Cisco Systems has now cleared earnings expectations for 35 consecutive quarters - a streak that says less about one-off upside and more about how slowly consensus models have adjusted to persistent operational discipline. Broadcom, Intuit, Apple, and IBM are showing similar patterns across very different parts of the market, at a time when investors have started rotating back toward balance-sheet durability and earnings reliability over pure narrative exposure.
This week's screen used the FMP Earnings Surprises Bulk API to isolate companies with sustained earnings-beat streaks and examine what repeated outperformance actually signals beneath the headline numbers. In this article, we'll break down the five companies that surfaced, the logic behind the screen itself, and how the API can be used to systematically track earnings consistency across broader equity coverage.
Key Takeaways
- Cisco, Broadcom, Intuit, Apple, and IBM have all sustained double-digit earnings-beat streaks despite operating in very different sectors and demand environments — pointing to operational consistency rather than isolated upside surprises.
- The screen revealed that repeated earnings outperformance is increasingly concentrated among companies with recurring cash-flow profiles, embedded enterprise relationships, and lower execution volatility.
- Across multiple reporting cycles, the stronger signal was not simply earnings growth itself, but the persistent gap between actual execution and how slowly consensus expectations adjusted to it.
Earnings Beat Streaks Across Five Market Leaders
Cisco Systems, Inc. (CSCO)
Beat Streak: 35 quarters.
Next quarterly report: Aug. 12 — EPS: $1.15; Revenue: $16.70B (consensus).
Cisco's streak stands out less because of the magnitude of individual surprises and more because of the duration. Thirty-five consecutive quarters of beating EPS expectations suggests an organization operating with unusually high forecasting discipline across supply chains, enterprise demand cycles, and margin management. In large-cap infrastructure technology, that level of consistency is difficult to maintain through multiple macro regimes — especially across pandemic-era hardware volatility, post-cycle inventory digestion, and the current AI infrastructure buildout.
The latest earnings cycle added another layer to that signal. Cisco recently raised its annual revenue outlook after reporting stronger AI-related infrastructure demand from hyperscalers, with networking product orders increasing more than 50% year over year and AI infrastructure orders reaching $5.3 billion so far this fiscal year. The company also announced workforce reductions tied to a broader restructuring effort focused on reallocating capital toward silicon, optics, and security infrastructure. That combination — stable earnings execution alongside active strategic repositioning — is often where streak analysis becomes more useful than headline EPS alone.
From a research perspective, this is the type of name where earnings-history datasets become more informative when paired with segment-level revenue trends and cash-flow stability. Looking at networking order growth, operating margin consistency, and capital allocation behavior across multiple quarters can help determine whether the beat pattern is being driven primarily by operational leverage, conservative guidance practices, or structurally improving demand visibility.
Broadcom Inc. (AVGO)
Beat Streak: 17 quarters.
Next quarterly report: June 3 — EPS: $2.40; Revenue: $22.03B (consensus).
Broadcom's earnings consistency has increasingly become tied to one of the market's clearest structural themes: AI infrastructure spending. Unlike companies whose upside surprises depend heavily on cyclical consumer demand, Broadcom's recent reporting periods have reflected sustained enterprise and hyperscaler capital expenditure directed toward networking, custom silicon, and data-center scaling. A 17-quarter beat streak inside that environment suggests not only strong execution, but also unusually durable visibility into customer demand.
Recent developments reinforce that interpretation. Broadcom signed a long-term agreement with Google to co-develop custom AI chips through 2031, extending the company's role deeper into hyperscaler infrastructure planning cycles. That matters because long-duration infrastructure agreements tend to reduce revenue volatility relative to shorter enterprise upgrade cycles. At the same time, it shifts attention toward backlog quality, customer concentration, and the sustainability of AI-driven capex rather than quarterly semiconductor pricing alone.
The underlying signal here becomes easier to evaluate when earnings-surprise history is viewed alongside datasets such as segment revenue composition, customer concentration trends, and analyst estimate revisions. In Broadcom's case, tracking infrastructure-software contribution versus semiconductor revenue can provide additional context around how diversified the earnings stream actually is beneath the headline AI narrative.
Intuit Inc. (INTU)
Beat Streak: 16 quarters.
Next quarterly report: May 20 — EPS: $12.48; Revenue: $8.53B (consensus).
Intuit's streak operates differently from the hardware-oriented names on this list. The signal here is rooted in recurring software monetization, ecosystem retention, and the company's ability to steadily expand average revenue per user across tax, accounting, and small-business financial workflows. Sixteen consecutive quarters of earnings beats in a subscription-heavy software model typically reflects disciplined expense control combined with highly predictable user behavior — particularly during tax-season cycles where demand patterns are historically easier to model.
What makes the current setup more notable is how aggressively Intuit has been integrating AI functionality into its platform stack while maintaining margin stability. The company raised forecasts on stronger demand for AI-driven financial tools and continued QuickBooks expansion. More recently, management acknowledged higher marketing spend tied to customer acquisition efforts during tax season, even as revenue growth remained solid. That combination suggests management is still prioritizing ecosystem expansion rather than optimizing purely for near-term operating efficiency.
For analytical purposes, this is the type of earnings streak that becomes more meaningful when paired with customer-growth metrics, deferred revenue trends, and operating-margin progression. Watching estimate revisions around the Consumer and Global Business Solutions segments can also help contextualize whether earnings consistency is broad-based across the platform or concentrated in a smaller set of mature businesses.
Apple Inc. (AAPL)
Beat Streak: 13 quarters.
