Weekly Signals Desk | Price-Target Gaps Identified via the FMP API (Aug 31-Sept 4)

The widest price-target gaps in this week's universe were not the most interesting ones. Ten names screened above 60% before coverage quality was applied, and most of them collapsed on inspection: two estimates ever, or none refreshed inside a year. What survived was a tighter band, Dycom, Generac, MKS, DigitalOcean and Capri, at 52% to 69%, each carried by coverage deep enough for the number to mean something.

The screen runs on FMP's Price Target Summary Bulk API, which returns average targets alongside analyst participation counts. This article uses both halves of that response, because the second one is what separates a gap that consensus is actively debating from a gap that is simply old.

Key Takeaways

• Coverage depth, not gap size, did the work this week. Ten wider spreads were removed because the target set behind them was thin, stale, or both.
• Comparing the last-month average target against the full consensus splits the five into two groups: names where coverage has converged and stopped moving, and names where recent revisions are still tracking downward while the headline level stays high.
• MKS carries the sharpest version of that split, with the most recent targets well below consensus even as reported revenue and margins accelerated. The debate there is the balance sheet, not the operating business.
• Two of the five are being valued on revenue that has not been recognised yet, one in a data-centre backlog and one in remaining performance obligations. Those gaps close on conversion rather than on demand.

This Week's Five Consensus Disconnects

Dycom Industries, Inc. (DY)

Current Price: $300.23 • Consensus Target: $507.00 • Upside Potential: 68.9%

Dycom has the widest gap in the group and the freshest coverage supporting it: six targets published inside the last month, twenty-seven across the year. The fiscal second quarter, reported in late August, was strong on almost every line. Revenue rose about 46% year over year to roughly $2.01 billion, adjusted earnings per share came in above expectation, adjusted EBITDA margin held near 15.7%, operating margin expanded by more than five points, and backlog grew 53% to $12.24 billion. Fiber-to-the-home work grew close to 60% in the first half, and the Building Systems segment lifted its margin to 24.5% following an acquisition integration.

The share price fell anyway, from roughly $352 before the print to $300.23 at the end of the covered week, some 47% below the 52-week high. Two items did the damage: about $150 million of wireless equipment revenue pushed into 2027 on deployment scheduling, and third-quarter guidance below consensus as workforce and scaling investments compress near-term margin. Management nonetheless raised the full-year revenue midpoint.

What makes this a live disagreement rather than a stale one is visible in the target data itself. The six most recent targets average roughly $471, below the $507 consensus, so coverage has been revising downward, and yet even the revised level sits well above the market. FMP's Price Target Summary API is the direct instrument for that comparison, because the last-month average and the full consensus are separate fields in the same response. Backlog conversion, the timing of the deferred wireless work, and whether the labour investment settles into durable margin or permanent cost are what will move both numbers toward each other.

Generac Holdings Inc. (GNRC)

Current Price: $187.35 • Consensus Target: $298.46 • Upside Potential: 59.3%

Generac's spread is a timing disagreement rather than a demand one. The shares have fallen roughly 30% from a late-June peak near $296, and the consensus target sits almost exactly where the last quarter's eleven published targets averaged, which means coverage has converged and is not drifting. Thirty-eight targets have been published across the year, so the level is well populated.

The operating story is unusually easy to state. Commercial and industrial sales grew 29% in the second quarter to about $556 million, with full-year growth in that segment guided to the low thirties. The data-centre backlog reached roughly $1.6 billion by late July, around $1 billion of that added since the prior update, including a first hyperscaler commitment of nearly $700 million of volume for 2027. Residential standby, historically the core, slipped about 2%. To serve the backlog the company is committing roughly $250 million of expansion capital and adding about a thousand workers.

