This week's earnings data scan surfaced five companies quietly extending clean earnings-beat streaks—signals that stand out amid choppy sentiment and ongoing sector rotation. While headline narratives continue to shift, the underlying data is pointing to a narrower group where execution has remained consistent quarter after quarter.

The screen was built off the FMP Earnings Surprises Bulk API, which allows a full-universe sweep of quarterly EPS outcomes before narrowing down to persistence and repeatability. In this note, we walk through how that API was used, why repeat beats matter more than isolated surprises, and what these five names collectively suggest about where operational momentum is still holding.

Five Companies With Long Earnings Beat Streaks

CLS Celestica Inc.

Beat Streak: 16 quarters.
Next quarterly report: Feb. 4 — EPS: $1.73; Revenue: $3.49B (consensus).

A 16-quarter earnings beat streak is rare in any cycle, and Celestica's persistence places it firmly in the category of operational compounders rather than episodic outperformers. What stands out is not a single inflection quarter, but the consistency with which margins and execution have cleared expectations across multiple demand environments, including periods of supply-chain stress and shifting enterprise capex priorities.

The signal here is durability. Celestica's earnings pattern suggests cost discipline and program mix have mattered more than top-line volatility. Investors monitoring this streak typically focus less on headline growth and more on incremental margin behavior and backlog quality—areas where quarter-over-quarter stability tends to reinforce confidence in forward estimates without requiring aggressive revisions.

To contextualize whether this streak remains intact, income statement granularity—particularly segment-level operating margins—offers the most explanatory power. Pairing that with order backlog or customer concentration data can help assess whether the earnings beats are being driven by structural mix improvement or shorter-cycle demand pockets.

COHR Coherent Corp.

Beat Streak: 10 consecutive quarters.
Next quarterly report: Feb. 4 — EPS: $1.21; Revenue: $1.64B (consensus).

Coherent's 10-quarter beat streak spans a period of significant transformation for the company, including integration work and exposure to uneven end markets such as industrial, communications, and semiconductor equipment. Against that backdrop, the ability to repeatedly clear earnings expectations points to execution consistency rather than cyclical tailwinds alone.

The relevance of this streak lies in how expectations have evolved. Analyst estimates have adjusted alongside the company's portfolio changes, yet actual results have continued to land modestly above those resets. That pattern often reflects conservative external modeling rather than aggressive internal guidance, a dynamic worth tracking as visibility shifts across Coherent's served markets.

To deepen the read-through, analyst estimate revisions and segment revenue disclosures are particularly informative. Monitoring how consensus margins evolve relative to management commentary can help determine whether the earnings beats are driven by cost controls, mix, or end-market stabilization.

HST Host Hotels & Resorts

Beat Streak: 4 quarters.
Next quarterly report: Feb. 18 — EPS: $0.47; Revenue: $1.49B (consensus).

Host Hotels' four-quarter beat streak comes during a period when lodging fundamentals have been normalizing after post-pandemic volatility. In that environment, repeated earnings beats often reflect expense management and asset-level optimization rather than accelerating demand.

What makes the streak notable is how it aligns with steady RevPAR trends and disciplined capital allocation. Instead of dramatic estimate gaps, Host's results have tended to exceed expectations at the margin—suggesting consensus may be underestimating the durability of cash flows across its portfolio.

To assess whether this pattern continues, property-level operating metrics and funds-from-operations (FFO) reconciliation are central. Watching how margin assumptions evolve alongside occupancy and average daily rate data provides a clearer lens than revenue growth alone.

VIAV Viavi Solutions Inc.

Beat Streak: 4 quarters.
Next quarterly report: Jan. 29 — EPS: $0.19; Revenue: $365.25M (consensus).

Viavi's earnings beat streak has developed quietly alongside uneven spending trends in telecom and network infrastructure. That backdrop makes repeated beats less about demand acceleration and more about internal execution, cost structure, and revenue mix resilience.

The consistency suggests management has navigated customer spending pauses without allowing earnings expectations to drift materially lower. In many cases, this reflects conservative consensus assumptions around carrier capex rather than a sharp rebound in orders—an important distinction when interpreting the signal.

To refine the outlook, backlog disclosures, book-to-bill ratios, and segment-level revenue splits offer the most insight. When earnings beats coincide with stable backlog trends, the data tends to support the view that execution—not sentiment—is anchoring results.

AEM Agnico Eagle Mines

Beat Streak: 3 quarters.
Next quarterly report: Feb. 12 — EPS: $2.01; Revenue: $3.42B (consensus).

A three-quarter beat streak may look modest relative to others on this list, but in the context of large-cap gold mining, it carries different implications. Earnings volatility in the sector is often tied to input costs, operational variability, and commodity pricing, making consistency harder to sustain even in supportive price environments.

For Agnico Eagle, the recent sequence of beats suggests operational execution has remained disciplined despite fluctuating external conditions. Rather than signaling outsized upside, the streak points to reliability—production, costs, and realized pricing have aligned closely enough with expectations to produce repeatable results.