Next quarterly report: July 30 — EPS: $1.86; Revenue: $108.29B (consensus).
Apple's inclusion on this list highlights an important distinction between scale and predictability. Large-cap consumer technology companies often face elevated expectations precisely because analyst coverage is so dense. Sustaining a 13-quarter earnings-beat streak inside one of the most heavily modeled equities in global markets suggests a level of operational visibility that extends well beyond product-cycle timing alone.
What continues to separate Apple from many peers is the stability created by its ecosystem economics. Hardware demand still matters, but recurring services revenue, installed-base expansion, and supply-chain discipline increasingly shape earnings outcomes quarter to quarter. In practice, this tends to compress downside volatility around consensus estimates, particularly during periods when broader hardware demand across the industry becomes uneven.
The more useful analytical question is not simply whether Apple beats expectations again, but what the composition of those beats reveals underneath the surface. Segment-level gross margins, services growth rates, geographic revenue mix, and buyback activity often provide more insight than EPS itself. Combining earnings-history datasets with cash-flow statements and analyst target revisions can help clarify whether the consistency signal is being driven primarily by operating leverage, capital returns, or durable consumer retention patterns.
International Business Machines (IBM)
Beat Streak: 13 quarters.
Next quarterly report: July 22 — EPS: $3.02; Revenue: $17.83B (consensus).
IBM's streak reflects a very different kind of earnings consistency than the higher-growth AI infrastructure names currently dominating market attention. In IBM's case, the signal is tied more closely to recurring enterprise relationships, long-duration contracts, and the gradual repositioning of the business toward hybrid cloud and AI-enabled enterprise software. Sustained beats in this type of environment often indicate stable execution against internal efficiency targets rather than explosive top-line acceleration.
That distinction matters because IBM has spent several years reshaping how the market evaluates the company. Instead of competing directly on hyperscale growth metrics, management has focused on improving free cash flow visibility, integrating Red Hat more deeply into enterprise workflows, and positioning generative AI offerings around existing corporate infrastructure rather than consumer-facing adoption cycles. The result is a business profile where earnings variability has become lower even as broader technology-sector volatility has increased.
From a data perspective, IBM is a strong example of why earnings-surprise analysis works best when connected to balance-sheet and cash-flow datasets rather than EPS in isolation. Free cash flow conversion, consulting backlog trends, and hybrid-cloud revenue contribution often provide clearer evidence of operating consistency than headline revenue growth alone. Tracking analyst estimate dispersion across those categories can also help identify where consensus expectations remain less aligned with underlying execution trends.
What Persistent Earnings Outperformance Actually Signals
The common thread across Cisco, Broadcom, Intuit, Apple, and IBM is not sector exposure or growth profile — it is operational predictability. These companies sit in different parts of the market, face different demand cycles, and trade under different valuation frameworks, yet they all continue to produce results that consensus models have adjusted to more slowly than expected. That distinction matters because repeated earnings beats are rarely just accounting events. Over longer periods, they often reflect deeper characteristics: pricing power, disciplined forecasting, stable customer retention, or management teams with unusually strong visibility into demand conditions.
What makes the signal more interesting in the current environment is where these streaks are appearing. Markets over the last several quarters have rewarded AI-linked narratives aggressively, but sustained earnings consistency has increasingly surfaced among companies with mature infrastructure, embedded enterprise relationships, and recurring cash-flow profiles. In other words, the pattern is showing up less in speculative growth stories and more in businesses where execution variance remains structurally lower. That does not automatically make the signal bullish or defensive on its own, but it does suggest that reliability itself has become a more measurable factor in how capital is being allocated across large-cap equities.
The earnings surprise itself is only the starting point. Once a streak is identified through the FMP Earnings Surprises Bulk API, the analysis becomes more useful when layered against other datasets that help explain why the consistency exists. That broader workflow is where platforms like Financial Modeling Prep become more relevant analytically — not because a single endpoint reveals the signal outright, but because combining earnings history with operating-margin trends, free cash flow data, analyst revisions, and insider activity makes it easier to distinguish durable execution from temporary reporting strength. Comparing surprise frequency against Income Statement data, for example, can help separate genuine operating leverage from simple cost containment, while estimate revisions and price-target dispersion often reveal whether consensus expectations are actually catching up to the underlying business trajectory.
The deeper takeaway is that repeat earnings beats tend to become more informative when viewed as part of a broader pattern-recognition framework rather than isolated quarterly outcomes. A single upside surprise can happen for dozens of reasons — tax adjustments, temporary demand pull-forwards, inventory timing, or conservative guidance resets. Sustained streaks across multiple years are different. They usually indicate that some part of the business is operating with a level of consistency the market has not fully modeled in real time. The objective of the screen is not to predict future performance from that observation alone, but to identify where expectations, execution, and market positioning have remained persistently misaligned over extended reporting cycles.
Building a Repeatable Earnings-Consistency Screen With FMP
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.
When Earnings Screens Become Research Infrastructure
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.
Tracking Operational Consistency Beyond the Headline Beat
Earnings streaks become more meaningful when they stop looking isolated and start appearing across different sectors, business models, and reporting cycles at the same time. Using the FMP Earnings Surprises Bulk API as the starting point makes it possible to track those patterns systematically — not just to identify who beat expectations last quarter, but to examine where operational consistency continues to outpace how the market is modeling it.
Want more? Explore our earlier article: Weekly Signals Desk | Price-Target Gaps Identified via the FMP API (May 4-8)
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.
