This week's earnings screen wasn't driven by upside shock or narrative catalysts—it was driven by repetition. Running a broad scan through the FMP Earnings Surprises Bulk API surfaced a small group of companies that have quietly made beating estimates routine, not occasional. In a market increasingly sensitive to execution quality over forward storytelling, these streaks stand out as signals of operational control rather than one-off forecasting errors.

This article breaks down how that data was pulled, why repeatable earnings beats matter more in the current tape, and what five specific companies reveal when consistency—not surprise—is the filter.

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).

Celestica's 16-quarter earnings beat streak is notable not for volatility, but for its absence. Over that span, results have reflected steady execution across its advanced technology solutions and capital equipment exposure, even as demand conditions across electronics manufacturing have moved through multiple cycles. The signal here is less about top-line acceleration and more about forecasting discipline—management has repeatedly delivered outcomes that sit just above expectations rather than swinging for outlier quarters.

That consistency matters in a contract manufacturing landscape where margin control, program mix, and customer concentration often introduce noise into quarterly results. Celestica's pattern suggests internal cost visibility and demand planning have remained aligned with external expectations. For readers monitoring durability rather than growth inflection, this is the type of streak that tends to persist until either end-market conditions materially change or estimate baselines reset.

To contextualize this further, company-level segment income statements and customer concentration disclosures would help illustrate where margin stability is being generated. Tracking backlog trends alongside quarterly earnings data would also provide insight into whether the streak reflects structural improvements or simply disciplined guidance management.

LRCX Lam Research Corporation

Beat Streak: 14 quarters.
Next quarterly report: Jan. 28 — EPS: $1.15; Revenue: $5.23B (consensus).

Lam Research's earnings beat streak spans a period marked by sharp swings in semiconductor capital expenditure, making its consistency particularly informative. Over 14 quarters, Lam has navigated memory downturns, logic capacity pauses, and shifting customer investment priorities while still delivering results ahead of consensus. The takeaway isn't immunity to the cycle, but precision in how revenue visibility and cost structure are managed within it.

In equipment names, earnings reliability often hinges on backlog quality and timing rather than absolute demand strength. Lam's repeated beats indicate that shipment schedules, customer readiness, and internal planning assumptions have stayed closely calibrated to reality. This reduces the gap between reported performance and Street expectations, which is increasingly valued during periods of uneven semiconductor spending.

For analysts following this name, order backlog disclosures, regional revenue breakdowns, and wafer fab equipment spending data help anchor the streak in observable operating metrics. Comparing earnings outcomes against changes in backlog conversion rates can clarify whether consistency is being driven by mix, timing, or structural efficiency.

AMZN Amazon.com, Inc.

Beat Streak: 5 quarters.
Next quarterly report: Feb. 5 — EPS: $1.97; Revenue: $211.02B (consensus).

Amazon's five-quarter beat streak carries a different signal than the longer-duration patterns elsewhere on this list. At this scale, consistency reflects operational tightening rather than linear growth. Over recent quarters, earnings delivery has benefited from cost rationalization, logistics efficiency, and margin expansion in higher-contribution segments, particularly AWS and advertising, rather than outsized revenue surprises.

What stands out is that expectations for Amazon are rarely conservative. Beating consensus repeatedly suggests that incremental margin improvements and expense controls have outpaced what analysts were willing to model, even as revenue growth normalized. The streak points to internal execution catching up to scale after a period of heavy reinvestment.

To monitor whether this pattern holds, segment-level operating income, capex trends, and headcount data provide more signal than headline revenue. Analyst estimate revisions across AWS margins and retail fulfillment costs are also useful context for interpreting how expectations are evolving relative to results.

VLO Valero Energy Corporation

Beat Streak: 5 quarters.
Next quarterly report: Jan. 29 — EPS: $2.93; Revenue: $28.43B (consensus).

Valero's earnings beat streak has unfolded against a backdrop of fluctuating crack spreads, shifting product demand, and evolving regulatory pressures. In refining, consistency is rarely about volume growth—it's about throughput optimization, feedstock advantage, and cost discipline. Valero's repeated beats suggest that its asset mix and operational execution have remained resilient despite commodity-driven variability.

Unlike upstream energy names, refiners are more exposed to relative pricing dynamics than outright oil prices. Valero's performance indicates that management has effectively navigated those spreads, maintaining earnings outcomes that exceed expectations even as market assumptions adjusted quarter to quarter.

Readers looking to contextualize this streak should focus on refining margin data, regional crack spreads, and throughput utilization rates rather than revenue alone. Comparing reported results against changes in benchmark margins can help determine whether earnings reliability is being driven by structural advantages or favorable—but potentially transient—market conditions.

NEM Newmont Corporation

Beat Streak: 4 quarters.
Next quarterly report: Feb. 19 — EPS: $1.67; Revenue: $6.12B (consensus).

Newmont's four-quarter earnings beat streak comes during a period of heightened sensitivity to cost inflation, reserve quality, and capital discipline within the gold mining sector. While commodity prices influence the backdrop, Newmont's recent consistency points more directly to operational execution—particularly around cost containment and production stability—than to gold price leverage alone.

In mining, earnings beats often hinge on controllables: all-in sustaining costs, project timing, and operational reliability. Newmont's results suggest that these variables have tracked favorably relative to expectations, allowing reported earnings to clear consensus even as analysts account for macro and commodity uncertainties.

To deepen the analysis, all-in sustaining cost disclosures, production guidance versus actuals, and capital expenditure data are essential. Monitoring how these metrics evolve alongside earnings outcomes provides a clearer view of whether the streak reflects temporary alignment or sustained operational control.

