Signals Desk Weekly Take via FMP API | Five Companies With Persistent Earnings Beats (Aug 17-21)

Linde beat consensus by one cent in July. That is the thirtieth consecutive quarter it has cleared the estimate, and it is also the narrowest margin of the run. Held next to the four other names this week's scan surfaced, that detail is the point: these are unusually long streaks, at 49, 48, 30, 28 and 25 quarters, and in three of them the size of the beat is compressing even as the count keeps climbing. A streak is a cumulative measure. What it is made of changes underneath it.

In this article we use FMP's Earnings Surprises Bulk API to identify those streaks, then read each one against the operating evidence behind the most recent print, with particular attention to whether the beat came from demand, from disclosure discipline, or from a line that will not repeat.

Key Takeaways

• All five streaks run 25 quarters or longer, which shifts the question from whether the company can beat to what the estimate-setting process is actually measuring.
• Linde's one-cent beat, Watts Water's margin contraction and Bank of America's markets-led quarter are three different forms of the same strain: an intact streak with a changing composition.
• IQVIA and Arista sit on the opposite side, with a pre-sold backlog and a supply-constrained guidance frame that both keep the bar deliberately low.

Five Streaks That Outlasted the Estimate Cycle

IQVIA Holdings Inc. (IQV)

Beat Streak: 49 quarters.
Next quarterly report: Oct. 27 — EPS: $3.25; Revenue: $4.36B (consensus).

IQVIA has the longest run in the universe, and the record shows no miss anywhere in the available history, which means 49 is a floor rather than a measurement. Second-quarter revenue grew 8.7% and adjusted EPS of $3.15 cleared $3.03. What makes the streak structurally repeatable is disclosed in the same release: contracted backlog of $34.2 billion, with $9.2 billion of that scheduled to convert over the next twelve months. Near-term revenue is largely pre-sold before guidance is set, so management is forecasting against a schedule rather than against demand.

The current quarter is the strongest in that sequence for some time, and not because of the EPS line. Net new bookings in research and development solutions reached a record $3.15 billion for a book-to-bill of 1.22, with trailing twelve-month bookings up 12.9% and rising for a fourth consecutive quarter. Management described request-for-proposal activity as running at record levels with cancellations in the normal range, and pointed to improving win rates against incumbent contract research organisations. Emerging biopharma funding, which underwrites roughly a third of that segment's revenue, roughly doubled year over year. Full-year guidance went up, and the shares rose sharply on the print.

The item worth watching is the composition of the guidance rather than its level, since the raise breaks down into organic improvement, acquisition contribution and a currency drag pulling the other way. FMP's Income Statement Growth API is the useful instrument for a backlog business, because it shows whether revenue and operating income are compounding at similar rates or whether the converted backlog is arriving at lower margin than the work it replaced. That distinction is the whole question for a company where the top line is visible years ahead and the profitability of it is not.

Arista Networks, Inc. (ANET)

Beat Streak: 48 quarters.
Next quarterly report: Nov. 3 — EPS: $1.06; Revenue: $3.33B (consensus).

Arista has beaten consensus in every quarter but its first as a public company. The most recent print was its first above $3 billion in revenue, up 37.7% year over year, with non-GAAP EPS of $1.02 against $0.886 and non-GAAP operating margin near 50%. Full-year revenue guidance moved to roughly $12.6 billion with AI fabrics guided above $3.5 billion, and the shares rose about 12%.

The mechanism behind the streak is a specific piece of guidance behaviour. Management has repeatedly described its visibility as limited to about two quarters and frames its outlook as supply-constrained, which sets a bar that reported results have consistently cleared. That is a durable arrangement while supply is the binding constraint. It is also why the more informative line in this quarter sits below revenue: non-GAAP gross margin fell 220 basis points year over year on customer mix and rising memory and silicon costs, with management characterising the component problem as an industry condition persisting for some years.

