Signals Desk Weekly Take via FMP API | 5 Companies With Persistent Earnings Beats (Feb 16-20)

This week's earnings scan flagged something more durable than a one-quarter surprise. Using the FMP Earnings Surprises Bulk API, we mapped consecutive EPS beats across the reporting universe and isolated five companies where execution has systematically outrun consensus.

The common thread isn't sector or market cap — it's repetition. In an environment where estimate revisions remain reactive and positioning has rotated quickly across themes, these names have continued to clear the bar quarter after quarter. In this article, we break down how the screen was built with the FMP API and what persistent beat streaks may be signaling beneath the surface.

Five Companies With Long Earnings Beat Streaks

RTX RTX Corporation

Beat Streak: 36 quarters.
Next quarterly report: April 28 — EPS: $1.51; Revenue: $21.3B (consensus).

Thirty-six consecutive quarters of earnings beats is not statistical noise. For an industrial and aerospace complex operator like RTX, that length of consistency reflects disciplined cost control, long-cycle backlog visibility, and calibrated guidance management. In a sector where program execution risk and supply-chain volatility can distort quarterly outcomes, this kind of streak signals internal forecasting stability as much as operational strength.

Recent public filings and earnings coverage have emphasized RTX's sizable defense backlog and commercial aerospace recovery dynamics, particularly tied to Pratt & Whitney and Collins Aerospace programs. The signal here is less about a single platform cycle and more about cash flow normalization and margin repair following prior engine-related disruptions. Reviewing the company's segment-level operating margins in the income statement, alongside backlog disclosures in SEC filings, helps contextualize whether beats are being driven by mix, pricing power, or cost absorption.

With consensus currently at $1.51 in EPS on $21.3B in revenue for the April 28 report, the focus shifts to durability: does free cash flow conversion remain aligned with earnings delivery? Tracking cash flow statements and updates to long-term guidance will matter more than the headline beat itself.

CLS Celestica Inc.

Beat Streak: 26 quarters.
Next quarterly report: April 23 — EPS: $2.07; Revenue: $4.06B (consensus).

Celestica's 26-quarter beat streak stands out within electronic manufacturing services, a segment typically exposed to customer inventory swings and end-market cyclicality. Repeated outperformance suggests effective customer diversification and operational leverage within its advanced technology solutions portfolio, including exposure to data center and industrial programs.

Public commentary around Celestica in recent quarters has highlighted demand tied to AI infrastructure buildouts and higher-value networking programs. That matters because margin expansion in EMS businesses is often tied to product mix and design complexity rather than volume alone. Reviewing gross margin trends in the income statement, along with revenue by end-market segmentation, can clarify whether beats are coming from structural mix improvements or cyclical demand spikes.

With consensus set at $2.07 EPS on $4.06B in revenue for April 23, the signal to monitor is margin trajectory relative to revenue growth. Analyst estimate revisions and target adjustments over the past several quarters provide additional context for whether expectations are beginning to recalibrate closer to management's demonstrated run rate.

LCII LCI Industries

Beat Streak: 9 quarters.
Next quarterly report: May 12 — EPS: $2.31; Revenue: $1.08B (consensus).

LCI Industries operates in a more visibly cyclical space tied to recreational vehicles and outdoor lifestyle components. A nine-quarter streak in that environment indicates cost adaptability and disciplined inventory management through shifting demand cycles. In industries sensitive to consumer discretionary spending and dealer restocking patterns, sustained earnings beats often reflect tight working capital control as much as topline resilience.

Recent earnings commentary in the RV ecosystem has centered on normalization after pandemic-era demand surges and subsequent inventory corrections. For LCII, examining operating margin stability relative to revenue volatility can help determine whether the company is preserving profitability through pricing, procurement efficiencies, or SG&A controls. The balance sheet — particularly inventory and receivables trends — provides an additional lens into operational execution.

With consensus at $2.31 EPS on $1.08B in revenue for May 12, attention should be placed on order commentary and channel inventory data in management disclosures. Comparing quarterly free cash flow and leverage metrics against prior cycles can help assess whether repeat beats reflect structural improvements or short-cycle cost adjustments.

CRDO Credo Technology Group Holding Ltd

Beat Streak: 5 quarters.
Next quarterly report: March 2 — EPS: $0.96; Revenue: $387.62M (consensus).

Credo's five-quarter beat streak comes in the context of elevated investor focus on connectivity silicon and high-speed data center interconnect solutions. In semiconductor segments linked to AI workloads and hyperscale spending, earnings surprises often track with rapid order ramps and shifting capacity utilization.

Public reporting has underscored strong demand for advanced connectivity products across AI infrastructure deployments. The analytical question is whether revenue growth is accompanied by sustainable gross margin expansion, particularly as product mix shifts toward higher-speed solutions. Reviewing quarterly gross margin trends and R&D intensity within the income statement provides insight into how much of the beat cadence is driven by pricing power versus volume acceleration.

With consensus at $0.96 EPS on $387.62M in revenue for the March 2 report, analyst estimate dispersion and revisions data can help frame whether expectations are stabilizing or still adjusting upward. Monitoring capital expenditure disclosures from hyperscale customers may also offer contextual signals about demand durability feeding into future quarters.

HGTY Hagerty, Inc.

Beat Streak: 4 quarters.
Next quarterly report: Feb. 26 — EPS: $0.04; Revenue: $323.93M (consensus).

Hagerty's four-quarter beat streak reflects consistent underwriting performance within a niche insurance segment focused on collectible vehicles. Specialty insurers often exhibit earnings variability tied to claims experience and investment income; repeated beats therefore point to underwriting discipline and effective risk segmentation rather than broad premium expansion alone.

