This week's data scan flagged five large- and mid-cap names where market pricing is moving faster than published expectations. Using the Financial Modeling Prep Price Target Summary Bulk API, the screen focuses on a simple but often overlooked condition: situations where price action is already forcing a rethink of consensus, before target revisions show up in reports.

The purpose isn't to forecast outcomes or argue valuation. It's to surface mismatches between where capital is flowing now and where analyst targets are still anchored. In this note, the API is used as a systematic lens to identify those divergences, outline the mechanics behind the screen, and explain why these target gaps can act as early indicators of sentiment shift rather than confirmation after the fact.

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

MercadoLibre, Inc. (MELI)

Current Price: $2,178.41 • Consensus Target: $2830 • Upside Potential: ~29.9%

MercadoLibre posts one of the widest gaps in this week's screen, reflecting how quickly price has responded relative to where consensus estimates still sit. In complex platform businesses, targets often move in steps rather than continuously, especially when multiple business lines—commerce, payments, logistics—are contributing unevenly to results. The current spread suggests the market is processing operating leverage and scale effects faster than analysts are updating long-term models.

What's notable is not just the size of the gap, but its persistence across recent sessions. This type of divergence tends to attract attention because it forces a reassessment of embedded growth and margin assumptions. To ground the signal, combining analyst targets with segment-level revenue data, operating margin trends, and regional growth disclosures provides a more disciplined way to evaluate whether price is reflecting durable fundamentals or compressing future upside into current levels.

Tyler Technologies, Inc. (TYL)

Current Price: $448.37 • Consensus Target: $575 • Upside Potential: ~28.2%

Tyler Technologies appears on the screen as a reminder that consensus can lag even in relatively predictable business models. The company's exposure to government software contracts and recurring revenue streams tends to anchor analyst assumptions, which can result in slower target adjustments when execution trends improve incrementally rather than through headline surprises.

The widening gap suggests price is incorporating expectations around backlog conversion, margin normalization, or longer-term contract visibility more quickly than targets reflect. This isn't a call on outcome, but a signal to monitor how reported bookings, deferred revenue, and cash flow metrics evolve relative to existing estimates. Analyst target data combined with backlog disclosures and cash flow statements would help clarify whether the current divergence is supported by observable operating progress or primarily sentiment-driven.

Brown & Brown, Inc. (BRO)

Current Price: $80.64 • Consensus Target: $99 • Upside Potential: ~22.8%

Brown & Brown shows up on this week's screen not because of a sudden valuation argument, but because of how steadily price has advanced relative to where consensus targets remain anchored. The stock's recent behavior reflects persistent accumulation rather than event-driven volatility, which is notable for a name typically associated with incremental, fundamentals-driven moves. When price drifts higher in this fashion without corresponding target revisions, it often points to expectations being adjusted informally by the market before they're formalized in analyst models.

What makes the signal worth tracking here is the interaction between stability and drift. Insurance brokerage tends to reprice around margin durability, acquisition cadence, and organic growth visibility. A widening gap between price and targets suggests that investors may be reweighting those variables ahead of published updates. To contextualize the move, pairing analyst target data with income statement trends and historical margin expansion provides a clearer read on whether the current gap aligns with operating momentum or reflects sentiment running ahead of confirmed fundamentals.

IDEAYA Biosciences, Inc. (IDYA)

Current Price: $37.38 • Consensus Target: $42.57 • Upside Potential: ~13.9%

IDEAYA's appearance on the screen is driven less by magnitude and more by timing. The gap between price and consensus is narrower than some peers, but it has emerged alongside heightened sensitivity to clinical and pipeline-related signals. In development-stage biotech, targets often lag trading behavior when the market begins assigning incremental probability to specific programs or readouts, even in the absence of headline news.

Here, the signal reflects a subtle shift rather than a decisive break. Price has adjusted upward while targets remain clustered, indicating that analysts have yet to materially recalibrate assumptions. Monitoring changes in analyst coverage counts alongside pipeline disclosures and trial updates can help determine whether this divergence resolves through revised estimates or through price consolidation. Endpoint-wise, analyst target data paired with R&D expense trends and trial milestone disclosures offers the most informative lens for interpreting the move.

Evercore Inc. (EVR)

Current Price: $369.01 • Consensus Target: $388.4 • Upside Potential: ~5.3%

Evercore stands out precisely because the gap is modest. In advisory-driven financials, price and targets typically move in closer lockstep, reflecting the sector's sensitivity to deal flow visibility and compensation dynamics. A smaller but persistent gap can still be meaningful when it appears after a period of consolidation or following shifts in capital markets activity.