Two balance-sheet items deserve equal weight to the income statement here. Purchase commitments rose to $9.7 billion from $8.9 billion and inventory stands at $2.5 billion, which management has acknowledged introduces cash-flow variability, while deferred revenue climbed to $6.9 billion with customer-specific acceptance clauses that can move recognition between quarters. Customer concentration is the other structural feature, with two long-standing accounts above 10% of revenue and management indicating one or two more may cross that line by year end. FMP's Balance Sheet Statement API is where this story is legible, because deferred revenue, inventory and commitments together determine how much discretion exists over the timing of a reported quarter.

Linde plc (LIN)

Beat Streak: 30 quarters.
Next quarterly report: Oct. 30 — EPS: $4.52; Revenue: $9.14B (consensus).

Linde is the clearest case of an intact streak under visible pressure. Adjusted EPS of $4.50 against $4.49 extended the run to thirty quarters by the smallest possible margin. Sales grew 9% as reported and 4% underlying, and the underlying split is where the strain shows: 2% price and 2% volume. Adjusted operating margin fell 60 basis points year over year, its first contraction in some time, on home-care cost inflation in the Americas, lower-margin equipment sales in Asia Pacific, and helium supply disruption.

The streak itself has a straightforward explanation, and it is contractual rather than commercial. Take-or-pay project economics and cost pass-through provisions make industrial gas earnings unusually schedulable, and Linde has habitually guided in narrow ranges set close to what it then delivers. That produces a beat almost by construction, which is precisely why a one-cent result is worth noticing: the machinery still worked, with nothing left over.

The forward evidence is more encouraging than the margin line suggests, though it is concentrated. Backlog reached a record $11.1 billion, split between sale-of-gas and sale-of-plant work, with more than twenty project start-ups expected in the second half. Electronics volumes grew 18%, and the company committed roughly $1.8 billion across Arizona and Taiwan under long-term supply agreements tied to semiconductor fab expansion. So growth is shifting from broad industrial volume toward project and electronics demand. FMP's Financial Ratios API is the right lens for that transition, because the question the data suggests monitoring is whether margin compression is mix, as a heavier equipment and home-care weighting would imply, or whether pricing has stopped outrunning cost inflation.

Watts Water Technologies, Inc. (WTS)

Beat Streak: 28 quarters.
Next quarterly report: Nov. 4 — EPS: $3.16; Revenue: $694.1M (consensus).

Watts Water delivered the largest proportional beat in this group, with adjusted EPS of $3.66 against $3.33, on record sales up 19% as reported and 12% organic. It is also the only mid-cap here, and the streak has a simple foundation: roughly 60% of sales come from repair and replacement work, which is far less discretionary than new construction, and the company has run rolling price increases against a conservatively guided organic range.

Underneath the beat, three things point the other way at once. Adjusted operating margin fell 60 basis points despite about six points of price realisation, on acquisition dilution, inflation and tariffs. Third-quarter organic guidance of 5% to 8% sits well below the 12% just delivered, and management expects pricing contribution to decline sequentially as prior increases lap. And a portion of the quarter was pulled forward, with roughly $5 million of data centre revenue arriving early and about $10 million of wholesale demand brought forward ahead of a systems implementation.

The end-market picture is genuinely two-sided rather than uniformly soft. Data centre sales roughly tripled year over year to 8% of first-half revenue, Europe and Asia Pacific both expanded margin, and full-year guidance went up. Against that, residential was described as slightly worse and non-residential outside data centres as soft. First-half free cash flow fell 6.5% on a tariff-related and strategic inventory build. FMP's Cash Flow Statement API is the practical check here, because a quarter carrying pull-forwards and an inventory build is exactly the configuration where reported earnings and cash generation can separate, and the streak lives on the earnings side of that gap.

Bank of America Corporation (BAC)

Beat Streak: 25 quarters.
Next quarterly report: Oct. 14 — EPS: $1.18; Revenue: $31.35B (consensus).