That sequencing is the whole gap. The spending lands in 2026; the revenue lands in 2027 and beyond. Analysts are discounting the later cash flows, the market is paying for the current ones, and neither position is unreasonable. The data suggests this is an area to monitor through the FMP Cash Flow Statement API rather than through order announcements, since capital expenditure running ahead of operating cash flow is precisely what a backlog-to-earnings conversion looks like before it converts. A second hyperscaler agreement moving from negotiation to contract would be the first hard evidence either way.

MKS Inc. (MKSI)

Current Price: $260.31 • Consensus Target: $408.14 • Upside Potential: 56.8%

MKS produces the most instructive target pattern in the screen. Seven targets were published in the last quarter averaging about $428, but the two published in the last month average roughly $345, well below the $408 consensus. Coverage is not stale here, it is actively moving lower, and it is moving lower against reported results that accelerated.

The second quarter delivered $1,248 million of revenue, ahead of the company's own guidance, with double-digit year-over-year growth across all three end markets: semiconductor up 28%, electronics and packaging up 43%, and specialty industrial up 14%. Gross margin improved to 47.6%, non-GAAP operating margin reached 25.6%, and non-GAAP diluted earnings per share nearly doubled to $3.30. Third-quarter guidance points to further sequential growth.

So the disagreement is not about the business. It is about the capital structure underneath it. The company carries roughly $4.9 billion of total debt against $611 million of cash, and its convertible notes became convertible during the third quarter and were reclassified as short-term obligations. That reclassification does not change the economics, but it changes where the obligation sits and how the balance sheet reads. The FMP Balance Sheet Statement API is where that shows up cleanly, in the split between current and long-term liabilities alongside the cash position. A 52-week range running from $104 to $448 tells you how violently this name reprices when the financing question moves, in either direction.

DigitalOcean Holdings, Inc. (DOCN)

Current Price: $112.47 • Consensus Target: $172.73 • Upside Potential: 53.6%

DigitalOcean's gap rests on a number that is not yet revenue. Second-quarter revenue rose 29% to $281 million and annual recurring revenue reached $1,125 million, also up 29%, which is solid but not extraordinary. Remaining performance obligations, however, reached $894 million, more than eleven times the prior-year figure. Incremental ARR added in the quarter rose 191%, AI customer ARR grew 212% to $234 million, and customers spending more than $100,000 now account for 35% of revenue while growing 98%.

The gap between a contracted book that grew elevenfold and recognised revenue that grew 29% is the entire investment case, and it is also the entire risk. Management has described landing nine-figure annual commitments, which on a $1.1 billion ARR base materially changes the customer concentration profile of a company built on serving small developers and start-ups. Profitability has held up, with adjusted EBITDA near $114 million and operating cash flow of $110 million, and the balance sheet improved to $767 million of cash after repurchasing convertible notes.

Coverage sits three targets deep in the last month averaging about $159, modestly below the $172.73 consensus, with twenty-nine published across the year. That is a mild downward drift rather than the markdown seen at MKS. The most direct test is the FMP Financial Estimates API, since comparing forward revenue consensus against the contracted book shows how much of that RPO analysts actually expect to convert inside the estimate horizon, which is a more precise question than whether the backlog is real.

Capri Holdings Limited (CPRI)

Current Price: $13.57 • Consensus Target: $20.57 • Upside Potential: 51.6%

Capri has the most settled consensus in the group. The last-month average of about $20.67, the last-quarter average of $20.33 and the full consensus of $20.57 sit within thirty-five cents of one another, across twenty-seven targets in the year. Coverage has converged, which makes this gap a considered position rather than an estimate set in motion.

The fiscal first quarter, reported on 5 August for the period ended 27 June, showed the shape of a margin-led turnaround. Revenue fell 3.5% to $769 million, with Michael Kors down 7.1% to $590 million and Jimmy Choo up 10.5% to $179 million. Everything below the revenue line improved: gross margin widened 200 basis points to 65.0%, adjusted operating margin gained 110 basis points, adjusted diluted earnings per share rose 34% to $0.67, inventory fell 20%, and net debt dropped 85% year over year to $224 million. Management trimmed full-year revenue guidance to roughly $3.4 billion, citing about $135 million of combined headwinds at Michael Kors from inventory delays, softer European trends and currency, while still guiding to $2.15 of diluted earnings per share.

