Signals Desk Weekly Take via FMP API | Five Companies With Persistent Earnings Beats (Oct 5-9)

This week's screen surfaced five companies whose earnings streaks are about to meet reporting season head-on. Goldman Sachs reports on Tuesday and BlackRock on Wednesday, ADP and Flex follow on October 28, and PepsiCo already extended its run on Thursday. The streaks range from six quarters to thirty-six, but the more revealing pattern is in the size of the beats: some companies clear consensus by a near-identical margin every quarter, while others clear it by amounts that swing widely with market conditions.

Using FMP's Earnings Surprises Bulk API, this edition identifies those streaks, examines what each one is actually measuring, and shows how the API supports a broader screen for earnings consistency. A beat is counted only when reported EPS is strictly above the estimate; a tie ends the run.

Key Takeaways

• Four of the five streaks face a direct test within the next three weeks, which makes this a near-term check on whether consistency survives a new quarter of data.
• ADP's beats cluster tightly around 2%, a pattern that points to disciplined guidance, while Goldman Sachs and BlackRock beat by margins that move with markets and trading activity.
• PepsiCo extended its streak on Thursday while lowering its full-year core EPS outlook, showing that a quarterly beat and a weaker annual trajectory can coexist.
• Reading streak length alongside beat size, estimate revisions and cash conversion separates forecasting discipline from genuine operating surprise.

Five Earnings Streaks Heading Into Reporting Season

Automatic Data Processing, Inc. (ADP)

Beat Streak: 36 quarters.
Next quarterly report: Oct. 28 — EPS: $2.77; Revenue: $5.48B (consensus).

ADP has exceeded the EPS estimate every quarter since late 2017, the longest run in this week's screen. What stands out in recent quarters is not the count but the consistency of the margin: the last four beats each landed at roughly 2% above consensus. That regularity is characteristic of a business with highly recurring payroll revenue and a management team that sets guidance it can reliably clear, so the streak says as much about forecasting discipline as about upside surprise.

The fiscal 2027 outlook frames what the October 28 report needs to show. Management guided to revenue growth of 5% to 6% and adjusted EPS growth of 9% to 11%, but assumed U.S. pays per control would rise only 0% to 1%, reflecting a slower hiring environment. A growing share of the earnings path depends on interest earned on client funds, which management expects to rise as average yields climb. FMP's Financial Estimates API shows whether consensus has drifted from that guidance, and the Treasury Rates API puts the client-fund yield assumption in context, since a shift in short-term rates would affect that line directly.

Flex Ltd. (FLEX)

Beat Streak: 27 quarters.
Next quarterly report: Oct. 28 — EPS: $1.06; Revenue: $8.17B (consensus).

Flex's run began in early 2020, immediately after a quarter in which EPS matched the estimate exactly. Unlike ADP, its recent beats have been wider and less uniform, ranging from roughly 5% to more than 11%, and revenue has also come in well ahead of expectations, including in the most recent quarter. For a contract manufacturer, that combination suggests demand has repeatedly outrun the volume assumptions in analyst models rather than margins doing all the work.

Much of that demand traces to data center infrastructure, where Flex supplies power and cooling systems as well as rack-level integration for hyperscale customers. That exposure explains both the size of the beats and the sensitivity of the stock to sentiment around AI capital spending, which turned cautious across hardware names this week. FMP's Revenue Product Segmentation API isolates how much of the growth now comes from data center work, and the Key Metrics TTM API shows whether returns on invested capital are improving alongside that mix shift, a useful test of whether the growth is high quality.

BlackRock, Inc. (BLK)

Beat Streak: 16 quarters.
Next quarterly report: Oct. 14 — EPS: $14.33; Revenue: $7.43B (consensus).

BlackRock's streak dates to late 2022, and the beats have typically landed between about 2% and 10% above consensus. The second quarter of 2026 showed why: assets under management reached a record of more than $15 trillion, net inflows for the first half were the strongest on record, and organic base fee growth ran at 8%. Revenue growth was boosted by the integration of HPS, which has expanded the firm's private markets platform and added performance fees.

