This week's screen surfaced an unusual clustering of price-to-target dislocations across several unrelated sectors — a pattern that tends to appear when market pricing starts moving faster than published analyst consensus. Using the FMP's Price Target Summary Bulk API, we ran a broad scan to isolate names where current prices have materially diverged from aggregated sell-side expectations.

What stood out wasn't any single company, but the consistency of the signal across real estate, consumer staples, energy, and healthcare. These aren't speculative outliers; they're widely covered names where price action is beginning to challenge consensus assumptions. The analysis below walks through how those gaps surfaced, what the data is actually capturing, and why this type of divergence tends to appear before narratives adjust.

This Week's Screen: Where Price Is Getting Ahead of Consensus

CVS Health Corp (NYSE: CVS)

Current Price: $79.44 • Consensus Target: $174.57 • Upside Potential: ~119.7%

CVS shows one of the widest gaps in the current screen, reflecting an ongoing reassessment of its multi-segment model spanning retail pharmacy, insurance, and healthcare services. Market pricing appears to be discounting execution risk and margin compression, while consensus targets still embed longer-term integration and scale assumptions. The divergence highlights a broader question around how quickly complex healthcare platforms can translate strategic positioning into financial clarity. Movements in medical cost ratios, segment-level earnings, and updated guidance will be key reference points in determining whether the gap narrows through price adjustment or estimate revision.

Iron Mountain Incorporated (NYSE: IRM)

Current Price: $81.51 • Consensus Target: $120 • Upside Potential: ~47.2%

Iron Mountain stands out for the scale of the disconnect between market price and consensus targets, particularly given its hybrid profile across data infrastructure and legacy storage. The divergence suggests that the market may be placing greater weight on near-term financing conditions and capital structure sensitivity, while analyst models continue to emphasize long-duration cash flow visibility from digital and data center assets. This type of spread often emerges when investors are recalibrating risk assumptions faster than earnings models are updated. Monitoring changes in debt metrics, refinancing activity, and segment-level revenue mix — especially from data center operations — will be central to understanding whether consensus expectations realign with market pricing or vice versa.

Sunoco LP (NYSE: SUN)

Current Price: $52.19 • Consensus Target: $67 • Upside Potential: ~28.4%

Sunoco's spread reflects a broader pattern seen in energy logistics and distribution: stable cash flows paired with valuation discounts driven by capital structure and sector perception. The partnership's price action suggests the market is applying a higher discount rate despite relatively predictable operating cash flows. Analyst targets, by contrast, continue to lean on distribution coverage and asset durability. This divergence often narrows or widens depending on how investors weigh balance sheet resilience versus income reliability — a dynamic that becomes clearer as updated cash flow and leverage data are released.

Clorox Co (NYSE: CLX)

Current Price: $98.11 • Consensus Target: $125 • Upside Potential: ~27.4%

Clorox's appearance on the screen reflects a familiar tension between defensive brand strength and near-term margin uncertainty. While consensus targets still imply a materially higher valuation, recent price behavior suggests investors remain focused on cost pressures, recovery pacing, and volume elasticity following prior operational disruptions. The divergence highlights how quickly sentiment can reset even in traditionally stable consumer staples names. Here, the gap is less about growth expectations and more about confidence in normalization timelines — something that tends to evolve as cost and margin data filter through quarterly reporting.

Cousins Properties Incorporated (NYSE: CUZ)

Current Price: $25.70 • Consensus Target: $29.25 • Upside Potential: ~13.8%

Cousins appears on the screen as a case where public market pricing has moved more quickly than sell-side revisions in the office REIT space. The gap is notable given how closely covered the name is and how conservative analyst models have remained around office fundamentals. Recent price action suggests that investors are increasingly differentiating among office operators rather than treating the group as a single macro trade. The signal here is less about a broad sector recovery and more about relative confidence in asset quality, leasing exposure, and balance-sheet durability — variables that often adjust in price before they are reflected in forward estimates. Watching how consensus targets evolve alongside updated leasing and occupancy disclosures will be key to assessing whether the current gap persists or compresses.

Reading the Signal Beneath Market Dislocations

Across the names in this screen, a consistent pattern emerges: market prices are adjusting faster than consensus frameworks can absorb. Rather than reflecting a single sector-level mispricing, the divergence points to shifting assumptions around balance-sheet resilience, cost structures, and execution risk that are being priced in ahead of formal estimate revisions.

The signal becomes clearer when price targets are viewed alongside underlying fundamentals. Comparing consensus targets with cash flow and income statement data from the FMP platform helps distinguish between temporary dislocations and more structural pressure points. In several cases, the gap appears less about near-term revenue softness and more about how markets are reassessing capital intensity, leverage, or margin durability — factors that tend to surface in price before they appear in estimates.

Framed this way, the screen functions less as a valuation shortcut and more as a timing lens. By pairing target dispersion with broader financial context — such as the analytical framework outlined in FMP's article on single-stock estimate and price-target mapping — the signal becomes an early indicator of where consensus may be lagging evolving fundamentals, rather than a simple measure of upside or downside.

Building a Repeatable Target-Gap Screen with FMP

At its foundation, a target-gap screen is about sequencing inputs correctly so the output can be trusted and reproduced. Once the workflow is set up, the process scales cleanly across large universes without requiring ticker-by-ticker intervention. The key is treating the screen as a system — not a one-off calculation — where each data pull has a defined role in the final signal.

Before running anything, confirm that your API key is enabled.

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

With both the current price and the consensus target available, calculate the percentage difference:

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

Working in percentage terms standardizes the results so that high-priced and low-priced names can be compared on the same footing.

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 Analyst Tool to Shared Research Infrastructure

What begins as an individual analyst's screen often becomes more valuable once it moves beyond the individual. The real inflection point comes when a workflow like this transitions from a personal research aid into shared analytical infrastructure. At that stage, the question is no longer whether the signal is useful, but whether it can be applied consistently across teams without being reinterpreted or rebuilt each time.

In practice, this shift is less about adding complexity and more about standardization. When the same inputs, definitions, and refresh logic are used across desks, the output becomes comparable by design. Analysts stop debating whose numbers are “right” and start focusing on what the signal is actually indicating. Shared dashboards, consistent thresholds, and agreed-upon data sources turn what might otherwise be an isolated screen into a common reference point across research, strategy, and risk functions.

This is where governance quietly becomes an advantage. Centralizing data access, versioning, and calculation logic reduces the friction that comes from ad hoc spreadsheets or duplicated scripts. It also makes the process auditable — an increasingly important requirement as investment decisions rely more heavily on systematic inputs. When a screen can be rerun, reviewed, and traced back to the same underlying assumptions, it earns credibility beyond the individual who built it.

For teams operating at scale, this kind of standardization is often supported through shared infrastructure such as FMP's Enterprise plan, which allows multiple users to work from the same data foundation without fragmenting workflows. The result is not just efficiency, but alignment: a common analytical language that allows insights to travel across desks without being reinterpreted at every handoff. At that point, the screen stops being a personal tool and starts functioning as part of the firm's research architecture.

When the Market Reprices Before the Story Catches Up

When price moves ahead of published expectations, it's often a signal that the market is processing information faster than formal models can absorb. Patterns like these tend to surface first in aggregated data — where shifts in consensus, coverage, and dispersion become visible before the narrative adjusts. Tools such as the Price Target Summary Bulk API make it possible to observe those transitions in real time, offering a clearer view of how expectations evolve before they're fully reflected in research language or forecasts.

If you enjoyed this analysis, you'll also want to read: Weekly Signals Desk | 5 Large Valuation Disconnects via the FMP API (Dec 15-19)

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.