Signals Desk Weekly Take via FMP API | Five Companies With Persistent Earnings Beats (Oct 19-23)

Western Digital beat consensus for a fifteenth consecutive quarter on Thursday, guided revenue for the current quarter well above where analysts had it, raised its dividend, and still traded lower the next morning. That reaction captures the tension in this week's screen. A beat streak tells you that a company keeps clearing the bar; it does not tell you whether the bar was set in the right place, or whether the market still cares.

This edition uses FMP's Earnings Surprises Bulk API to surface five companies that extended EPS beat streaks of at least five quarters during the week: Kinsale Capital, Western Digital, Elevance Health, Norfolk Southern and Dow. It then looks at what separates a long, structural streak from a newer one built on reset expectations, and how the API can anchor a repeatable screen for earnings consistency.

Key Takeaways

• Kinsale's 24-quarter run and Western Digital's 15 reflect business models that have repeatedly outpaced analyst models through very different cycles, which gives those streaks more weight than their size alone.
• Elevance, Norfolk Southern and Dow all missed in the same mid-2025 quarter and have beaten in every quarter since, which suggests their streaks owe part of their strength to expectations that were reset lower.
• Western Digital's guidance sits above the current consensus for its next quarter, so the next test of the streak may be against a bar that has yet to move.
• Several of these companies saw muted or negative share reactions despite beats, a reminder that a streak measures execution against estimates, not the market's appetite to pay for it.

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Five Names Extending Their Run of Earnings Beats

Kinsale Capital Group, Inc. (KNSL)

Beat Streak: 24 quarters.
Next quarterly report: Feb. 11 — EPS: $5.65; Revenue: $369.75M (consensus).

Kinsale has now beaten EPS consensus in every quarter since early 2021, a span that includes hard-market pricing, catastrophe-heavy years and, more recently, a softer excess and surplus lines market. That durability says something about the business model. As a specialty insurer that writes small, hard-to-place risks with tight expense control, Kinsale's results depend heavily on underwriting discipline, and analysts have consistently underestimated how much margin that discipline produces.

The third-quarter beat came with a more nuanced picture than earlier ones. Operating earnings rose and underwriting profitability remained strong, but competition in the E&S market pressured written premiums, particularly in commercial property, and higher investment income did more of the work. The company also announced a chief financial officer transition. FMP's Income Statement API separates net investment income from underwriting results across quarters, which is the clearest way to see whether future beats are coming from the insurance book or from the portfolio that funds it.

Western Digital Corporation (WDC)

Beat Streak: 15 quarters.
Next quarterly report: Late Jan. (date TBA) — EPS: $5.15; Revenue: $4.70B (consensus).

Western Digital's streak runs back to the depths of the 2023 storage downturn, when it was beating heavily negative estimates, and it has carried through the recovery into the current AI-driven demand cycle. The fiscal first quarter extended it decisively: revenue grew 55% to $4.38 billion, non-GAAP gross margin expanded to 58.1%, and free cash flow reached about $1.4 billion. Management guided fiscal second-quarter revenue to around $5.1 billion, implying growth of 66% to 72%, and raised the dividend by 17%.

The notable detail is the gap between that guidance and the consensus shown above, which sits closer to $4.7 billion and may not yet reflect post-report revisions. That leaves the next quarter's bar in flux. The shares have fallen sharply since June on concerns about industry capacity additions, capital spending and the durability of hyperscaler demand, and Thursday's sector selloff added to that pressure. FMP's Financial Estimates API shows how quickly analysts move toward the new guidance, while the Cash Flow Statement API tracks whether free cash flow keeps pace as capital expenditure rises.

Elevance Health, Inc. (ELV)

Beat Streak: 5 quarters.
Next quarterly report: Jan. 27 — EPS: $2.17; Revenue: $49.32B (consensus).

Elevance's streak began in the fourth quarter of 2025, immediately after a mid-2025 miss that coincided with a sector-wide reset in managed care as medical cost trends ran well above plan. Since then the company has beaten in five straight quarters, and the latest was the largest by far: adjusted EPS of $6.99 against expectations near $4.70, on revenue of $50.4 billion. Management raised its 2026 outlook and reiterated a target of at least 12% adjusted EPS growth in 2027 from a higher baseline, citing improving Medicaid rates and cost trends alongside stronger Medicare Advantage performance.

