This week's screen surfaced five large-cap and mid-cap names where price action has quietly moved ahead of published analyst targets. The pattern isn't about valuation calls; it's about timing, and where consensus appears slow to react to shifts already reflected in tape behavior and sector flows.

The names were identified using the FMP Price Target Summary Bulk API, which allows price targets and coverage depth to be pulled across a basket of stocks in a single pass. In this piece, we walk through how that API was used, what the resulting price-target gaps are signaling, and how the screen can be rebuilt as a repeatable diagnostic rather than a one-off list.

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

Legend Biotech Corporation (LEGN)

Current Price: $17.10 • Consensus Target: $55.5 • Upside Potential: ~224%

Legend Biotech's share price has diverged sharply from current analyst targets, with the FMP Price Target Summary Bulk API capturing an outsized implied gap relative to where the stock has traded recently. Based on a consensus target near $55.50, the implied price-target gap sits north of 220%, a spread that reflects lingering sell-side skepticism alongside a material compression in market valuation over the past year. This dispersion in expectations is not simply academic — it often signals a market that has priced in near-term uncertainties (commercial uptake, competitive pressure), while analysts continue to anchor models around longer-term franchise value.

From a signal perspective, the magnitude of the gap suggests that either market participants are discounting forward growth consensus too aggressively or that analysts have yet to fully adjust models to reflect updated clinical and commercial developments. For example, Legend Biotech's forthcoming presentations on CAR-T therapy data at industry conferences indicate fresh data flows that can materially affect addressable market assumptions and reimbursement dynamics. Integrating datasets such as upcoming sales cadence from CARVYKTI® adoption curves (quarterly revenue trend data) or shifts in clinical trial enrollments into a quantitative screen would help clarify whether this price-target divergence drifts toward fair value over time.

In the context of cross-sectional screens, the LEGN case underscores the utility of overlaying price momentum with analyst coverage breadth metrics; stocks with thin or stale coverage often exhibit larger consensus gaps simply because fewer model updates are being published. Tracking evolving analyst revisions in tandem with changes in institutional ownership data and short interest from regulatory filings could provide early warning on shifts in narrative that are not yet captured in consensus targets.

DraftKings Inc. (DKNG)

Current Price: $26.62 • Consensus Target: $45.67 • Upside Potential: ~72%

The DraftKings screen signal emerges from the juxtaposition of market momentum and a relatively elevated consensus target that has not fully realigned with recent price action. At ~$26.6, DKNG is trading more than 50% below its 52-week highs, even as the average analyst target cluster in the mid-$40s suggests a sizeable gap in valuation expectations. Analysts, in aggregate, maintain a Buy consensus, with a broad range of targets stretching into the $60s on the high end — a distribution that both compresses and elongates the implied upside depending on which segment of the target distribution is referenced.

Why this matters: divergent price behavior relative to modeled outcomes can signal an interim repricing of risk premia, particularly in discretionary digital entertainment where cash-flow visibility and customer acquisition economics remain volatile. DKNG's recent price erosion has coincided with sharper headlines around quarterly misses and revised EBITDA trajectories, which have pressured momentum metrics even as sell-side valuations have been slow to adjust. In screens that integrate both momentum indicators (e.g., RSI and relative performance) and fundamental revisions (e.g., earnings or revenue estimate drift), such asymmetries may flag the stock as information-imbalanced rather than simply undervalued or overvalued.

For analytical context, layering in datasets such as earnings revision trends or segment revenue breakdowns from quarterly filings would help clarify whether the current consensus target reflects a realistic earnings trajectory or is anchored in outdated assumptions about growth and regulatory headwinds — especially given the regulatory noise in U.S. gaming jurisdictions and growing competition in prediction markets.

JD.com, Inc. (JD)

Current Price: $28.07 • Consensus Target: $37 • Upside Potential: ~32%

JD.com's screening signal stems from its position as a major e-commerce operator in China where macro sentiment and sector rotation have shown marked sensitivity to growth versus value biases. The roughly 30% implied gap between price and consensus targets captured via the API suggests that sell-side expectations remain moderately above where the stock is currently trading. In this context, price behavior appears to be discounting a combination of slower top-line growth and broader investor caution tied to China's consumption data and regulatory backdrop.

The divergence between price and target here highlights two layers of market signals: one, that consensus models may not be fully pricing recent softness in consumer demand metrics from China's official releases; and two, that relative underperformance versus regional peers has likely tempered risk appetite. Incorporating data endpoints such as revenue growth revisions, same-store sales trends, and supply-chain operating margins from JD.com's quarterly statements would help quantify how near-term fundamental drivers are shaping these gaps relative to sell-side forecasts.

Analytically, cross-referencing macro consumption indicators with JD's internal KPIs (e.g., active customer growth or fulfillment cost trends) could help elucidate whether the consensus is systematically overlooking cyclical headwinds or anchoring too heavily on structural e-commerce growth narratives.

Silicon Motion Technology (SIMO)

Current Price: $128.06 • Consensus Target: $142.5 • Upside Potential: ~11%

Silicon Motion's implied gap is narrower than in other cases, but it is notable in a semiconductor context where price action often leads consensus during inflection periods in memory pricing or flash demand cycles. At approximately 11% implied upside, the differential between current price and the $142.5 mean target suggests that the market has already priced in a significant portion of sell-side expectations. Semiconductor demand data, especially NAND controller shipments and backlog figures from quarterly reports, would help clarify whether current price levels are consistent with seasonally adjusted capacity utilization and end-market demand.

This signal is significant because in technology screens, an absence of large implied upside can still be informative: stocks that trade close to consensus but without corresponding revisions in estimates may reflect near-term supply chain normalization or shifts in channel inventory. Tracking datasets such as book-to-bill ratios and inventory days on hand from industry surveys or SEC filings adds granularity, helping distinguish between genuine fundamental improvement versus mechanical valuation multiple compression.

