This week's earnings scan surfaced five companies that have repeatedly cleared consensus, even as sector leadership, estimate revisions, and market expectations continue to shift. ADP, Monolithic Power Systems, AMETEK, Hershey, and General Dynamics stand out not for a single upside surprise, but for the consistency of their execution across multiple reporting cycles.
In this article, we use the FMP Earnings Surprises Bulk API to identify these recurring beat patterns, examine what they may signal, and show how the same API can support a repeatable earnings-consistency screen.
Key Takeaways
- The screen identified five companies with persistent earnings-beat streaks, ranging from six quarters at Hershey and General Dynamics to 37 quarters at AMETEK.
- The signal appears across very different business models, suggesting that repeat beats are more closely tied to execution and estimate management than to any single sector.
- A streak is most informative when EPS outperformance is supported by revenue quality, margin stability, cash conversion, and consistent operating trends.
Five Companies With Long Earnings Beat Streaks
Automatic Data Processing, Inc. (ADP)
Beat Streak: 35 quarters.
Next quarterly report: July 29 — EPS: $2.59; Revenue: $5.43B (consensus).
A 35-quarter beat streak points to more than recurring upside against consensus. For ADP, it indicates a long period in which a recurring-revenue model, client retention, payroll volumes, pricing, and interest earned on client funds have collectively remained more predictable than analysts' quarterly forecasts. That distinction matters because the size of each beat can vary, while the repeated ability to clear estimates says more about operating discipline and the quality of management's forecasting framework.
ADP's fiscal third-quarter 2026 revenue increased 7% to $5.9 billion, while adjusted EPS rose 10% to $3.37. Adjusted EBIT margin expanded by 80 basis points to 30.2%, and interest earned on client funds increased 14% to $404 million. The next report should therefore be read across several variables rather than EPS alone: Employer Services growth, client retention, pays per control, margin performance, and the contribution from client-funds interest. Pairing the earnings-surprise history with FMP income statement data and key operating metrics would help show whether the streak continues to reflect broad operating execution or an increasing contribution from interest income and other non-core factors.
Monolithic Power Systems, Inc. (MPWR)
Beat Streak: 27 quarters.
Next quarterly report: July 30 — EPS: $5.85; Revenue: $901.36M (consensus).
MPWR's 27-quarter streak carries a different signal because semiconductor results are exposed to product cycles, customer spending shifts, inventory adjustments, and rapid changes in end-market demand. Sustaining a beat pattern through those conditions suggests that the company has repeatedly managed its product mix and internal expectations with greater consistency than the surrounding industry cycle. The signal is not simply that demand has been strong. It is that MPWR has converted changing demand across enterprise data, communications, automotive, and computing into results that have remained ahead of consensus.
First-quarter 2026 revenue reached a record $804.2 million, up 26.1% year over year. Enterprise Data revenue increased 97.7% to $262.8 million, supported by demand for power-management products used in AI and server applications, while non-GAAP gross margin held at 55.5%. Management's second-quarter revenue outlook of $890 million to $910 million places the current $901.36 million consensus near the center of that range. That makes revenue mix, gross margin, and inventory more informative than the headline beat alone. FMP analyst-estimate data, income statements, and balance-sheet history would help track whether earnings outperformance is being supported by operating leverage and cash generation alongside the rapid expansion in enterprise data exposure.
AMETEK, Inc. (AME)
Beat Streak: 37 quarters.
Next quarterly report: July 30 — EPS: $1.99; Revenue: $1.95B (consensus).
At 37 quarters, AMETEK has the longest earnings-beat streak in this group. The record is notable because the company operates across specialized industrial and electronic-instrument markets, where demand can vary by application, geography, and capital-spending cycle. Its consistency has historically depended on a combination of organic growth, margin discipline, and acquisitions rather than one dominant volume driver. The streak therefore functions as a signal of repeatable execution across a diversified portfolio, but it should still be tested against the contribution from acquired businesses.
AMETEK reported first-quarter 2026 sales of $1.93 billion, an increase of 11%, with adjusted EPS rising 13% to $1.97. Adjusted operating margin expanded 50 basis points to 26.8%, while orders increased 23% and backlog reached a record level. The upcoming EPS consensus of $1.99 also sits near the upper end of management's previously issued second-quarter range of $1.96 to $2.00. The most useful read-through will come from organic sales, order conversion, core margin movement, and the split between acquired and internally generated growth. Combining the earnings screen with FMP income statements, cash-flow statements, and balance-sheet data would make it easier to assess how much of the repeatability is coming from operating improvement versus capital deployed into acquisitions.
The Hershey Company (HSY)
Beat Streak: 6 quarters.
Next quarterly report: July 30 — EPS: $1.46; Revenue: $2.63B (consensus).