The signal here is less about dislocation and more about confirmation lag. Price action suggests improving confidence in advisory conditions or capital markets normalization, while targets imply analysts are waiting for clearer evidence in reported revenues or backlog commentary. Tracking quarterly revenue mix, compensation ratios, and transaction volume data alongside evolving target levels helps determine whether this narrow divergence expands, closes, or simply marks a pause in consensus adjustment.

Reading the Signal Beneath Market Dislocations

Viewed together, the five names in this week's screen don't point to a single sector call or macro thesis. What they illustrate instead is a broader pattern in how information is being absorbed: price is reacting incrementally, name by name, while consensus frameworks update more slowly and in batches. The gaps aren't uniform in size or cause — advisory, software, biotech, insurance, and e-commerce each reprice on different inputs — but the common thread is timing. In each case, the market appears to be incorporating operating or structural signals before those signals are fully reflected in published targets.

That distinction matters. Target gaps are often dismissed as static valuation artifacts, but in practice they behave more like timing indicators. When divergences surface across otherwise unrelated business models, the implication is less about outright mispricing and more about expectations being revised informally before they are codified in models. This is typically the phase when qualitative judgments — around margin durability, backlog visibility, capital intensity, or balance sheet flexibility — begin to influence positioning ahead of formal estimate changes.

The signal becomes more informative when price targets are treated as just one layer of a broader data stack. Comparing target gaps against operating cash flow trends from FMP's Financial Statement APIs helps distinguish gaps rooted in improving fundamentals from those driven primarily by sentiment or positioning. This sequencing mirrors how target revisions tend to unfold in practice, a process outlined in FMP's analysis of monitoring target price changes relative to shifting fair value assumptions.

In that sense, the value of the screen isn't in the individual outcomes, but in how it frames the workflow. Dislocations like these are best read as prompts for deeper interrogation, not conclusions. Data platforms such as FMP make this approach workable at scale, allowing analysts to cross-check assumptions, reconcile multiple inputs, and identify where consensus may already be moving — quietly — before those changes become explicit in published estimates or retrospective commentary.

Building a Repeatable Target-Gap Screen with FMP

A target-gap screen is useful if it's built to hold up under repetition. That means pulling each input deliberately, in the right sequence, and for a clearly defined role. When the components are structured this way, the result isn't a one-time snapshot — it's a small, durable system that can be rerun, expanded, and checked without reworking the logic each time. Before starting, the only prerequisite is confirming that your API key is active.

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 Analyst Tool to Shared Research Infrastructure

Signals like these tend to spread quickly once they demonstrate practical value. What limits their usefulness over time isn't the quality of the insight, but the ability to run the same logic consistently across teams. As workflows move beyond a single analyst or desk, the real challenge becomes standardization — ensuring that assumptions, inputs, and calculations don't quietly diverge as more hands touch the process. This is where analysts often shift from users of tools to advocates for institutional alignment.

At the firm level, durable scale is built through shared reference points. When research, strategy, and risk teams are working from the same datasets, definitions, and refresh schedules, the output becomes comparable by default. The focus moves away from reconciling versions of the same screen and toward interpreting what the signal implies. Common dashboards, consistent thresholds, and agreed-upon data sources reduce friction at handoffs and make cross-team dialogue more productive.

That consistency only holds if it's supported by governance. Centralized access to data, version-controlled logic, and repeatable calculations help prevent the gradual fragmentation that sets in when workflows are passed along informally. Just as importantly, they introduce auditability. Being able to rerun a screen, inspect its construction, and explain its assumptions is increasingly expected as quantitative signals feed into broader decision-making processes.

For teams looking to formalize workflows that have already proven themselves at the desk level, infrastructure such as FMP's Enterprise plan often serves as the practical bridge. Not as a new methodology, but as a way to preserve what already works while making it accessible, consistent, and durable across the organization. At that point, the screen stops being an individual artifact and becomes part of the firm's shared research foundation.

When the Market Reprices Before the Story Catches Up

When price adjusts ahead of published targets, it's usually a signal that the market is incorporating information faster than formal narratives can keep pace. Screens built on aggregated consensus data — such as those using the FMP Price Target Summary Bulk API — make that transition visible while it's still forming. The value lies in recognizing where expectations are actively shifting, not after the revisions have already been written.

If you enjoyed this analysis, you'll also want to read: Weekly Signals Desk | Five Notable Valuation Disconnects from the FMP API (Dec 29-Jan 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.