This week's earnings scan surfaced a quiet but persistent pattern: a small group of companies continuing to deliver clean quarterly beats while broader sentiment remains uneven. Using the FMP Earnings Surprises Bulk API, we ran a fresh sweep across reported results to isolate names extending multi-quarter streaks — not on narrative momentum, but on repeated execution.

The output isn't about one-off surprises. It's about consistency emerging underneath shifting sector leadership, where a handful of firms continue to outpace expectations quarter after quarter. In this piece, we break down what the latest data reveals, how these streaks are identified using the FMP Earnings Surprises Bulk API, and why repeatable performance still matters as market conditions rotate.

Five Companies With Long Earnings Beat Streaks

DDOG Datadog, Inc.

Beat Streak: 20 consecutive quarters.
Next quarterly report: Feb. 12 — EPS: $0.55; Revenue: $916.82M (consensus).

Datadog's streak now spans multiple demand cycles, making it one of the cleanest examples of operational consistency in large-cap software. What stands out is not just the length of the streak, but its durability through periods of enterprise budget scrutiny and shifting cloud spend priorities. Beating expectations across both expansionary and cost-conscious phases suggests disciplined forecasting and sustained usage growth across its observability platform, rather than reliance on short-term consumption spikes.

From an analytical perspective, the pattern reinforces the value of monitoring revenue-per-customer trends and module adoption rates alongside headline EPS. The earnings history indicates that Datadog's execution has remained stable even as sentiment around high-multiple software has reset, making the company a useful reference point when assessing whether operational momentum is truly decoupled from macro compression.

FN Fabrinet

Beat Streak: 14 quarters.
Next quarterly report: Feb. 2 — EPS: $3.26; Revenue: $1.07B (consensus).

Fabrinet's streak reflects something different: manufacturing discipline rather than recurring revenue leverage. The company sits deep in the optical and precision manufacturing supply chain, and its ability to exceed expectations over multiple years suggests consistent cost control and order flow visibility, even as end markets such as data center infrastructure and networking equipment cycle unevenly.

What makes the streak notable is how it has persisted through periods of inventory normalization across hardware ecosystems. Analysts often focus on margin sensitivity and customer concentration here, making Fabrinet's earnings cadence a useful signal for underlying demand stability. Tracking backlog disclosures, utilization trends, and customer mix helps contextualize whether the beat streak is being driven by volume stability, pricing, or operational efficiency.

ALL The Allstate Corporation

Beat Streak: 9 quarters.
Next quarterly report: Feb. 4 — EPS: $8.47; Revenue: $17.23B (consensus).

Allstate's earnings consistency comes from a very different source: underwriting discipline and pricing recalibration rather than growth acceleration. Over the past several quarters, the company has worked through elevated claims costs and catastrophe exposure by tightening underwriting standards and repricing risk. The resulting earnings pattern reflects not expansion, but stabilization.

For insurers, repeat beats often signal that loss ratio assumptions and reserve modeling are converging with reality. In Allstate's case, monitoring combined ratios, catastrophe losses, and premium growth provides more insight than top-line movement alone. The streak suggests execution progress in restoring underwriting balance, a dynamic that tends to show up in earnings data before it becomes fully visible in broader profitability metrics.

FUTU Futu Holdings

Beat Streak: 5 quarters.
Next quarterly report: March 12 — EPS: $3.16; Revenue: $727.60M (consensus).

Futu's earnings pattern reflects a different kind of operating leverage—one tied to market activity and client engagement rather than fixed-cost efficiency. As a digital brokerage with significant exposure to trading volumes and investor participation, its recent string of beats points to resilient engagement levels despite fluctuating market sentiment across regions.

What makes the streak notable is its persistence through periods of uneven retail participation. Tracking account growth, client assets, and trading activity alongside earnings provides context for whether performance is being driven by structural platform usage or short-term market bursts. The consistency suggests that engagement metrics remain supportive even as broader risk appetite ebbs and flows.

HGTY Hagerty, Inc.

Beat Streak: 4 quarters.
Next quarterly report: March 3 — EPS: $0.03; Revenue: $324.94M (consensus).

Hagerty's run of earnings beats reflects steady execution within a niche but loyal customer base. The company's model—blending insurance, membership, and automotive lifestyle services—creates a different earnings profile than traditional insurers, with recurring engagement playing a central role.

The recent streak highlights improving operating leverage as revenue scales against a relatively fixed cost base. For observers, the more informative signals lie in policy retention, membership growth, and loss ratios rather than headline growth rates alone. The consistency suggests that the business is converting brand affinity into predictable financial outcomes, a pattern worth monitoring as the company continues to mature.

Interpreting What Repeatable Beats Are Actually Telling Us

Taken together, these companies don't point to a single sector theme—they point to a shared pattern of execution. The common denominator isn't exposure to a particular macro tailwind, but the ability to consistently align guidance, operations, and outcomes over time. That kind of repeatability tends to surface only when forecasting discipline and internal visibility are working in sync.

