Signals Desk Weekly Take via FMP API | Five Companies With Persistent Earnings Beats (Nov 2-6)

This week's earnings scan ran across 284 S&P 500 and MidCap 400 reporters, and 107 of the companies that beat consensus extended a run of at least five consecutive quarters. Five of them stand out for the length of their records and the range of businesses behind them: Parker-Hannifin, The New York Times, IDEXX Laboratories, Tapestry and Expedia. None depends on the AI hardware cycle that dominated the week's price action, which makes their consistency a useful counterpoint to a tape driven by repricing.

This article uses FMP's Earnings Surprises Bulk API to identify those streaks, then shows how the same dataset supports a broader screen for earnings repeatability, estimate discipline and the quality behind each beat.

Key Takeaways

• Parker-Hannifin has now beaten EPS consensus for 45 consecutive quarters, a record that spans the pandemic, a rate cycle and a major acquisition program.
• The size of the beats varies widely: Parker and Tapestry cleared estimates by wide margins, while The New York Times tends to edge past them, which points to different relationships between management guidance and analyst models.
• Revenue surprises were smaller than EPS surprises across most of the group, suggesting margin execution, not top-line upside, is doing much of the work.
• A streak is most informative when surprise history is read alongside estimate revisions, segment mix and margin trends.

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Five Companies Extending Their Beat Records

Parker-Hannifin Corporation (PH)

Beat Streak: 45 quarters.
Next quarterly report: Feb. 4 — EPS: $8.55; Revenue: $6.08B (consensus).

A 45-quarter streak in a cyclical industrial business is unusual enough that the question shifts from whether Parker can beat to what keeps the gap open. The company has consistently converted volume into margin faster than consensus anticipates, a pattern tied to its operating system, pricing discipline and the integration of large acquisitions. In fiscal first-quarter results this week, sales rose to a record, organic growth reached double digits, adjusted segment margins expanded again and management raised its full-year outlook for growth, margins, EPS and free cash flow, citing record backlog.

What makes the latest quarter informative is its breadth. Management described increased demand across all market verticals, which reduces the risk that the streak rests on a single strong end market such as aerospace. The next test is whether analysts move estimates up enough to narrow the gap, or whether conservative guidance keeps the bar within reach. Separating organic and acquired growth through FMP's Revenue Product Segmentation API, then tracking how forward EPS shifts in the Financial Estimates API after this raise, would show whether consensus is catching up with the company's margin trajectory.

The New York Times Company (NYT)

Beat Streak: 18 quarters.
Next quarterly report: Feb. 3 — EPS: $0.93; Revenue: $868.5M (consensus).

The New York Times offers a different kind of streak. Its EPS beats are typically small in absolute terms, and third-quarter revenue landed almost exactly on consensus. That profile suggests a company whose subscription model makes revenue highly predictable, with the beat coming from cost control and operating leverage rather than demand upside. Revenue still grew close to 10% year over year, so the streak is built on a steadily expanding base rather than a flat one.

Small, repeated beats can reflect tight guidance practices as much as outperformance, which places more weight on margin direction than on the size of each surprise. The relevant questions are whether digital subscription growth and average revenue per user continue to lift operating margins, and how advertising and licensing contribute around that core. FMP's Income Statement API would show whether operating margin is expanding quarter over quarter, and the Key Metrics TTM API would help assess whether that margin progress is translating into free cash flow per share.

IDEXX Laboratories, Inc. (IDXX)

Beat Streak: 17 quarters.
Next quarterly report: Feb. 1 — EPS: $3.31; Revenue: $1.18B (consensus).

IDEXX's 17-quarter streak has been assembled through a period when veterinary visit volumes were soft for much of the industry. Third-quarter revenue grew 10%, entirely organic, led by its Companion Animal Group, and EPS rose faster than revenue. That combination points to pricing power and a growing installed base of diagnostic instruments that generate recurring consumable and reference lab revenue, even when clinic traffic is uneven.

The streak's durability therefore rests on the recurring diagnostics model more than on the broader pet care cycle. What deserves monitoring is the balance between price and volume: a streak driven mainly by pricing can be more exposed if clinics push back, while one supported by rising test utilization is broader. Breaking down revenue by segment through FMP's Revenue Product Segmentation API, and comparing gross margin trends in the Income Statement API, would help clarify how much of the consistency comes from instrument placements and testing volume versus price realization.