The structure of that guidance is the point: revenue down, earnings sharply up. The case therefore rests on gross margin, cost, and a shrinking share count rather than on demand recovery, and the company repurchased 2.6 million shares during the quarter at an average of $19.31, close to the consensus target and far above the market price. FMP's Key Metrics TTM API puts that on testable footing, since free cash flow yield and leverage measures show whether the deleveraging and buyback can continue at this pace while the top line is still contracting.

Where a Gap Is Live and Where It Is Just Old

The five spreads sit inside a seventeen-point band, from 51.6% to 68.9%, which makes them look interchangeable in a ranked list. Read the coverage data alongside them and they separate cleanly into two groups.

In the first, consensus has converged and stopped moving. Capri's last-month, last-quarter and full-consensus targets are effectively identical, and Generac's quarterly average sits within three dollars of its consensus. When coverage clusters that tightly, the gap represents a position the research community has settled on and is holding: analysts have looked at the same evidence the market has and reached a different conclusion. In the second group, targets are still in motion and the direction is down. Dycom's recent targets average about 7% below its consensus, DigitalOcean's about 8% below, and MKS's roughly 15% below. There the headline spread is partly an artefact of a level that has not finished adjusting.

That distinction is not visible in the percentage, and it is why ten wider gaps were removed from this screen before it was written. A 90% spread carried by two estimates, neither refreshed in a year, is not a bigger version of the same signal. It is a stale number attached to a moving price, and treating the two as comparable is the most common way a target-gap screen produces noise.

Once the coverage filter has done its work, the remaining question is what the gap is made of. Pairing the target data with the Income Statement API and Cash Flow Statement API tests whether reported results support the consensus level, which is where Generac and DigitalOcean become interesting, since both are being valued on revenue that has been contracted but not recognised. The Enterprise Values API answers a different question: whether the spread reflects a compressed multiple against a stable earnings base or a genuine expectation of higher earnings, and those two have very different resolution paths. For a company like MKS, where operating momentum is not in dispute but leverage is, the Financial Scores API adds a solvency read before the gap is taken seriously at all.

Ratings history closes the loop. The Historical Ratings API and Historical Stock Grades API show whether analysts moved their recommendations alongside their targets or only trimmed the numbers, and a target cut without a rating change usually means a valuation adjustment rather than a changed thesis. Holding all of that in one frame is where the breadth of coverage available through FMP becomes practical rather than decorative: the target data, the participation counts, the reported statements and the ratings history carry the same identifiers, so a gap surfaced on Friday can be tested for freshness, support and conviction without leaving the dataset.

The screen's real output, then, is not a ranking of upside. It is a shortlist of disagreements worth understanding, sorted by how recently someone bothered to defend them.

Creating a Structured Target-Gap Workflow

A price-target spread only becomes useful when the calculation can be reproduced reliably. That means fixing the data inputs, pulling them in a consistent sequence, and applying the same formula every time the screen runs. Once those elements are standardized, the exercise stops being a one-off comparison and turns into a process that can be refreshed on a schedule.

The only requirement before running the workflow is a valid API key.

Step 1: Pull Analyst Price Targets

The process starts by establishing where consensus currently sits. This is done by querying the Price Target Summary Bulk API, which returns average price targets along with analyst participation counts across the ticker set in a single call. That combination matters: the average target provides the reference level, while coverage depth helps contextualize how representative that number is. Together, they form the baseline against which market prices will be compared.