Here, the most telling datasets are cash cost metrics, sustaining capital expenditures, and production guidance reconciliations. When earnings beats align with stable or improving cost profiles rather than gold price surprises, the signal becomes more about operational control than macro leverage.

Interpreting What Repeatable Beats Are Actually Telling Us

Taken together, these five earnings-beat streaks say less about upside surprise and more about estimate discipline. None of the companies are riding sudden narrative shifts or one-off demand spikes; instead, results have been landing consistently just above where consensus has anchored expectations. In a market defined by selective risk-taking and uneven capital rotation, that kind of persistence often carries more signal than a single standout quarter.

What's notable is where these patterns tend to form. Repeatable beats show up where incremental improvements in cost structure, mix, or execution are not fully reflected in models, rather than where growth is visibly accelerating. Across manufacturing, technology hardware, mining, lodging, and network infrastructure, uncertainty has kept assumptions cautious even as operations quietly stabilize. That gap is often what allows earnings signals to persist quarter after quarter.

From an analytical perspective, earnings surprises are only a starting point. The signal becomes clearer when surprise data is layered with income statements, cash flow trends, and analyst expectations—an approach consistent with how earnings-based signals are stress-tested across cycles, such as in structured backtests that examine portfolio behavior around surprise data. Platforms like FMP make this kind of cross-dataset analysis practical, allowing repeatability to be evaluated as a stability marker rather than a directional call.

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

Looking across the full sequence of reported quarters allows you to measure streak length directly and separate isolated surprises from repeatable execution. The cutoff you apply here is flexible and should reflect the objective of the screen. Some workflows treat three consecutive beats as a meaningful signal; others layer in minimum surprise thresholds to dampen noise from marginal estimates. Regardless of how tight the criteria are, the intent is the same: convert a list of individual beats into a structured repeatability profile that captures both how often the company exceeds expectations and how cleanly it does so.

Broadening the Universe as Coverage Scales

A repeatability screen is easiest to validate when it's introduced gradually. Starting with limited coverage helps confirm that the logic behaves as expected before it's exposed to edge cases. FMP's Free plan provides a practical proving ground, covering widely followed companies such as AAPL, GOOGL, and JPM. That scope is large enough to test streak logic and filtering rules without introducing excess noise from irregular reporters or thinly covered names.

Once the mechanics are working cleanly, expanding to the Starter plan becomes a functional next step. Opening the screen to the full U.S. equity universe changes the texture of the results, bringing in smaller caps and sector-specific operators where earnings cadence and estimate quality can differ materially. That expansion is often where repeatability signals become more informative, as patterns begin to diverge across industries and market-cap tiers.

For teams looking beyond domestic equities, the Premium plan extends coverage to U.K. and Canadian listings. At that point, the methodology itself doesn't change—only the breadth of the dataset does. Keeping the same screening logic across regions makes it easier to compare consistency profiles globally without introducing structural inconsistencies.

Across each stage, the principle is the same: scale coverage only after the workflow is stable. That discipline ensures the signal remains interpretable as the universe grows, rather than being diluted by premature complexity.

When a Desk-Level Tool Becomes Shared Infrastructure

A repeatability screen reaches its full value once it stops being an individual analyst's tool and starts functioning as shared infrastructure. At that point, the benefit is no longer speed or novelty—it's consistency. When multiple desks reference the same earnings-streak logic, discussions shift away from reconciling inputs and toward interpreting outcomes. The signal becomes a common frame of reference rather than a proprietary view.

Analysts are typically the catalyst for that transition. By formalizing how streaks are defined, how surprises are filtered, and which supporting metrics are included, they help convert an ad hoc workflow into something others can rely on. Standardized definitions reduce friction across teams, make assumptions explicit, and allow portfolio managers, strategists, and risk teams to evaluate conclusions using the same underlying data rather than parallel models.

At scale, governance matters as much as insight. Shared dashboards replace isolated spreadsheets, version control replaces silent tweaks, and clear data lineage reduces the need for manual validation. This is where an environment like FMP's Enterprise plan fits naturally into the workflow, providing the structure needed to operationalize a process that has already proven its value at the desk level.

When that shift happens, the framework stops behaving like a personal screen and starts operating as an institutional standard—auditable, repeatable, and portable across teams. The result is less fragmentation, fewer debates over whose numbers to trust, and a cleaner path from raw data to firmwide interpretation.

Keeping Earnings Consistency in Motion

Once a beat streak is established, its value comes from how it evolves—not how long it lasted. Refreshing the framework through the FMP's Earnings Surprises Bulk API keeps prior consistency anchored to live data, making it easier to see where execution continues to reinforce expectations and where the pattern begins to fade. At that point, the streak functions less as history and more as a rolling reference for how discipline holds up as new quarters print.

Want more? Explore our earlier article: Weekly Signals Desk | Multi-Year CAGR Breakouts via the FMP API (Dec 8-12)

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.