Consumer-facing stocks have carried much of the market's caution through September, and this week's target-gap screen reflects it. Three of the five widest gaps backed by recent coverage belong to businesses that depend on discretionary spending, from casino floors in Macau to gym memberships and pet supply subscriptions. The other two, a power generator and a public-safety technology platform, have pulled back from highs while analysts have left their targets largely where they were. The pattern points less to a sector call than to a gap between how quickly prices have adjusted to softer sentiment and how slowly published targets have followed.
The screen is built on FMP's Price Target Summary Bulk API, which returns average analyst targets and coverage counts across a full ticker set in one pull. This article works through the five resulting gaps, then shows how the endpoint can anchor a repeatable workflow that weighs each spread by the depth and recency of the coverage behind it.
Key Takeaways
- Wynn Resorts, Chewy and Planet Fitness carry the widest gaps, and in each case recent analyst targets have stayed close to consensus while the share price has absorbed weaker consumer data.
- Chewy stands out on recency: all eight targets published in the past quarter arrived within the last month, after its second-quarter report, so the gap is hard to attribute to stale estimates.
- Vistra and Axon show a different mechanism, where a pullback from elevated levels has opened a spread without any meaningful change in the long-term demand story analysts are underwriting.
- Coverage quality filtered the list heavily: several larger headline gaps were set aside because they rested on one or two targets or on estimates that newer calls had already moved below.
Five Consensus Gaps Leading This Week's Screen
Wynn Resorts, Limited (WYNN)
Current Price: $81.12 • Consensus Target: $132.36 • Upside Potential: 63.2%
Wynn tops the screen after trading down to a 52-week low in September. The pressure has come from two directions. Macau gaming revenue has been softer than expected after the summer, with August showing only a modest recovery and early September weakening again. At the same time, tensions in the Middle East have raised questions about the timing and ramp of Wynn Al Marjan Island in Ras Al Khaimah, the resort in which Wynn holds a 40% stake and which is scheduled to open in 2027. Current pricing appears to assign little value to that project.
What makes the gap notable is its stability. Analysts have published a steady run of targets over the past quarter, and their average sits almost exactly on the consensus figure. That suggests coverage is looking through near-term Macau volatility to the earnings contribution from a new market. FMP's Revenue Geographic Segments API is well suited to tracking whether Macau's share of revenue shifts as Las Vegas and, eventually, the UAE contribute more, which is the structural change the consensus target depends on.
Chewy, Inc. (CHWY)
Current Price: $18.27 • Consensus Target: $29.64 • Upside Potential: 62.2%
Chewy's gap is the freshest in the group. All eight targets published over the past quarter were issued within the last month, following a second-quarter report in which net sales rose at the top end of guidance, Autoship grew faster than overall revenue and now accounts for the large majority of sales, and adjusted EBITDA margin came in ahead of the company's own range. Management raised its full-year sales and margin outlook. The stock declined anyway and has continued to slide, down about 11% this week alone.
Part of the reaction reflects the quality of the margin beat, since some of it came from timing items such as tariff refunds and other one-off benefits. Part reflects broader concern about discretionary pet spending. Analysts, however, reset their targets after the print and still landed well above the share price. FMP's Key Metrics TTM API provides a direct read on free cash flow yield and margin trends, which helps test whether the business is being valued on its recurring Autoship base or on fears about the discretionary portion of the basket.
Planet Fitness, Inc. (PLNT)
Current Price: $42.68 • Consensus Target: $69.18 • Upside Potential: 62.1%
Planet Fitness has been under pressure since May, when it lowered guidance and pulled back from planned membership price increases, a decision followed by a roughly 30% single-day decline in the shares. The shares have continued to weaken since then and fell nearly 14% this week, placing them among the heaviest decliners in the S&P 500 and MidCap 400 universe. The central question is whether membership growth can recover without the pricing lever management chose not to pull.
The consensus target tells a more measured story than the share price. Analysts who updated after the guidance cut moved their targets sharply lower from their year-ago average, but the latest quarter's targets still cluster near the current consensus. That implies coverage has already absorbed a lower earnings base and does not see the recent slide as reflecting new information. FMP's Historical Stock Grades API shows whether the rating mix has shifted alongside those target revisions, which is the clearest way to separate lower price targets from a change in underlying analyst stance.
