The latest earnings cycle has started to expose a familiar pattern beneath the surface of sector rotation: companies that consistently outperform expectations are still being repriced more slowly than the fundamentals would suggest. While macro positioning has shifted between defensives, AI-linked growth, commodities, and consumer resilience, a smaller group of names has continued to deliver the same signal quarter after quarter — operational execution running ahead of consensus models.
Using the FMP Earnings Surprises Bulk API, this week's screen isolated five companies with sustained earnings-beat streaks across very different parts of the market, from payments infrastructure and pharmaceuticals to gold mining and wholesale retail. In this article, we break down how the API can be used to systematically identify repeat earnings outperformers — and why persistent surprise patterns still matter even in a market increasingly driven by macro narratives.
Key Takeaways
- Persistent earnings-beat streaks often reveal where consensus models are underestimating operational consistency rather than simply missing one-quarter upside surprises.
- The five companies identified — Mastercard, UNFI, Newmont, Eli Lilly, and Costco — operate in unrelated sectors, yet all showed the same underlying signal: execution holding above expectations across multiple reporting cycles.
- Combining earnings surprise data with broader datasets such as cash flow trends, analyst revisions, margin expansion, and insider activity provides a more complete view of whether earnings consistency is operationally driven or largely cyclical.
Five Companies With Long Earnings Beat Streaks
Mastercard Incorporated (MA)
Beat Streak: 22 quarters.
Next quarterly report: July 30 — EPS: $4.80; Revenue: $9.07B (consensus).
Mastercard's 22-quarter earnings beat streak stands out less because of the magnitude of individual surprises and more because of how consistently the company has maintained operating momentum across very different macro environments. Inflation spikes, rate tightening, shifting travel patterns, and periodic slowdowns in discretionary spending have all tested consumer-facing businesses over the last several years. Yet Mastercard has continued to outperform consensus estimates with unusual regularity, largely because the underlying business is tied to payment velocity rather than isolated product cycles.
Recent payment-network commentary has reinforced that point. Reuters reported that Mastercard continued to post resilient cross-border volume growth in recent quarters, even as markets focused heavily on slowing travel demand and macro uncertainty. Cross-border transaction data remains one of the clearest real-time indicators of consumer and business activity globally because it captures spending behavior before it fully appears in broader economic releases. What matters in Mastercard's case is not simply that volumes remain elevated, but that the company has repeatedly demonstrated pricing power and operating leverage while shifting more revenue mix toward higher-margin services and fraud infrastructure.
From a research perspective, the most useful supporting datasets here are transaction-volume metrics, segment-level revenue mix, and margin trends visible through quarterly income statements. Watching changes in cross-border volumes alongside value-added services revenue can help contextualize whether earnings beats are being driven by broad payment activity or by deeper monetization of Mastercard's network infrastructure. Analyst estimate revisions also become important after long streaks because the signal often shifts from “unexpected upside” toward understanding how quickly consensus models adapt to sustained execution.
United Natural Foods, Inc. (UNFI)
Beat Streak: 7 quarters.
Next quarterly report: June 9 — EPS: $0.772; Revenue: $7.79B (consensus).
UNFI's presence on this screen is structurally different from the other names. Unlike mega-cap platforms or dominant healthcare franchises, UNFI operates in a lower-margin distribution business where earnings consistency is typically harder to sustain. That makes a seven-quarter beat streak more notable than it initially appears. In distribution-heavy industries, small improvements in procurement efficiency, inventory control, or logistics execution can materially affect quarterly profitability, especially when consensus expectations remain conservative.
The company has spent the last several quarters navigating a difficult retail environment marked by uneven grocery demand, changing consumer preferences, and persistent margin pressure across food supply chains. Against that backdrop, repeated earnings outperformance suggests that internal cost controls and operational discipline have remained more stable than analyst models initially assumed. The signal here is less about aggressive top-line acceleration and more about execution reliability inside a historically volatile operating model.
