In that composite view, repeatable beats act as markers of reduced outcome noise, a trait that tends to matter most when dispersion rises and selectivity replaces broad exposure.

Building a Repeatability Screen with FMP Data

If the objective is to identify companies that beat earnings estimates as a habit rather than by chance, the workflow needs to start wide and narrow only after patterns reveal themselves. Pre-selecting tickers tends to bias the outcome. A cleaner approach is to begin with the full earnings landscape, observe every reported outcome, and let consistency emerge from the data rather than assumptions. This is where FMP's Earnings Surprises Bulk API becomes the backbone of the process, offering a standardized view of quarterly EPS results across the coverage universe.

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 easiest to validate when it's expanded deliberately rather than all at once. Starting with a tighter universe allows the logic to be tested under familiar conditions before introducing additional noise. That's why the workflow typically begins at the Free plan level, where coverage is concentrated in heavily followed names like AAPL, GOOGL, and JPM. At that scale, estimate quality is high and reporting cadence is stable, making it easier to confirm that streak definitions and filters behave as intended.

Once the mechanics are sound, widening the scope through the Starter plan adds a more varied cross-section of U.S. equities. Smaller companies and niche industries introduce greater dispersion in earnings quality and forecasting accuracy, which is often where repeatability signals become more informative. Differences in cost structures, seasonality, and estimate coverage tend to surface more clearly as the universe broadens.

For teams extending the framework further, the Premium plan brings international listings—such as U.K. and Canadian equities—into view. The methodology itself doesn't change at this stage; only the breadth of coverage does. Applying the same screening logic across regions allows earnings consistency to be evaluated on a comparable basis, without tailoring assumptions market by market.

Across each expansion step, the guiding principle stays the same: scale only after the workflow has proven stable. That sequencing helps preserve signal clarity as coverage grows, keeping the screen interpretable rather than diluted as additional complexity is introduced.

When a Desk-Level Tool Becomes Shared Infrastructure

Once a screening framework proves reliable, its role inside an organization naturally changes. What starts as a single analyst's way to track earnings consistency becomes a candidate for institutional use, where the emphasis shifts from speed of discovery to uniformity of interpretation. At that stage, the real value lies in having teams speak the same analytical language when evaluating results across sectors and coverage lists.

That transition is usually driven by analysts themselves. By formalizing how streaks are defined, which filters are applied, and how edge cases are treated, practitioners turn an informal process into something reproducible. Clear rules make the workflow transferable, allowing other desks to adopt it without rebuilding their own versions. Over time, this reduces the drift that tends to appear when similar screens are maintained in parallel, each with slightly different assumptions.

As adoption widens, the structural benefits become more visible. Shared dashboards replace isolated spreadsheets, version control becomes implicit, and underlying assumptions are easier to review or challenge. With a common framework in place, conversations move away from reconciling data inputs and toward evaluating what the signals actually imply. That consistency is what allows insights to move across teams without being reinterpreted at every step.

At that point, a platform-level setup—such as extending the workflow through FMP's Enterprise plan—functions less as an upgrade and more as connective tissue. It supports governance, continuity, and shared access without fragmenting methodology, helping ensure that repeatability is reflected not only in earnings data, but in how research itself is organized and scaled.

Keeping Earnings Consistency in Motion

Earnings consistency only matters if it's monitored as conditions evolve, not archived as a historical artifact. Revisiting streaks through the FMP Earnings Surprises Bulk API keeps the signal tethered to current results, allowing patterns to update as fundamentals shift. Used this way, repeatability becomes an ongoing diagnostic of execution rather than a backward-looking scorecard.

Want more? Explore our earlier article: Weekly Signals Desk | Four 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.