Analyst price targets rarely move in lockstep with market prices. Periods of elevated volatility, sector rotation, and shifting sentiment often create temporary gaps between where stocks trade and where Wall Street consensus believes they should be. Those disconnects don't automatically signal opportunity, but they do provide a useful starting point for deeper research.
This week's screen identified five companies where that divergence stands out. Using the FMP's Price Target Summary Bulk API, we ranked stocks trading meaningfully below the latest analyst consensus and examined what those gaps may reveal about current market positioning. Later in this article, we'll also break down how the API works and show how to build the same repeatable target-gap workflow for your own research.
Key Takeaways
- Five companies with notable analyst-market disconnects: This week's screen highlights DraftKings, Disney, CoreWeave, Brown & Brown, and Duolingo as stocks where current prices remain meaningfully below analyst consensus targets.
- A price-target gap is a research signal, not a conclusion: The spread between market price and analyst expectations is most valuable when used to identify companies that warrant deeper fundamental analysis, rather than as a standalone valuation indicator.
- Combining datasets provides stronger context: Pairing analyst targets with financial statements, cash flow, profitability metrics, and estimate revisions creates a more complete framework for interpreting why these gaps exist.
This Week's Largest Price-Target Disconnects
DraftKings Inc. (DKNG)
Current Price: $25.89 • Consensus Target: $35.65 • Upside Potential: 37.7%
DraftKings posts one of the widest price-to-consensus gaps in this week's screen. A market price of $25.89 compared with an average analyst target of $35.65 produces a 37.7% spread, placing the company among the most significant disconnects identified by the Price Target Summary Bulk API. Rather than treating that gap as a valuation conclusion, it is more useful to view it as an indication that market pricing and analyst expectations are currently reflecting different assumptions about the business.
Recent attention has centered on the company's expanding prediction-markets business alongside its established sportsbook platform, with management highlighting increasing user activity during the early stages of the 2026 FIFA World Cup. Reuters has also noted expectations that the tournament could become one of the largest betting events on record, creating an unusually active operating backdrop for the industry. The next layer of analysis is less about headlines and more about operating execution. Reviewing quarterly income statements alongside analyst estimate revisions can help determine whether consensus targets are being supported by improving profitability, revenue trends, or changing earnings expectations rather than sentiment alone.
The Walt Disney Company (DIS)
Current Price: $99.50 • Consensus Target: $136.5 • Upside Potential: 37.2%
Disney appears near the top of this week's screen with a market price of $99.50 against a consensus target of $136.50, representing a 37.2% spread.
Unlike businesses driven by a single product cycle, Disney's investment narrative depends on several operating segments moving together, including streaming, parks, experiences, and film content. Because multiple business lines contribute to overall valuation, isolated news rarely explains the entire price-target gap.
For that reason, following segment-level revenue trends in the income statement, together with analyst estimate revisions and earnings guidance, provides more context than price action alone. Those datasets help distinguish whether consensus is anchored in measurable operating performance or in longer-term assumptions that have yet to be reflected in the market.
CoreWeave, Inc. (CRWV)
Current Price: $81.75 • Consensus Target: $131.5 • Upside Potential: 60.9%
CoreWeave records the largest spread in this week's group. With shares trading at $81.75 and the analyst consensus standing at $131.50, the calculated difference reaches 60.9%. That magnitude immediately stands out in a quantitative screen, although it should be interpreted as a signal for further investigation rather than an indication of intrinsic value.
The company remains closely tied to investor demand for AI infrastructure, an area where capital spending, customer concentration, and execution all receive considerable attention. Because businesses in rapidly expanding markets often experience frequent revisions to revenue expectations, analyst targets can change meaningfully over relatively short periods. Examining revenue growth, capital expenditure trends, major customer disclosures, and analyst estimate history provides a clearer framework for understanding why consensus remains materially above the current market price. Those datasets often reveal whether valuation differences are driven primarily by changing fundamentals or by shifts in market risk appetite.
