This week's data scan flagged something the tape isn't fully pricing: a cluster of companies quietly extending multi-quarter earnings beat streaks while capital rotates unevenly across sectors. In a market where positioning has become crowded and revisions momentum is increasingly selective, repeatable upside surprises matter more than one-off headline beats.

Using the FMP Earnings Surprises Bulk API, we pulled a full-quarter earnings surprise dataset and screened for consistency rather than magnitude. The result is a short list of five companies with sustained beat streaks—names where execution has persistently run ahead of consensus modeling. In this note, we break down what the data shows, how the screen was built, and what repeatability signals may imply beneath the surface.

Five Companies With Long Earnings Beat Streaks

Monolithic Power Systems, Inc. (MPWR)

Beat Streak: 26 quarters.
Next quarterly report: May 7 — EPS: $4.89; Revenue: $781.30M (consensus).

A 26-quarter earnings beat streak is not statistical noise — it reflects a structural pattern. In the case of Monolithic Power Systems, the signal likely ties to disciplined operating execution within analog and power management semiconductors, where product cycles are longer and pricing can be more durable than in more commoditized chip segments. Repeated upside surprises at this duration often point to a combination of conservative guidance, margin resilience, and stable end-market exposure rather than episodic demand spikes.

In a semiconductor landscape that has rotated sharply between AI-linked names and cyclical recovery plays, MPWR's streak stands out as a steadier execution story. Reviewing multi-period income statement data — particularly gross margin stability and operating leverage — would help contextualize whether the beats are driven by mix, pricing power, or cost control. Analysts may also examine backlog commentary and segment-level revenue breakdowns to assess whether strength is concentrated in industrial, automotive, or data infrastructure channels. The durability of this streak suggests forecasting friction rather than headline volatility, which is precisely what a repeatability screen is designed to isolate.

General Motors Company (GM)

Beat Streak: 14 quarters.
Next quarterly report: May 5 — EPS: $2.66; Revenue: $43.76B (consensus).

Fourteen consecutive beats in a capital-intensive, cyclical industry like autos warrants attention. For General Motors, this pattern has occurred through supply chain normalization, EV investment cycles, and evolving pricing dynamics across North America. Sustained upside relative to consensus during a period of margin compression concerns suggests that cost discipline and mix management have exceeded prevailing expectations.

The key interpretive question is whether the beat streak reflects conservative sell-side modeling or operational variability that the market continues to underestimate. Segment-level income statement data — especially North American EBIT margins versus international operations — provides useful context. Additionally, capital allocation disclosures and cash flow statements are relevant, as free cash flow durability has been a focal point in recent investor discussions. In a sector frequently subject to macro narrative swings, repeated earnings outperformance shifts the conversation from demand speculation toward execution metrics that are directly observable.

The Allstate Corporation (ALL)

Beat Streak: 10 quarters.
Next quarterly report: April 29 — EPS: $6.97; Revenue: $17.30B (consensus).

Allstate's 10-quarter streak spans a period marked by elevated catastrophe losses, premium repricing cycles, and regulatory scrutiny in personal lines insurance. Delivering repeated EPS beats in that environment signals more than favorable weather — it implies disciplined underwriting adjustments, rate increases, and expense management that have outpaced consensus assumptions.

For insurers, combined ratio trends and underwriting margins are central to interpreting earnings consistency. Reviewing quarterly income statements alongside catastrophe loss disclosures can clarify whether the beats stem from core underwriting improvement or reserve development. Investment income also plays a role in a higher-rate environment, making balance sheet and portfolio yield data relevant. The streak suggests that consensus models may have lagged the pace of pricing adjustments and cost actions already reflected in reported results. Monitoring those underwriting metrics alongside reserve commentary would help determine whether the pattern reflects structural recalibration or cyclical variability.

Delta Air Lines, Inc. (DAL)

Beat Streak: 5 quarters.
Next quarterly report: April 8 — EPS: $0.72; Revenue: $14.70B (consensus).

Airlines are rarely associated with multi-quarter predictability, making Delta's five consecutive beats notable. The streak has unfolded during a post-pandemic normalization phase characterized by shifting corporate travel demand, capacity discipline, and fuel cost volatility. Consistent outperformance relative to EPS estimates suggests improved yield management and tighter cost control versus prior cycles.

To contextualize the signal, operating margin trends and unit revenue metrics (PRASM) provide insight into whether pricing or load factors are driving the upside. Fuel expense disclosures and hedging commentary are equally important, given their historical impact on quarterly variability. Cash flow and net debt reduction trends would also illuminate whether the earnings beats are translating into balance sheet repair. In a sector where small demand or cost shifts can materially alter results, repeated EPS surprises imply that internal planning assumptions have been more aligned with realized conditions than external forecasts.

Viavi Solutions Inc. (VIAV)

Beat Streak: 5 quarters.
Next quarterly report: May 7 — EPS: $0.22; Revenue: $393.80M (consensus).

Viavi's five-quarter streak occurs against a backdrop of uneven telecom capital expenditure and ongoing 5G infrastructure adjustments. In equipment and optical test markets, order timing and customer budget cycles can introduce volatility, so repeated EPS outperformance suggests either cost flexibility or revenue visibility stronger than consensus expectations imply.

Evaluating segment revenue trends and backlog disclosures would help determine whether the beats reflect end-market stabilization or disciplined expense management. Gross margin progression in the income statement can also indicate whether product mix has shifted toward higher-value solutions. Given the company's exposure to carrier spending cycles, reviewing analyst estimate revisions and target changes over time could further clarify whether the streak is closing a persistent expectation gap. The data suggests that reported performance has consistently exceeded modeled outcomes, a pattern that warrants continued monitoring rather than assumption.