International Flavors & Fragrances Inc. (IFF)

Current Price: $74.82 • Consensus Target: $78.6 • Upside Potential: ~5%

IFF's tighter implied gap — roughly 5% — sits within what might be expected for a mature specialty chemicals and flavoring franchise with stable cash flows and predictable margins. In consensus screens, such tight spreads can sometimes signal that investors and analysts are largely aligned, or that forward expectations have limited variance because of relatively stable growth projections. However, even a modest gap can be informative in the context of inflationary pressure on input costs and shifting consumer demand patterns.

For this global ingredi­ent supplier, incorporating input cost trend data, FX exposure analyses, and margin revision history would help illuminate whether the current consensus target is still anchored in historical segment assumptions or has absorbed recent shifts in commodity price cycles. The relatively small implied upside suggests limited disagreement between price and sell-side expectations at this stage, but the signal gains nuance when cross-referenced with inventory turnover and regional revenue mix trends from quarterly filings.

Reading the Disconnect: What Price-Consensus Divergence Reveals

Taken together, the five names in this week's screen don't point to a single valuation narrative. They point to asymmetry—between how quickly prices have adjusted and how slowly consensus frameworks tend to follow. In each case, the gap reflects a different form of tension: long-duration biotech assumptions colliding with compressed risk appetite, consumer and gaming models lagging shifts in unit economics, China exposure being discounted faster than estimates reset, or mature franchises where price and consensus are already converging. The unifying theme isn't direction; it's timing.

What gives these gaps analytical value is not their absolute size, but the conditions surrounding them. A wide divergence like LEGN's carries a very different signal weight than a narrow spread like IFF's, depending on coverage depth, estimate dispersion, and the recency of revisions. This is the point where price targets alone stop being sufficient. When targets are evaluated alongside operating data—such as margins and revenue trajectories pulled from FMP's Income Statement endpoints—it becomes clearer whether consensus is anchored to outdated profitability assumptions or whether the market is discounting near-term execution risk more aggressively. Frameworks that visualize dispersion and revision density, such as single-stock estimate and price-target heatmaps outlined in this guide help surface those pressures before they show up in headline target changes.

Across this basket, another pattern stands out: gaps tend to widen when model inputs change faster than published targets. Cash flow sensitivity, customer acquisition costs, inventory normalization, or FX exposure often appear first in quarterly financials or guidance commentary, well ahead of formal estimate resets. Combining the Price Target Summary Bulk API with earnings surprise data, historical revision trends, and insider transaction activity allows the gap to function less as a valuation call and more as a diagnostic—highlighting where the market is already incorporating information that consensus has not yet standardized.

Seen this way, the price-consensus gap behaves less like a measure of upside and more like a map of friction points inside the research ecosystem. It shows where narratives are in transition, where assumptions are being stress-tested in real time, and where future revisions are more likely to originate. Platforms like FMP make this kind of cross-dataset interrogation practical, allowing analysts to focus not on prediction, but on identifying where deeper scrutiny is most likely to matter next.

Building a Repeatable Target-Gap Screen with FMP

A price-target gap only becomes meaningful once it can be generated the same way every time. That consistency comes from treating each data input as a discrete component—pulled intentionally, sequenced correctly, and used for a specific analytical role. When the workflow is structured this way, the screen stops being a one-off observation and turns into a compact system that can be refreshed on schedule without revisiting assumptions. The only prerequisite before starting is a valid, active FMP 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 Analyst Tool to Shared Research Infrastructure

Signals like these tend to break down when they remain tied to individual workflows. What works well at a single desk often starts to fray once multiple teams reference similar ideas built on slightly different data pulls, refresh times, or assumptions. Over time, those inconsistencies dilute the signal itself—attention shifts from interpretation to reconciliation, and the analytical edge gets lost in process noise.

This is typically where analysts play an internal leadership role. Having built and stress-tested the workflow closest to the data, they're often the first to recognize when a useful screen should move beyond personal use. The goal isn't to add complexity; it's to remove ambiguity. Standardized inputs, shared definitions, and repeatable calculations allow the signal to function as a common reference point rather than an individual artifact. Screens migrate from local files into shared dashboards. Updates become scheduled rather than discretionary. Conversations move away from whose numbers are right and toward what the data is actually saying.

At scale, governance stops being a constraint and starts becoming a feature. Centralized data access, version-controlled logic, and clearly documented assumptions make outputs easier to audit and easier to trust. Teams can rerun the same screen, trace how each metric was constructed, and explain results without reverse-engineering inherited spreadsheets or undocumented scripts. That level of transparency matters as signals feed into portfolio discussions, strategy reviews, or risk forums where comparability and accountability are expected.

For firms looking to formalize workflows that have already proven their value at the desk level, infrastructure like FMP's Enterprise plan becomes a practical bridge—not a new analytical layer, but a way to preserve what works while making it durable across teams. At that point, the screen stops being an edge held by one analyst and starts operating as part of the firm's shared research fabric—reducing fragmentation, improving alignment, and raising the overall quality of decision-making.

When the Market Reprices Before the Story Catches Up

When prices move ahead of published narratives, the divergence itself becomes a data point. Screens built on tools like the FMP Price Target Summary Bulk API help surface those moments early, before revisions, commentary, or consensus language fully adjust. The work that follows isn't about forecasting—it's about reading what the market has already started to process, and deciding where deeper analysis is warranted next.

If you enjoyed this analysis, you'll also want to read: Signals Desk Weekly Take via FMP API | 5 Companies With Persistent Earnings Beats (Jan 26-30)

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.