Bank of America's twenty-five quarter run has not been broken since the middle of 2020. The second quarter produced revenue up 15%, net income up 27%, diluted EPS of $1.21 against $1.13, an efficiency ratio improving by more than 350 basis points, and return on tangible common equity of 17.0%. Full-year net interest income guidance moved to the upper end of its range and the operating leverage target was raised. The shares fell on the day.

That reaction is the most useful fact in the entry, because it indicates expectations had already moved past the disclosed path. The streak's mechanism is disclosure discipline: management pre-commits to net interest income and expense trajectories a quarter or more ahead, consensus tracks that guidance, and the bank has delivered against it. Where the composition has shifted is in what produced the upside. Global Markets net income rose 72% with equities revenue up 70% and investment banking fees up 50%, which is the least schedulable part of the franchise, while the provision came in below consensus as the net charge-off rate improved to 0.47%.

Two asymmetries belong in the frame. Reserve benefit from a falling charge-off rate is finite by construction, and the bank's own disclosed rate sensitivity is lopsided: a 100 basis point rise adds roughly $1.0 billion to net interest income over twelve months while an equivalent cut removes about $2.2 billion. The July dividend increase of 14% and $13.2 billion of first-half repurchases against a $40 billion authorisation support per-share results independently of either. FMP's Key Metrics TTM API is the appropriate reference for a bank, because efficiency ratio, return on tangible equity and capital ratios describe the durability of the result in a way an EPS surprise cannot.

What a Streak Stops Telling You

Past roughly twenty quarters, a beat count stops being a statement about a business and becomes a statement about a relationship between two forecasting processes. IQVIA guides against a disclosed backlog conversion schedule. Arista guides against a supply constraint with two quarters of visibility. Linde guides in narrow ranges backed by contractual pass-through. Bank of America pre-commits to a net interest income and expense path. Watts Water guides organic growth conservatively against a repair-and-replacement base. In every case the streak reflects a company that has chosen to set a bar it can clear, which is a genuine operating virtue and a very different claim from the one a count implies.

That is why the more informative series is surprise magnitude rather than surprise frequency. Linde cleared by a cent. Watts Water cleared by 10% while its margin contracted. Bank of America cleared by 7% on the most cyclical revenue it has. Arista cleared by 15% with gross margin down 220 basis points. IQVIA cleared by 4% with its bookings and backlog both accelerating, which is the only case in the group where the beat and the forward evidence point in the same direction. Ranked by streak length the order is IQVIA, Arista, Linde, Watts, Bank of America. Ranked by whether the beat was supported by the rest of the disclosure, the order rearranges almost completely.

Building that read is a matter of adding depth behind the surprise. The Earnings Surprises Bulk API establishes the population, and the Earnings Report API supplies each company's full quarterly sequence, which is what allows surprise magnitude to be trended rather than merely counted. Tested against income-statement and cash-flow data available through the broader FMP platform, the question becomes whether operating income and operating cash flow moved with revenue or whether the beat was assembled from below the operating line.

Two further datasets separate quality from persistence. FMP's Financial Scores API gives a fast read on balance-sheet and profitability composition, which matters most where a beat coincides with inventory build or rising purchase commitments, as at Watts Water and Arista. And the Owner Earnings API offers an independent construction of distributable cash that does not inherit the adjustments an adjusted-EPS series carries, which is the cleanest available cross-check on whether a twenty-five-quarter record reflects cash economics or accounting consistency. Where those two agree, the streak is evidence. Where they diverge, the divergence is the story.

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.

Carrying the Streak Into the Next Print

Five records this long say less about whether the next quarter clears the estimate than about how much room is left between the two. Running each new result back through the Earnings Surprises Bulk API keeps the surprise margin visible alongside the count, which is where the change shows up first.

Want more? Explore our earlier article: Weekly Signals Desk | Concentrated Analyst Revisions via the FMP API (Aug 10-14)

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.