Hershey's six-quarter streak is shorter than those of the industrial and technology names, and its information content is more sensitive to pricing, commodity costs, consumer elasticity, and product mix. Repeated beats during a period of volatile cocoa costs suggest that pricing actions, cost controls, and portfolio diversification have allowed reported earnings to remain ahead of expectations. The streak does not remove the underlying pressure from input costs or weaker demand in parts of the confectionery category. Instead, it highlights the company's ability to manage those pressures relative to the assumptions already embedded in consensus.
First-quarter 2026 net sales increased 10.6% to $3.10 billion, while adjusted EPS rose 12.4% to $2.35. The quarter also showed a meaningful split within the portfolio: salty-snack volumes increased 5% and Ice Breakers retail sales rose more than 8%, while volume in the core North America confectionery business declined 4%. For the next report, the central question is how much of the revenue result comes from pricing, underlying volume, acquisitions, and faster-growing adjacent categories. FMP income statement and analyst-estimate datasets, supplemented by company disclosures on price and volume, would help distinguish a high-quality operating beat from one primarily produced by price realization or changes in product mix.
General Dynamics Corporation (GD)
Beat Streak: 6 quarters.
Next quarterly report: July 29 — EPS: $3.92; Revenue: $13.47B (consensus).
General Dynamics also enters the screen with six consecutive beats, but defense and aerospace earnings require a different interpretation. Revenue recognition depends on program milestones, contract timing, aircraft deliveries, production rates, and cost execution across long-duration projects. A sustained beat pattern in this setting suggests that the company has managed those variables more effectively than consensus models anticipated. It does not mean quarterly results are free from timing effects, especially when large awards or deliveries shift between reporting periods.
First-quarter 2026 revenue increased 10.3% to $13.5 billion, with growth across all four operating segments, while diluted EPS rose 12% to $4.10. Orders totaled $26.6 billion, producing a companywide book-to-bill ratio of 2.0, and reported backlog ended the quarter at $130.8 billion. Because orders and backlog do not convert immediately into recognized revenue, the next report is best evaluated through segment margins, Gulfstream deliveries, defense-program execution, and cash conversion. FMP income statement and cash-flow data, combined with backlog and contract information from SEC filings, would show whether the earnings streak is being reinforced by underlying program economics rather than the timing of individual awards.
Decoding the Signal Behind Sustained Earnings Beats
Taken together, these five companies show that an earnings-beat streak is not tied to a single sector, business model, or economic backdrop. ADP's recurring-revenue profile, MPWR's exposure to fast-moving semiconductor demand, AMETEK's acquisition-supported industrial model, Hershey's pricing and commodity dynamics, and General Dynamics' contract-driven backlog all produce earnings through different mechanisms. The shared signal is narrower but still meaningful: each company has repeatedly delivered results above the expectations available before the report.
That pattern should be treated as evidence of forecast reliability, not as a standalone investment conclusion. A long streak may reflect strong operating control, conservative guidance, slow-moving consensus estimates, favorable accounting timing, or some combination of those factors. The analytical task is to determine what is producing the beat and whether the underlying quality has remained consistent. A company that exceeds EPS estimates while margins, cash conversion, or organic revenue weaken is sending a different signal from one where the upside is supported across the financial statements.
The initial Earnings Surprises Bulk API screen is best treated as an entry point rather than a complete signal. Within the broader FMP data framework, income statement and cash flow datasets can show whether repeated EPS beats are supported by revenue growth, operating leverage, and cash conversion, or are being shaped by timing, estimates, or accounting effects. That distinction is especially relevant when comparing businesses with very different capital requirements and revenue-recognition patterns.
Expectations also need to be measured alongside execution. FMP's Financial Estimates API provides the revenue and EPS consensus against which the next result will be judged, while the Price Target Consensus API offers a separate view of how analysts are framing valuation expectations. Comparing those datasets with historical earnings outcomes and end-of-day price data can help identify whether repeated beats are still changing the market's assessment or are already embedded in the share price. Earnings-call transcripts add the final qualitative layer, allowing analysts to test whether management's discussion of margins, demand, costs, and capital allocation supports what appears in the reported numbers.
The strategist's takeaway is straightforward: persistence matters, but composition matters more. The strongest signal is not simply a high count of consecutive beats. It is a repeatable combination of estimate outperformance, durable revenue and margin trends, credible cash generation, and expectations that have not become detached from the operating data.
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:
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https://financialmodelingprep.com/stable/earnings-surprises-bulk?year=2025&apikey=YOUR_API_KEY |
Sample Response:
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[ { "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:
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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
The FMP earnings-surprise dataset provides the starting point, but the real value comes from tracking how each beat is supported by margins, cash flow, and changing expectations. Over time, that broader context separates a durable operating pattern from a streak sustained mainly by favorable comparisons.
Want more? Explore our earlier article: Weekly Signals Desk | Five Notable Valuation Disconnects from the FMP API (June 29-July 3)
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.