For an asset manager, part of every beat comes from market levels that analysts cannot forecast precisely, so the more durable signal is organic growth and fee mix. Private markets and technology services carry higher fees than index products, and their growing share is what supports margin expansion over time. FMP's Income Statement Growth API tracks whether revenue and operating income are compounding faster than assets, and the Price Target Summary API shows whether analysts have raised targets in step with the beats or kept them anchored ahead of Wednesday's report.

The Goldman Sachs Group, Inc. (GS)

Beat Streak: 12 quarters.
Next quarterly report: Oct. 13 — EPS: $12.86; Revenue: $16.83B (consensus).

Goldman Sachs has beaten for twelve straight quarters, but the size of those beats varies more than anywhere else in the screen. The second quarter of 2026 delivered EPS far above consensus on record equities trading revenue, a sharp rise in investment banking fees and an annualized return on equity above 23%. Earlier beats in the streak were far narrower, which reflects how difficult trading and deal revenue are to model in advance.

That variability shapes how the streak should be read. The October consensus sits well below the second-quarter result, which means analysts are assuming some normalization in trading activity. A beat on Tuesday would extend the run, but whether it comes from underwriting and advisory momentum or another surge in trading volatility matters more for durability than the beat itself. FMP's Earnings Report API makes the magnitude of each surprise easy to chart across the streak, and the Financial Ratios API tracks return on equity, which is the clearest measure of whether the earnings base is structurally higher.

PepsiCo, Inc. (PEP)

Beat Streak: 6 quarters.
Next quarterly report: Feb. 2, 2027 — EPS: $2.24; Revenue: $30.41B (consensus).

PepsiCo extended its streak on Thursday, reporting third-quarter core EPS of $2.34 against a $2.29 estimate on organic revenue growth of about 3%. The beat came with a mixed message: management raised its organic revenue outlook to roughly 3% but cut its full-year core EPS growth guidance, citing input cost inflation and higher investment, and North American beverage volumes declined again. Several beats within the streak were narrow, some by only a cent or two.

That combination makes PepsiCo the clearest example in this screen of a quarterly beat coexisting with a weaker annual trajectory. PepsiCo drew two downgrades the previous week, and the guidance cut gives analysts a lower anchor for 2026 estimates even as the streak lengthens. The test for the next report is whether productivity savings and planned structural cost reductions can protect margins. FMP's Cash Flow Statement API shows whether free cash flow still supports the company's planned cash returns, and the Historical Stock Grades API tracks whether analyst sentiment stabilizes after the recent downgrades.

What Sits Behind These Repeated Beats

These five streaks share a label but measure different things. ADP's tightly clustered beats reflect a highly visible revenue base and guidance set conservatively enough to clear. Flex's wider beats point to demand running ahead of models. BlackRock and Goldman Sachs beat partly because markets and trading activity moved faster than analysts assumed, which is genuine upside but less repeatable by design. PepsiCo shows that a streak can lengthen while the annual earnings outlook weakens.

That is why streak length alone is a blunt instrument. Pulling the full surprise history from FMP's Earnings Surprises Bulk API and measuring the percentage size of each beat separates three patterns: consistent small beats that signal forecasting discipline, large and variable beats that track external conditions, and narrow beats that may simply reflect estimates being reset lower. Each carries different information about how much of the streak sits within management's control.

The second layer is the estimates themselves. Comparing surprise history with FMP's Financial Estimates API reveals whether consensus is rising after each beat or being trimmed before it, which is the difference between a company outrunning expectations and expectations retreating toward the company. Adding cash flow data from the Cash Flow Statement API and margin trends from the Income Statement API, all within the broader FMP platform, tests whether the reported beats are converting into cash or relying on cost timing and below-the-line items.

The practical takeaway is that the next three weeks will test these streaks against the same question from different angles. For ADP and BlackRock, the question is whether consistency holds through a softer hiring market and volatile asset prices. For Goldman Sachs, it is whether the earnings base has reset higher. For Flex, it is whether data center demand continues to outpace forecasts. For PepsiCo, it is whether a streak built on narrow beats can coexist with a lower annual target.

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 Consistency Test Forward

A streak is a record of the past; the next print is the only new evidence. Rerunning FMP's Earnings Surprises Bulk API once these reports land will show which of the five runs reflect durable execution and which were carried by conditions that may not repeat.

Want more? Explore our earlier article: Signals Desk Weekly Take via FMP API | Five Biggest Stock Movers (Sept 28-Oct 2)

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.