Recent public updates have highlighted growth in policy count and membership-based ecosystem initiatives. The analytical angle centers on combined ratio trends and retention metrics, both available in detailed earnings releases and filings. Improvements in the combined ratio — if present — suggest structural underwriting efficiency rather than temporary claims variability.

With consensus at $0.04 EPS on $323.93M in revenue for the February 26 report, the next layer to examine is investment portfolio yield and reserve development. Reviewing statutory filings and segment disclosures can help determine whether earnings consistency is primarily underwriting-driven or influenced by capital market conditions affecting investment income.

Interpreting What Repeatable Beats Are Actually Telling Us

Across aerospace, electronics manufacturing, RV components, semiconductor connectivity, and specialty insurance, the shared thread is not sector beta — it is disciplined expectation management. A four-quarter streak draws attention; a multi-decade run like RTX's signals a repeatable interaction between guidance, operating control, and how quickly analysts adjust their models.

When companies in unrelated industries consistently clear consensus, it points to a structural lag in the market's forecasting cycle. Cost structures improve, backlog visibility strengthens, or mix shifts upward — yet revisions tend to move incrementally. The result is not just a positive surprise, but a persistent gap between internal performance and external expectations.

Testing whether that gap reflects operating strength rather than timing requires triangulation. Surprise frequency can be compared against multi-quarter revenue, margin, and cash flow progression to determine whether beats are driven by sustainable economics or short-term variability. Situating this workflow within a broader data infrastructure — such as the standardized financial datasets available through Financial Modeling Prep — makes it possible to cross-reference income statements, cash flow conversion, and balance sheet leverage without shifting frameworks.

Viewed in aggregate, repeatable beats are less about short-term direction and more about structural consistency. They highlight where operating cadence continues to outpace modeling cadence — a condition that, when confirmed across income, cash flow, and estimate data, becomes analytically significant rather than narrative-driven.

Building a Repeatability Screen with FMP Data

If the objective is to identify companies that consistently outperform expectations, the screen has to be built from the ground up without bias. Starting with a curated watchlist defeats the purpose — it narrows the field before the data has had a chance to reveal patterns. A cleaner approach is to begin with the full universe of reported earnings outcomes and then let repetition emerge from the dataset itself. FMP's Earnings Surprises Bulk API provides exactly that foundation: a standardized, quarter-level record of EPS actuals versus estimates across a broad equity universe.

As with any automated pull, the first step is simply confirming your API key is active and ready.

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.

Broadening the Universe as Coverage Scales

Expansion should be incremental. A repeatability framework needs to demonstrate internal consistency in a controlled environment before being introduced to wider dispersion in estimates, liquidity, and reporting variability.

The logical entry point is the Free plan, where coverage includes heavily followed large-cap names such as AAPL, GOOGL, and JPM. In that cohort, analyst coverage is dense and estimate dispersion is typically narrow. That makes it an effective testing ground. If streak definitions and filtering thresholds produce coherent results here, the screening logic itself is likely calibrated correctly rather than benefiting from noise.

Stepping into the Starter plan expands the U.S. universe to include smaller-cap and more specialized companies. As coverage thins and estimate ranges widen, volatility increases. This is a deliberate stress test. If repeatable beats persist under conditions of looser modeling and greater quarter-to-quarter variability, the signal reflects operating discipline rather than simply tight consensus clustering.

The Premium plan extends the same framework internationally, incorporating U.K. and Canadian listings. The screening mechanics remain unchanged, but the backdrop shifts — accounting standards, sector mix, and margin structures differ across markets. Applying identical criteria across regions ensures comparability. If the streak logic holds without adjustment, repeatability is being measured consistently rather than retrofitted to local conditions.

The discipline is sequential validation. Confirm the signal at one layer, then widen the aperture. When done carefully, scaling enhances clarity instead of diluting it, allowing earnings consistency to remain a stable analytical metric as coverage broadens.

From Individual Workflow to Firmwide Analytical Standard

When a screening process demonstrates that it holds up under repeated use, its value inside a firm naturally shifts. What starts as a desk-level solution becomes a candidate for institutional adoption—less about individual efficiency and more about establishing a shared analytical reference point. At that stage, the priority is no longer how quickly one analyst can run the screen, but whether the same earnings patterns are being identified, filtered, and interpreted consistently across coverage teams.

That evolution is typically driven by analysts closest to the work. As a workflow becomes embedded in active coverage, those users are the ones who codify definitions, resolve gray areas, and make assumptions explicit. In practice, that effort replaces fragmented spreadsheets and slightly different logic across sectors with a common framework—one that can be reviewed, challenged, and improved without being rebuilt from scratch each time.

The benefits of standardization surface quickly. Shared dashboards supplant one-off models, methodological changes become visible and auditable, and governance improves because inputs and rules are clearly defined. Just as importantly, conversations across teams move away from reconciling numbers and toward interpreting what the data implies. The result is less duplication, fewer inconsistencies, and more time spent on analysis rather than maintenance.

Once a workflow reaches that point, scaling it through a platform-level setup—such as FMP's Enterprise plan—becomes a matter of durability rather than expansion. It allows a proven desk-level process to support firm-wide usage, auditability, and continuity without compromising the underlying methodology, effectively turning an individual solution into shared research infrastructure.

Keeping Earnings Consistency in Motion

Earnings consistency is not a headline event — it's a pattern that reveals itself through disciplined measurement. By continuously tracking surprise frequency through the Earnings Surprises Bulk API, the focus stays on execution versus expectation, not narrative. The signal endures only as long as the data confirms it.

Want more? Explore our earlier article: Signals Desk Weekly | Multi-Year CAGR Strength Taking Shape Across Five Names (Feb 9-13)

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.