Interpreting What Repeatable Beats Are Actually Telling Us

Across semiconductors, autos, insurance, airlines, and telecom equipment, the common thread is not sector momentum — it is expectation discipline. A 26-quarter streak in power semis, a 14-quarter run in autos, and consistent upside in industries as cyclical as airlines and insurance suggest that consensus models are persistently trailing internal execution. When beats cluster across different economic sensitivities, the pattern points less to synchronized tailwinds and more to structural conservatism embedded in forecasts.

But frequency alone is not sufficient. A repeat beat can stem from durable margin architecture — or from accounting timing. That distinction is where deeper financial context matters. Pairing earnings surprise histories with multi-period margin data from FMP's Income Statement API helps determine whether operating leverage is actually expanding or whether estimates are simply drifting lower. And that analysis becomes more credible when layered against balance sheet quality and accrual trends — the same type of forensic lens outlined in FMP's discussion of quality of earnings and red flags in financial reports. Persistent beats backed by clean cash flow conversion tell a different story than those supported primarily by working capital shifts.

Cash flow provides the second checkpoint. Linking EPS performance to data from FMP's Cash Flow Statement API clarifies whether reported strength converts into durable free cash flow or remains accounting-heavy. In capital-intensive sectors, that conversion ratio often determines whether the beat streak reflects operating discipline or temporary leverage within the model. When cash generation strengthens alongside repeated upside surprises, the signal moves beyond quarterly variance.

The expectations layer is equally important. Comparing realized results against consensus targets and estimate revisions — available through FMP's Analyst Estimates and Price Target APIs — reveals whether the market is recalibrating or remaining anchored to outdated assumptions. Multi-quarter lags between delivery and forward revisions quantify expectation inertia rather than leaving it anecdotal.

Viewed together, repeatability becomes less a headline statistic and more a measurable variable within a broader analytical framework — one that integrates income statement durability, cash flow validation, and expectation drift. Structured properly, the data available across the FMP platform allows that framework to move from observation to verification, separating operational consistency from statistical coincidence.

Building a Repeatability Screen with FMP Data

If the objective is to identify companies that consistently outperform expectations, the screen has to be built from the ground up without bias. Starting with a curated watchlist defeats the purpose — it narrows the field before the data has had a chance to reveal patterns. A cleaner approach is to begin with the full universe of reported earnings outcomes and then let repetition emerge from the dataset itself. FMP's Earnings Surprises Bulk API provides exactly that foundation: a standardized, quarter-level record of EPS actuals versus estimates across a broad equity 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 should expand in stages, not all at once. The logic that works on a tightly followed group of large-cap names should prove itself before being exposed to wider dispersion in estimates and reporting quality.

The natural starting point is the Free plan, where coverage centers on widely tracked companies such as AAPL, GOOGL, and JPM. In that segment, analyst models tend to cluster more tightly and estimate revisions are incremental. That environment makes it easier to validate streak definitions and confirm that the filtering logic behaves as intended. If the screen produces sensible results there, the methodology is likely sound.

Moving into the Starter plan broadens U.S. equity coverage and introduces smaller-cap and more niche businesses. Estimate ranges widen, analyst coverage thins out, and quarter-to-quarter variability increases. That shift is useful. It tests whether repeatable beats are truly the result of operating discipline or simply a byproduct of tighter modeling in heavily covered names. A robust screen should continue to isolate consistency even as dispersion grows.

Expanding further through the Premium plan extends the same framework to international markets, including U.K. and Canadian listings. The mechanics of the screen do not change, but the context does. Reporting standards, industry composition, and margin structures differ across regions. Maintaining the same criteria across these markets helps ensure that repeatability is being measured consistently rather than redefined to fit local characteristics.

The discipline is sequential scaling: confirm the signal holds at one layer before adding complexity. Done properly, coverage expansion enhances interpretability rather than diluting it, allowing earnings repeatability to remain a comparable metric as the universe grows.

From Individual Workflow to Firmwide Analytical Standard

When a screening process demonstrates that it holds up under repeated use, its value inside a firm naturally shifts. What starts as a desk-level solution becomes a candidate for institutional adoption—less about individual efficiency and more about establishing a shared analytical reference point. At that stage, the priority is no longer how quickly one analyst can run the screen, but whether the same earnings patterns are being identified, filtered, and interpreted consistently across coverage teams.

That evolution is typically driven by analysts closest to the work. As a workflow becomes embedded in active coverage, those users are the ones who codify definitions, resolve gray areas, and make assumptions explicit. In practice, that effort replaces fragmented spreadsheets and slightly different logic across sectors with a common framework—one that can be reviewed, challenged, and improved without being rebuilt from scratch each time.

The benefits of standardization surface quickly. Shared dashboards supplant one-off models, methodological changes become visible and auditable, and governance improves because inputs and rules are clearly defined. Just as importantly, conversations across teams move away from reconciling numbers and toward interpreting what the data implies. The result is less duplication, fewer inconsistencies, and more time spent on analysis rather than maintenance.

Once a workflow reaches that point, scaling it through a platform-level setup—such as FMP's Enterprise plan—becomes a matter of durability rather than expansion. It allows a proven desk-level process to support firm-wide usage, auditability, and continuity without compromising the underlying methodology, effectively turning an individual solution into shared research infrastructure.

Keeping Earnings Consistency in Motion

Repeatable beats are not headlines — they are patterns, and patterns compound. Monitoring them systematically through the FMP Earnings Surprises Bulk API keeps the focus on measurable execution rather than narrative swings. The edge is not in reacting to a single quarter, but in tracking which companies continue to outpace expectations as the cycle evolves.

Want more? Explore our earlier article: Weekly Signals Desk | Five Dividend Moves Flagged by the FMP API (Feb 2-6)

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.