Interpreting What Repeatable Beats Are Actually Telling Us

Viewed together, these five companies don't point to a single sector bet or macro trade. What they surface instead is a shared behavioral pattern: estimates have repeatedly lagged realized performance, even as operating environments shifted. That gap is rarely about hidden growth. More often, it reflects internal visibility—cost control, backlog management, capital allocation—that has proven more stable than the market's forecasting models. In that sense, repeatable beats function less as upside surprises and more as evidence of forecasting asymmetry.

Across this group, the common thread is not scale, industry, or cyclicality, but execution precision. Celestica and Lam Research exhibit this through backlog conversion and margin discipline in capital-intensive models. Amazon's streak reflects incremental efficiency gains finally registering at scale. Valero's consistency highlights operational leverage within volatile refining spreads, while Newmont's results underscore how controllable cost structures can matter as much as commodity pricing. In each case, the streak persists because expectations recalibrate slowly when operational drivers are complex, granular, or unevenly modeled.

This is where a multi-endpoint data approach becomes essential. Earnings surprise data identifies that a gap exists, but rarely explains why it endures. Pairing surprise streaks with income statement detail helps distinguish margin expansion from expense containment. Cash flow data adds another layer, clarifying whether accounting beats are translating into real liquidity. When those results are then compared against analyst estimate revisions and price target histories, a familiar pattern often emerges: consensus adjusts incrementally rather than decisively. That behavior mirrors the mechanics behind post-earnings announcement drift, where market expectations catch up gradually rather than immediately—a dynamic explored in FMP's research on tracking post-earnings drift using market data.

Taken together, repeatable earnings beats are best interpreted as a diagnostic signal, not a conclusion. They highlight where consensus modeling may be structurally conservative, or where business complexity delays accurate expectation-setting. Using a unified data backbone—such as the datasets available across Financial Modeling Prep's platform—allows research teams to test whether operational consistency is being reflected evenly across earnings, cash generation, valuation assumptions, and market response. The analytical value lies not in the streak itself, but in what it reveals about the pace at which fundamentals and expectations are converging as new data arrives.

Building a Repeatability Screen with FMP Data

When the goal is to surface companies that outperform earnings expectations consistently—rather than sporadically—the process has to remain unbiased at the start. That means resisting the urge to predefine a watchlist. Starting with a narrow universe tends to hard-code assumptions into the output. A more reliable method is to scan the full earnings dataset first, capture every reported outcome, and allow repetition to reveal itself organically. This is where FMP's Earnings Surprises Bulk API becomes central, providing a uniform record of quarterly EPS results across the broader 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 holds up best when it's widened in stages, not deployed across the entire market at once. Keeping the initial universe narrow makes it easier to pressure-test the logic before additional variability is introduced. In practice, that's why the workflow often starts with the Free plan, where coverage is concentrated in widely followed companies such as AAPL, GOOGL, and JPM. In that environment, estimate quality is generally tighter and reporting patterns are predictable, which helps confirm that streak definitions and filters are behaving as expected.

After the mechanics are validated, expanding coverage through the Starter plan introduces a broader slice of U.S. equities. This is where dispersion increases. Smaller capitalization names and more specialized industries tend to exhibit wider gaps between estimates and results, making it easier to see whether repeatability is driven by execution or simply by noisy forecasting. Variations in seasonality, margin structure, and analyst coverage become more apparent as the universe opens up.

For teams that take the framework further, the Premium plan extends the same screening logic to international markets, including U.K. and Canadian listings. The underlying methodology remains unchanged at this point. What shifts is the context—different reporting norms and market structures—allowing earnings consistency to be evaluated across regions without rewriting assumptions for each one.

At every stage, the discipline is the same: increase scope only after the workflow has demonstrated stability. Expanding in sequence helps maintain signal integrity as coverage grows, ensuring the screen remains interpretable rather than overwhelmed by added complexity.

When a Desk-Level Tool Becomes Shared Infrastructure

Once a screening workflow proves dependable, its center of gravity inside a firm begins to shift. What initially serves one analyst's coverage needs starts to function as shared analytical plumbing. The priority moves away from individual speed and toward consistency—ensuring that earnings patterns are interpreted the same way across sectors, teams, and coverage universes.

That shift is typically led from the desk, not imposed from above. Analysts who rely on the screen day to day are the ones who formalize definitions, lock in filters, and document how edge cases are handled. By doing so, they turn a personal process into something portable. Other teams can plug into the same framework without rebuilding parallel versions, reducing the quiet fragmentation that tends to emerge when similar analyses are maintained independently.

As usage spreads, the operational advantages become tangible. Shared dashboards replace siloed spreadsheets, assumptions are explicit rather than implicit, and changes to methodology are traceable rather than ad hoc. Review and challenge become easier because everyone is working from the same inputs. The conversation shifts from reconciling numbers to interpreting signals, which is where research time is better spent.

At that stage, extending the workflow through a platform-level setup—such as FMP's Enterprise plan—becomes a structural decision rather than a feature upgrade. It provides a way to support governance, continuity, and firm-wide access without diluting methodology, allowing earnings repeatability to scale not just as a signal, but as a shared research standard.

Keeping Earnings Consistency in Motion

Earnings consistency only carries signal if it's treated as a living input, not a historical label. Revisiting streaks through the FMP Earnings Surprises Bulk API keeps the analysis anchored to current results, allowing patterns to evolve as execution and expectations change. Used this way, repeatability becomes an ongoing read on operational alignment rather than a static scorecard.

Want more? Explore our earlier article: Weekly Signals Desk | Price-Target Divergences Flagged via the FMP API (Dec 29-Jan 2)

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.