Endpoint:

https://financialmodelingprep.com/stable/price-target-summary-bulk?apikey=YOUR_API_KEY

Sample Response:

[ { "symbol": "AAPL", "lastQuarterCount": "12", "lastQuarterAvgPriceTarget": "228.15", "lastYearAvgPriceTarget": "205.34" } ]

Step 2: Pull Latest Market Prices

Once targets are in place, the next input is the current trading price. This comes from the Company Profile Data API, which includes the most recent quote used for comparison. At this stage, the goal isn't granularity or intraday precision — it's simply to anchor each name to the same market reference point so gaps are calculated consistently.

https://financialmodelingprep.com/stable/profile/AAPL?apikey=YOUR_API_KEY

Step 3: Derive the Target Gap

Once both values are available, the gap itself is straightforward to compute. Express it as a percentage to normalize results across different price levels:

Upside % = (Price Target - Current Price) / Current Price × 100

Using percentages allows large-cap and lower-priced names to sit in the same ranking without distortion.

Step 4: Apply a Threshold Filter

The final layer is judgment. Most workflows introduce a minimum threshold — often around 20% — to filter out routine variance and focus attention on gaps that are large enough to matter. At this stage, analyst coverage becomes part of the interpretation: a wide gap backed by broad, recent coverage carries a different weight than one driven by a small or outdated estimate set.

Structured this way, the process moves beyond a simple valuation screen. It becomes a repeatable diagnostic tool — one that highlights where price and consensus are drifting apart and does so in a way that can be refreshed, audited, and scaled across time and coverage universes.

From Individual Screen to Institutional Research Process

Most quantitative workflows begin quietly — a model or screen built by a single analyst to answer a recurring question with greater consistency. The first version usually lives in a spreadsheet or a small script: efficient, practical, and tailored to the needs of one desk. The turning point arrives when the signal proves useful enough that colleagues begin asking for it. Replication follows, and with it comes an unintended side effect: slight variations in endpoints, refresh schedules, or calculation logic start producing subtly different results.

At that stage, the analyst who created the workflow often becomes an informal architect of standardization. The challenge shifts from running the screen to defining the method behind it. Institutional value emerges when the process is formalized: the data sources are fixed, the sequence of API pulls is documented, formulas are locked, and thresholds are explicitly defined. Once those elements are stabilized, the workflow stops being a personal tool and begins to function as a shared research input.

Moving the process into a centralized dashboard with scheduled updates is usually the next step in that evolution. Instead of circulating spreadsheets or ad-hoc scripts, teams interact with the same data pipeline and the same calculation framework. This reduces workflow fragmentation across research groups and allows portfolio managers, analysts, and risk teams to reference the same signal simultaneously. When everyone is drawing from the same dataset and methodology, discussions shift away from reconciling numbers and toward interpreting what the signal actually means.

Standardization also strengthens governance and transparency. A centralized workflow creates a visible audit trail: where the data originated, when it refreshed, and how each metric was derived. That lineage matters in institutional environments where reproducibility is essential. When colleagues run the same query and obtain the same result, the signal becomes dependable infrastructure rather than a one-off analytical shortcut.

Scaling that kind of workflow across a team requires stable access to the underlying datasets and consistent distribution across users. Infrastructure becomes less about adding features and more about removing friction from the research process. Platforms designed for institutional usage — such as FMP's Enterprise plan — provide the access controls, refresh stability, and shared environment needed when a desk-level workflow transitions into a broader research tool.

When that transition happens successfully, the model itself changes role. The target-gap screen is no longer simply a clever comparison between price and analyst targets. It becomes part of the firm's analytical framework — a standardized diagnostic that multiple teams can rely on to identify where market pricing and consensus expectations are beginning to drift apart.

What Closes These Gaps

Five spreads of similar size will not resolve on similar timelines: a deferred contract, a factory being built for 2027 volumes, a convertible note reclassification and a margin-led turnaround all move on their own clocks. Tracking them through FMP's Price Target Summary Bulk API week to week shows which direction each gap is closing from, since a spread that narrows because targets came down is a different outcome from one that narrows because the price went up.

If you enjoyed this analysis, you'll also want to read: Weekly Signals Desk | Five Notable Valuation Disconnects from the FMP API (Aug 24-28)

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.