A beat of that size raises a question the streak alone cannot answer: how much reflects better operations and how much reflects estimates that were cut too far. The earnings history in FMP's Earnings Report API shows how the size of each surprise has changed since the reset, and pairing it with medical cost and operating margin trends from the Income Statement API helps judge whether the improvement is broad or concentrated in a single quarter's reserve and rate dynamics.

Norfolk Southern Corporation (NSC)

Beat Streak: 5 quarters.
Next quarterly report: Feb. 4 — EPS: $3.20; Revenue: $3.47B (consensus).

Norfolk Southern's run also starts after a mid-2025 miss, and its beats have been steady rather than dramatic. The third quarter added a timely twist: revenue reached a record, helped by volume growth, pricing and fuel surcharges, as elevated diesel costs made trucking more expensive and pushed freight toward rail. Management raised its 2026 capital spending outlook to fund capacity and resiliency, while lifting its operating expense outlook to reflect higher fuel costs.

That combination makes the streak sensitive to an external variable. Fuel works in two directions for a railroad, raising costs while improving its competitive position against trucks, and the net effect has been favorable so far. FMP's Commodities Quote API tracks crude prices as a proxy for that fuel backdrop, and the Financial Ratios TTM API shows whether operating margins hold as capital spending and fuel expense rise together.

Dow Inc. (DOW)

Beat Streak: 5 quarters.
Next quarterly report: Jan. 28 — EPS: $0.47; Revenue: $10.83B (consensus).

Dow's streak is the clearest example of beating a lowered bar. Three of its five consecutive beats came on quarters with negative EPS, where the company simply lost less than analysts expected during a prolonged chemicals downturn. The two most recent quarters have been different. Third-quarter sales rose about 13%, operating EBITDA increased sharply, and margins expanded on price increases and cost actions, with supply disruptions in the Middle East favoring U.S. producers that run on low-cost natural gas.

The question is how much of that improvement is structural. Management has pointed to roughly $1.3 billion of incremental 2027 earnings from transformation and asset actions, which would be within the company's control, while pricing gains tied to supply disruptions are not. FMP's Revenue Product Segmentation API shows which segments are driving the margin recovery, and the Financial Estimates API indicates whether consensus for 2027 is incorporating the self-help program or still anchored to cyclical assumptions.

What Sustained Beats Are Signaling Now

This week's five streaks fall into two clear groups. Kinsale and Western Digital have beaten through multiple phases of their industries' cycles, which suggests a persistent gap between how these businesses perform and how analysts model them. Elevance, Norfolk Southern and Dow share something more specific: each missed in the same mid-2025 quarter and has beaten in every quarter since. Their streaks are real, but they began against expectations that had just been lowered, which means part of the signal comes from the reset itself.

That distinction changes how a streak should be weighed. A five-quarter run that starts immediately after a downward revision cycle carries different information from a 24-quarter run that spans hard and soft markets. The data suggests that streak length, the starting point of the streak, and the trend in surprise size need to be read together. Elevance's widening beats, Dow's shift from narrower losses to rising profits and Norfolk's steady, modest surprises describe three different relationships between results and expectations.

The streak also needs to be tested against what the market does with it. Western Digital's shares fell despite a beat and a raised outlook, as did several AI-linked names that reported this week. That pattern resembles setups in which expectations embedded in price have moved ahead of published estimates, which can make consistent beats less informative for the share price than for the underlying business.

A practical workflow starts with FMP's Earnings Surprises Bulk API to identify streaks, then uses the Earnings Report API to establish where each one began and whether surprise sizes are growing or shrinking. Within the broader FMP platform, the Financial Estimates API shows whether consensus is catching up to guidance, as it has yet to do for Western Digital, while the Income Statement API and Cash Flow Statement API reveal whether beats come from operating performance, investment income or cost timing. Combined, those layers turn a simple count of consecutive beats into a view of which streaks rest on durable execution and which rest mainly on a low bar.

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 Earnings Consistency Forward

A beat streak is most informative when you know where it started and what bar it is clearing. Tracked quarter by quarter through the FMP Earnings Surprises Bulk API, this week's five offer a clean comparison between consistency that has survived full cycles and consistency that is still being tested.

Want more? Explore our earlier article: Weekly Signals Desk | Five Dividend Increases Flagged by the FMP API (Oct 12-16)

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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.