For analysts following the name, inventory turnover, gross margin stability, and free cash flow trends are likely more informative than headline revenue growth alone. Supplier concentration, working-capital management, and debt metrics visible through balance sheet datasets can also help determine whether the streak reflects durable operational improvement or temporary efficiency gains tied to the current cycle. In sectors like food distribution, repeat earnings surprises often emerge from operational normalization long before broader sentiment catches up.
Newmont Corporation (NEM)
Beat Streak: 6 quarters.
Next quarterly report: July 23 — EPS: $2.28; Revenue: $6.63B (consensus).
Newmont's streak arrives during a period when gold producers have moved back into focus as capital flows increasingly respond to geopolitical instability, reserve diversification, and commodity-price volatility. Unlike prior commodity cycles that were driven primarily by speculative positioning, the recent environment has involved a broader institutional rotation toward hard-asset exposure and cash-generating mining operators with scalable production profiles.
What makes Newmont particularly interesting in this context is the relationship between operational consistency and commodity sensitivity. Mining earnings are often viewed as direct reflections of metal prices, but repeated earnings beats usually signal something more nuanced: production discipline, cost management, and portfolio optimization. Sustained outperformance in this industry tends to matter because consensus models frequently struggle to fully capture how operational efficiencies compound during stronger pricing environments.
For research workflows, production-cost data, all-in sustaining cost (AISC) metrics, and quarterly cash flow statements become essential in separating commodity-driven earnings expansion from internally generated efficiency improvements. Reserve replacement trends and capital expenditure disclosures also provide useful context because long earnings streaks in mining can sometimes mask underlying production challenges that emerge later in the cycle. Monitoring insider transactions and analyst target revisions alongside gold-price sensitivity can help contextualize how the market is interpreting Newmont's consistency relative to broader commodity exposure.
Eli Lilly and Company (LLY)
Beat Streak: 6 quarters.
Next quarterly report: Aug. 5 — EPS: $8.64; Revenue: $20.33B (consensus).
Eli Lilly's earnings streak continues to reflect one of the strongest combinations of pricing power, demand expansion, and manufacturing scale currently visible across large-cap pharmaceuticals. The company's GLP-1 portfolio has shifted from being viewed as a high-growth product category to something closer to a structural healthcare allocation theme, particularly as obesity treatments move deeper into mainstream prescribing behavior.
Recent developments have added another layer to that narrative. Reuters reported this month that Lilly's newly launched oral obesity drug, Foundayo, has already reached thousands of prescriptions within weeks of launch, while the company simultaneously committed billions toward additional U.S. manufacturing expansion tied to obesity and metabolic treatment demand. The significance of that investment cycle is not simply sales growth; it reflects management positioning manufacturing capacity as a strategic constraint within the broader obesity-drug market. In practice, supply-chain scalability has become almost as important as clinical performance.
For analysts, prescription-trend datasets, manufacturing-capacity disclosures, and international revenue segmentation are central to understanding whether earnings momentum is broadening or remaining concentrated within a narrow set of therapies. Monitoring reimbursement dynamics, pricing adjustments, and physician adoption rates may also help frame how durable current growth patterns are under changing regulatory and competitive conditions. In healthcare, long earnings-beat streaks often coincide with periods where demand visibility improves faster than consensus assumptions.
Costco Wholesale Corporation (COST)
Beat Streak: 4 quarters.
Next quarterly report: May 28 — EPS: $4.91; Revenue: $69.28B (consensus).
Costco's four-quarter beat streak reflects a different type of consistency than most retailers currently showing up in earnings screens. The company has continued to benefit from membership-driven revenue stability at a time when broader consumer behavior remains uneven across income groups and discretionary categories. That distinction matters because Costco's model is designed around purchase frequency, inventory velocity, and renewal retention rather than short-term merchandising trends alone.
The persistence of earnings beats in this environment suggests that traffic patterns and basket resilience have remained stronger than consensus expectations despite ongoing pressure on household budgets. Costco also continues to occupy an unusual position within retail: value-oriented enough to capture cost-conscious consumers during inflationary periods, but operationally efficient enough to maintain strong margins even while emphasizing pricing discipline. That combination has historically made the company's earnings profile more stable than much of the broader retail sector.