Vistra Corp. (VST)
Current Price: $138.46 • Consensus Target: $217.45 • Upside Potential: 57.0%
Vistra's gap has opened as the stock retreated from earlier highs, even as the company continued to add contracted demand. This month it signed a 20-year agreement to supply power from a natural gas facility in Odessa, Texas to a planned data center campus, with delivery due to begin in 2027 and a small equity interest attached. It also raised $1.5 billion through junior subordinated notes to refinance preferred stock.
Analyst targets from the last quarter average slightly above the consensus, so coverage has not moved to meet the lower share price. The gap appears to reflect the market discounting the pace at which data-center contracts convert into earnings, along with sensitivity to power prices, rather than any analyst reassessment. The capital structure is part of the picture. FMP's Balance Sheet Statement API tracks debt and equity over time, which matters for a company funding growth and returning capital at the same time, and helps show whether leverage is moving in a direction that could influence how the market values each new contract.
Axon Enterprise, Inc. (AXON)
Current Price: $430.11 • Consensus Target: $652.14 • Upside Potential: 51.6%
Axon delivered second-quarter revenue growth above 30% and raised its full-year outlook, yet the stock has fallen well below its 2026 highs. Two issues have weighed on it. Gross margin slipped as a larger share of revenue came from services, which carry lower margins than the core software business. Separately, regulatory and privacy scrutiny of automated license plate readers across the industry raised questions about one product line, although some analysts have noted that it is not central to the company's longer-term targets.
The target data suggests coverage views the pullback as a valuation reset rather than a change in the growth path. The last quarter's average target sits above the consensus figure. The key measure to monitor is revenue mix. FMP's Revenue Product Segmentation API separates device, software and services revenue, which would show whether the margin dilution is a temporary effect of implementation work or a lasting shift in what Axon sells.
What the Pattern in This Week's Gaps Suggests
Across the five names, the common thread is timing. In every case, the most recent analyst targets sit close to or above the consensus, which means the spread is not being inflated by stale estimates. Instead, share prices have moved faster than coverage in response to consumer caution, regional risk or margin mix. That is a different situation from a gap created by outdated targets, and it deserves a different reading: the question is whether analysts are lagging or whether the market is overreacting, and the data available so far does not settle it.
The consumer concentration adds a second layer. Wynn, Chewy and Planet Fitness operate in unrelated categories, yet all three have been priced for a more hesitant spender. Their gaps are therefore partially correlated, and a change in consumer data could narrow or widen all three at once. Vistra and Axon, by contrast, are tied to infrastructure spending and public-sector budgets, so their spreads are more likely to move on company-specific news.
Coverage depth determined which gaps made it into this analysis, and that filter is where most of the analytical value sits. Within the FMP data environment, the Price Target Summary Bulk API supplies both the average target and the count of targets behind it across different lookback windows, which makes it possible to rank gaps by the freshness of the estimates as well as their size. The Price Target Consensus API adds the high and low targets, and a wide range signals that the average is masking real disagreement.
Estimates and ratings complete the picture. The Financial Estimates API shows whether forward revenue and EPS are rising or being cut for each name, while the Historical Stock Grades API captures whether ratings are changing alongside targets. Matching those against earnings dates from the Earnings Report API shows whether targets were reset after the latest results or are still waiting for them. Where all of those datasets agree, the price-target gap represents a considered view; where they diverge, the gap is better treated as a question the next data release is likely to answer.
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:
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https://financialmodelingprep.com/stable/price-target-summary-bulk?apikey=YOUR_API_KEY |
Sample Response:
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[ { "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.
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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.
When Prices Move Faster Than the Targets Behind Them
This week's gaps were opened by prices, not by estimates, and that distinction is the starting point for reading them. Refreshing FMP's Price Target Summary Bulk API over the coming weeks will show whether coverage begins to move toward the market or whether the market begins to move back toward coverage.
If you enjoyed this analysis, you'll also want to read: Weekly Signals Desk | Five Notable Valuation Disconnects from the FMP API (Sept 14-18)
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.