Brown & Brown, Inc. (BRO)
Current Price: $70.00 • Consensus Target: $92.83 • Upside Potential: 32.6%
Brown & Brown trades at $70.00, while the current analyst consensus target sits at $92.83, producing a 32.6% gap. Compared with some of the higher-volatility names in this week's screen, the disconnect appears in a business that has historically been associated with steadier operating performance and recurring revenue characteristics.
Insurance brokerage companies are often evaluated through acquisition execution, organic revenue growth, and margin consistency rather than short-term market narratives. That makes this spread particularly interesting because consensus expectations typically evolve more gradually than they do for emerging-growth companies. Reviewing income statements, cash flow trends, acquisition activity, and analyst revisions can provide useful context for determining whether the observed gap reflects changing operating expectations or simply a difference between current market pricing and longer-term analyst assumptions.
Duolingo, Inc. (DUOL)
Current Price: $125.76 • Consensus Target: $136.17 • Upside Potential: 8.3%
Duolingo completes this week's group with shares trading at $125.76 versus a consensus analyst target of $136.17, representing an 8.3% difference. While considerably narrower than the other companies in this screen, the stock still qualifies because the comparison highlights a measurable disconnect between current pricing and Wall Street expectations.
The company's valuation has generally been influenced by user growth, subscription economics, product expansion, and the pace at which new AI-enabled features translate into engagement and monetization. In businesses built around recurring subscriptions, small changes in user metrics often carry more analytical value than short-term price movements. Monitoring quarterly operating metrics, income statements, analyst estimate revisions, and user-growth disclosures can therefore provide stronger evidence for interpreting this type of spread than relying solely on changes in share price. The data suggests an area worth monitoring rather than a standalone investment conclusion, particularly as future results provide additional evidence on customer growth and profitability trends.
Reading the Signal Behind the Spread
Viewed individually, the five companies in this week's screen operate in very different parts of the market—from digital gaming and media to AI infrastructure, insurance brokerage, and education technology. What connects them is not their business model but the presence of a measurable disconnect between current market pricing and analyst consensus. That makes the spread itself less of an investment signal than a starting point for asking a more useful question: what assumptions are embedded in each side of the equation, and which dataset best explains the difference?
Price targets represent a synthesis of analyst expectations, while the market continuously incorporates new information, changing risk appetite, and evolving macro conditions. Those two processes rarely update at the same speed. As a result, larger gaps often emerge during periods when fundamentals, sentiment, or capital allocation are being reassessed. Some spreads reflect companies executing against long-term growth plans while investors remain cautious; others appear because consensus has yet to fully adjust to newer information. Without additional context, the gap alone cannot distinguish between those cases.
That is where combining multiple datasets becomes significantly more informative than looking at analyst targets in isolation. The Price Target Summary API establishes where Wall Street consensus currently sits, but comparing those figures with the Income Statement API helps determine whether revenue, operating income, and margins are evolving in a direction that supports those expectations. Adding the Cash Flow Statement API introduces another layer by showing whether accounting performance is translating into cash generation, while the Financial Ratios API provides standardized profitability, leverage, and efficiency metrics that make comparisons across sectors more meaningful. Viewed together within the broader Financial Modeling Prep data ecosystem, these datasets create a more complete analytical framework than any single metric can provide on its own.
Ultimately, the most valuable screens are rarely the ones that produce immediate conclusions. They are the ones that efficiently narrow a large investment universe into a manageable set of companies deserving deeper research. In that context, a price-target spread is best viewed as an analytical filter rather than a verdict. The signal gains meaning only after it is tested against earnings quality, cash generation, balance-sheet strength, and the direction of analyst revisions. When those pieces begin to align, or noticeably diverge, the spread evolves from an interesting statistic into a structured research signal worth monitoring.
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 the Narrative Lags the Market Signal
The most interesting signals often emerge before the narrative around a company becomes widely accepted, making systematic screens a useful way to identify where expectations and market pricing have diverged. Throughout this analysis, the Price Target Summary Bulk API served as the foundation for that process, illustrating how a repeatable, data-driven workflow can surface names that warrant closer fundamental research rather than immediate conclusions.
If you enjoyed this analysis, you'll also want to read: Weekly Signals Desk | Concentrated Analyst Revisions via the FMP API (June 22-26)
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.