From a data perspective, same-store sales trends, membership renewal rates, and inventory turnover metrics are likely the clearest indicators to monitor alongside quarterly earnings data. Analysts also tend to watch operating margin stability and regional expansion figures closely because Costco's earnings consistency has often been tied to execution efficiency rather than aggressive revenue engineering. In periods where consumer sentiment becomes harder to interpret through survey data alone, warehouse-retail traffic and renewal behavior can function as a more grounded read on household spending priorities.
Decoding the Signal Behind Sustained Earnings Beats
What stands out across these five companies is not sector similarity, valuation profile, or even growth rate. Mastercard operates inside global payment infrastructure, Eli Lilly sits at the center of the obesity-treatment cycle, Newmont is tied to commodity pricing, Costco reflects consumer purchasing behavior, and UNFI works within low-margin food distribution. On the surface, the group has very little in common. The shared signal is operational consistency outperforming the market's modeling process over multiple quarters.
That distinction matters because repeated earnings beats tend to reveal something different from isolated upside surprises. A single quarter can be distorted by one-off cost adjustments, temporary pricing strength, or timing effects. Multi-quarter streaks are harder to dismiss. They usually indicate that analysts are systematically underestimating some combination of demand durability, execution discipline, margin resilience, or capital allocation efficiency. In other words, the market is not simply reacting to good numbers — it is gradually recalibrating its assumptions about how these businesses actually operate under changing macro conditions.
The pattern also highlights how consensus models behave during periods of market rotation. In environments dominated by macro narratives — AI concentration, rate expectations, commodity volatility, defensive positioning — company-specific execution often gets discounted until the evidence becomes repetitive enough to force estimate revisions. That dynamic is visible across this screen. Mastercard's payment volumes, Lilly's manufacturing scale, Costco's renewal-driven retail model, and Newmont's production discipline all represent operational signals that persisted longer than many quarterly estimates initially reflected.
This is where the broader value of integrated market datasets becomes clearer. The Earnings Surprises Bulk API can isolate repeat outperformers at the event level, but the more informative signal emerges when those results are evaluated alongside balance sheet trends, analyst revisions, and cash flow durability within the wider Financial Modeling Prep platform. Comparing recurring earnings beats against Income Statement data, for example, can help determine whether the streak is being driven primarily by expanding margins, operating leverage, or unusually resilient revenue growth relative to consensus assumptions. Pairing analyst price-target revisions with historical cash flow trends can also reveal whether Wall Street expectations are adjusting faster or slower than the underlying operational data itself.
The same framework becomes more informative when balance sheet stability and capital allocation trends are introduced. Companies showing persistent earnings outperformance alongside improving free cash flow conversion, disciplined buyback activity, or stable debt profiles often exhibit a different quality profile than firms relying primarily on cyclical tailwinds. Insider trading data and institutional ownership changes can add another layer of context by showing whether management teams or large holders are materially changing exposure during extended streaks.
At a broader level, sustained earnings beats are less about “beating expectations” and more about identifying where expectations themselves remain structurally incomplete. That does not automatically translate into future outperformance, but it does create a measurable signal worth tracking — especially when it appears across unrelated sectors during periods where market attention is heavily concentrated elsewhere.
Building a Repeatability Screen with FMP Data
Screening for consistent earnings outperformance works best when you start from the entire dataset rather than a curated list of names. Pre-selecting companies introduces bias upfront—especially when the objective is to uncover patterns that may not be obvious within familiar coverage. A cleaner approach is to begin with the full distribution of earnings outcomes and let the filtering process surface repeat performers on its own.
The FMP Earnings Surprises Bulk API is the practical entry point for that process. It standardizes quarterly EPS results against consensus estimates across a broad equity universe, which makes it possible to scan for surprise patterns without needing to stitch together multiple sources. Before running any calls, the only requirement is a valid API key.