Tapestry, Inc. (TPR)

Beat Streak: 13 quarters.
Next quarterly report: Feb. 4 — EPS: $3.02; Revenue: $2.65B (consensus).

Tapestry's fiscal first-quarter report extended its streak with a comfortable beat on both earnings and revenue, and management raised its full-year outlook, describing growth ahead of expectations across regions. The shares responded strongly during the week, a notable reaction for a consumer discretionary name in a week dominated by selling in AI hardware. The streak has been driven largely by Coach, where pricing, product mix and a younger customer base have supported gross margin expansion.

The next report covers the holiday quarter, the most important period of the year, and consensus already embeds a substantial step up in earnings. That raises the bar for the streak at precisely the moment when consumer spending is hardest to forecast. Comparing regional performance through FMP's Revenue Geographic Segmentation API would show whether growth is broad or concentrated in North America and China, and the Stock Price Change API would put this week's rally in the context of how the market has priced earlier beats.

Expedia Group, Inc. (EXPE)

Beat Streak: 7 quarters.
Next quarterly report: Feb. 11 — EPS: $4.50; Revenue: $3.74B (consensus).

Expedia's streak is the shortest in the group, and it began after a miss in early 2025, which makes it a recovery story rather than a long-standing pattern. Third-quarter results showed room nights and gross bookings growing at a healthy pace, with the business-to-business segment, which powers travel for partners, growing nearly three times as fast as the consumer side. Adjusted net income grew faster than revenue, extending the margin improvement that has accompanied the streak.

The composition of growth is the key analytical point. B2B carries different economics from the consumer brands and depends on partner volumes and contract terms, so a streak led by that segment needs to be read through its unit economics rather than consolidated revenue alone. FMP's Revenue Product Segmentation API would show how much of the growth comes from B2B versus B2C, and the Cash Flow Statement API would indicate whether the margin gains are converting into free cash flow given the seasonality of travel bookings.

What Durable Beats Reveal in a Repricing Market

Taken together, these five streaks share one feature that mattered this week: none depends on the AI hardware cycle that drove the market's sharpest moves. Parker's industrial breadth, the Times' subscription base, IDEXX's diagnostic consumables, Tapestry's brand pricing and Expedia's B2B platform each generate a different kind of repeatable result. The common thread is that reported earnings have kept running ahead of consensus through very different macro conditions, which suggests analyst models have been slow to adjust to each company's operating leverage.

The pattern in surprise magnitude adds nuance. Across most of the group, EPS beats were larger in relative terms than revenue beats, pointing to margin execution as the main source of outperformance. That matters because margin-driven streaks depend on cost discipline and pricing holding up, while revenue-driven streaks depend on demand. The data suggests these companies are being underestimated on efficiency more than on growth, which is a different question for each upcoming report.

Testing that interpretation means moving beyond the surprise record within the FMP platform, where earnings, estimates and financial statements are aligned by company and period. The Earnings Surprises Bulk API identifies the streaks, and the Earnings Report API supplies the full quarter-by-quarter history and upcoming consensus for each name. The Financial Estimates API then shows whether forward EPS and revenue expectations rise after each beat or stay anchored, which is the clearest sign of whether consensus is adapting.

Margin and mix data provide the final layer. The Income Statement API traces operating and gross margins over the same quarters as the streak, while the Revenue Product Segmentation API shows whether outperformance is broad or concentrated in one segment. When rising estimates, stable margins and broad segment growth line up, the streak reflects durable execution. When estimates stall while beats continue, the gap may say more about conservative guidance than about the business itself.

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.

Consistency Worth Re-Testing Each Quarter

A long streak earns attention, but each new report decides whether the execution behind it is still running ahead of expectations. Tracking these records through the FMP Earnings Surprises Bulk API keeps that judgment anchored in reported results as the February reporting season approaches.

Want more? Explore our earlier article: Weekly Signals Desk | Five Dividend Increases Flagged by the FMP API (Oct 26-30)

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.