The signal becomes clearer when earnings results are evaluated alongside other fundamentals. Comparing surprise patterns with margin behavior, cash flow stability, or balance-sheet trends—using datasets available through the broader FMP platform—helps distinguish durable performance from temporary variance. In several of these cases, earnings consistency appears to be supported by underlying operating stability rather than one-off factors.

What strengthens the interpretation further is how these results hold up across changing market conditions. When companies maintain earnings reliability through shifting rates, demand cycles, or sentiment regimes, it often points to deeper operational control. Viewed through that lens, and supplemented by tools such as post-earnings reaction analysis explored in FMP's article on tracking post-earnings announcement drift, repeat beats become less about surprise and more about sustained execution.

Building a Repeatability Screen with FMP Data

When the goal is to isolate companies that beat expectations consistently rather than intermittently, the starting point matters. The process works best when it begins with the full earnings dataset and lets the data narrow the field organically. Instead of selecting tickers upfront, the idea is to scan every reported quarterly outcome first, then focus only once repeat patterns start to emerge. This is exactly where FMP's Earnings Surprises Bulk API fits into the workflow, providing a broad, uniform snapshot of earnings performance 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

Reviewing the complete sequence of reported quarters allows you to measure how often beats occur and whether they cluster consistently over time. This is where interpretation begins to matter. Some workflows define a streak as three or more consecutive beats; others add filters around minimum surprise magnitude or exclude marginal deviations. The specific thresholds are flexible, but the objective is consistent: distinguish repeatable execution from isolated variance.

At this stage, the screen evolves from a simple event scan into a structured profile of earnings reliability. What starts as a raw list of surprises becomes a framework for identifying companies that demonstrate sustained forecasting accuracy and operational control—traits that tend to persist more meaningfully than one-off quarterly results.

Broadening the Universe as Coverage Scales

A repeatability screen tends to work best when it's rolled out in stages rather than all at once. Starting with a narrower universe makes it easier to validate that the logic behaves as expected before introducing more variability. FMP's Free plan serves that purpose well, covering heavily followed names such as AAPL, GOOGL, and JPM. At that scale, the data is dense enough to stress-test the screening logic without being distorted by thinly covered or irregular reporters.

Once the mechanics are sound, expanding into the Starter plan naturally widens the lens. Bringing in the broader U.S. equity universe introduces smaller companies and more specialized industries, where earnings patterns tend to diverge more meaningfully. This is often where repeatability signals become more informative, as differences in reporting cadence, business models, and estimate quality start to surface more clearly across sectors.

For teams extending the framework further, the Premium plan opens access to international listings, including U.K. and Canadian equities. At that point, the methodology itself remains unchanged—the only variable is the breadth of coverage. Applying the same rules across regions allows consistency profiles to be compared on equal footing, without reengineering the process for each market.

Across each expansion step, the guiding principle stays constant: scale the universe only after the workflow is stable. That sequencing helps preserve signal clarity as coverage grows, ensuring the analysis remains interpretable rather than diluted by unnecessary complexity.

When a Desk-Level Tool Becomes Shared Infrastructure

A screening framework like this stops being just an individual productivity aid once it proves repeatable. At that point, its value shifts from speed to standardization. What begins as a personal workflow—used to surface earnings consistency or validate assumptions—can evolve into shared infrastructure that aligns how teams interpret results and compare signals across coverage areas.

In practice, that transition often starts with analysts codifying their process: defining how streaks are calculated, which filters matter, and how exceptions are handled. Once those rules are explicit, the workflow becomes portable. Other teams can apply the same logic without re-creating it from scratch, reducing discrepancies that tend to emerge when each group builds its own version of “the same” screen. Over time, this shared structure replaces ad hoc spreadsheets with a common analytical language.

At scale, the benefits compound. Centralized dashboards replace scattered files, version control becomes implicit, and assumptions are easier to audit. When multiple desks are working from the same underlying logic, discussions shift away from reconciling inputs and toward interpreting outcomes. That consistency is what allows insights to travel across teams without being re-litigated at every handoff.

This is where a platform-level setup—such as extending the workflow through FMP's Enterprise plan—becomes a practical enabler rather than a feature upgrade. It provides the governance and data continuity needed to support shared use without fragmenting methodology. The result is a research environment where repeatability isn't just measured in earnings data, but embedded in how analysis itself is produced and scaled.

Keeping Earnings Consistency in Motion

As earnings cycles continue to evolve, the value of a repeatability lens lies in how well it stays anchored to fresh data rather than past outcomes. Revisiting streaks through the FMP's Earnings Surprises Bulk API allows consistency to be monitored as a living signal—one that updates as fundamentals shift rather than freezing conclusions in time. Used this way, the framework becomes less about tracking history and more about maintaining an ongoing read on execution as conditions change.

Want more? Explore our earlier article: Weekly Signals Desk | Price vs Target Gaps Emerging via the FMP API (Week of Dec 15-19)

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.