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.
Scaling the Screen: Testing the Signal Across Broader Coverage
A repeatability screen earns its credibility not by how many companies it includes, but by how well it holds up as the data environment becomes less predictable. The most effective way to test that is to widen the scope in stages—starting with tightly modeled segments and moving outward into areas where estimates are looser and coverage is thinner. The goal is simple: see if the signal survives as conditions become less controlled.
The first pass sits within the Free plan, where the dataset is anchored in widely covered large-cap names like Apple Inc., Alphabet Inc., and JPMorgan Chase & Co.. This is the most efficiently modeled part of the market—analyst coverage is dense, estimates tend to cluster, and expectations are generally well-defined. If a streak-based approach produces consistent outputs here, it suggests the framework is functioning on stable ground rather than reacting to noise in the data.
Moving into the Starter plan expands the universe to include a wider range of U.S. equities, bringing in smaller-cap and more specialized companies. At this level, estimate dispersion becomes more visible as coverage thins out. That shift acts as a natural pressure test. When the same criteria continue to surface repeat performers, the pattern begins to look less dependent on tightly managed forecasts and more tied to actual operating consistency.
The final step, via the Premium plan, extends the screen across international markets such as the U.K. and Canada. The methodology remains unchanged, but the inputs become more varied. Differences in reporting conventions, sector mix, and estimate reliability introduce additional complexity. Applying the same framework across these regions helps validate that the signal—earnings consistency—remains comparable even as the underlying data becomes more heterogeneous.
From Analyst Workflow to Standardized Research Framework
The transition from individual screening model to firm-wide research infrastructure usually begins quietly. An analyst builds a process to track recurring earnings surprises, another team starts referencing the outputs, and over time the methodology becomes embedded in broader coverage discussions. What starts as a desk-level workflow gradually turns into a standardized framework for evaluating operational consistency across sectors and market regimes.
That evolution is rarely driven by top-down mandates alone. In practice, the strongest push toward standardization tends to come from analysts closest to the data — the people refining filters after each earnings cycle, stress-testing assumptions against new quarters, and identifying where consensus models repeatedly fail to adjust fast enough. Once a workflow demonstrates that it can consistently surface meaningful signals, the inefficiencies of fragmented research processes become harder to ignore.
Most firms already recognize the symptoms: multiple teams maintaining slightly different versions of the same screen, inconsistent threshold definitions across sectors, isolated spreadsheets with limited transparency, and duplicated effort spent reconciling conflicting outputs. Over time, those inconsistencies become operational friction. They slow collaboration, complicate audit trails, and make it difficult to determine whether differences in conclusions come from actual analytical disagreement or simply from variations in methodology.
A standardized framework changes the conversation from “who built the model” to “how the organization defines the signal.” Shared dashboards replace disconnected files. Screening criteria become reviewable rather than buried inside private workflows. Inputs, revisions, and assumptions can be tracked across teams, creating clearer governance around how data-driven conclusions are formed. For research groups operating across multiple sectors or geographies, that consistency becomes increasingly important as workflows expand beyond the analysts who originally designed them.
At that stage, the challenge is no longer discovering the signal — it is preserving methodological consistency as adoption widens across the organization. Centralized infrastructure such as the FMP Enterprise plan becomes relevant not because it changes the analytical framework itself, but because it helps maintain unified data access, version control, and cross-team visibility once the workflow moves beyond a single desk. The objective is continuity: ensuring that the same earnings-consistency signal is being measured the same way across the broader research platform.
Keeping Earnings Consistency in Motion
Quarterly earnings surprises rarely matter in isolation. What tends to hold analytical value is persistence — the point where repeated operational outperformance begins forcing the market to reassess its underlying assumptions. Using the FMP Earnings Surprises Bulk API as a starting point makes it possible to track those patterns systematically rather than react to them one quarter at a time.
Want more? Explore our earlier article: Signals Desk Weekly | Multi-Year CAGR Strength Taking Shape Across Five Names (April 27-May 1)
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.
