This week's valuation screen surfaced a familiar but increasingly sharp pattern: long-duration cash flows are being discounted aggressively across unrelated sectors, even as underlying fundamentals remain intact. Using the FMP's DCF Valuation API, the scan flagged five large-cap names where modeled intrinsic value and market pricing have meaningfully diverged over a short window—suggesting the move is less about company-specific deterioration and more about how capital is being allocated right now.

What makes the signal notable is its consistency. Payments, energy, regional banking, biotech, and government services all appear in the same output, despite very different operating drivers. The common thread isn't narrative risk—it's duration sensitivity and how quickly the market is repricing future cash flows under current sentiment.

Below, we break down those five valuation gaps and walk through how the FMP DCF Valuation API is used to surface them, step by step—showing how a repeatable DCF workflow can be turned into a live signal rather than a static model.

This Week's Screen: Where Valuation and Price Are Furthest Apart

Fiserv Inc (NASDAQ: FISV)

DCF Value: $260 — Market Price: $67.66 → Upside Potential: ~+284%

Fiserv screens as one of the widest valuation gaps in this week's output, and the signal is striking given the company's position in core payment infrastructure. The modeled DCF value of $260 contrasts sharply with a market price below $70, placing the disconnect well beyond what is typically explained by short-term earnings volatility or integration noise. This gap reflects how aggressively the market is discounting long-duration transaction-driven cash flows, even for platforms with embedded scale and recurring revenue characteristics.

What stands out is not a sudden deterioration in operating performance, but the compression applied to fintech-adjacent multiples amid broader rate and risk recalibration. For a business where merchant volumes, issuer processing, and software-driven services anchor cash generation over multi-year horizons, the magnitude of the discount suggests duration sensitivity rather than idiosyncratic weakness. To contextualize whether this divergence is expanding or stabilizing, income statement trends—particularly organic revenue growth and margin progression—paired with analyst estimate revisions would be the most informative datasets to monitor alongside the DCF signal.

Biogen Inc (NASDAQ: BIIB)

DCF Value: $491.79 — Market Price: $174.82 → Upside Potential: ~+181%

Biogen registers one of the largest absolute valuation spreads in the screen, underscoring how biotech cash flows tied to longer development timelines are being discounted in the current market environment. The nearly $320 gap between DCF value and market price reflects a heavy risk premium applied to pipeline uncertainty, patent cycles, and regulatory complexity—factors that are difficult to compress into near-term earnings metrics.

Rather than signaling a single catalyst, the DCF spread highlights the tension between durable legacy franchises and optionality embedded in future therapies. Markets often respond to biotech narratives in discrete steps, while DCF frameworks smooth those expectations over longer horizons. To contextualize this signal, segment-level revenue data, R&D spend trends, and analyst target dispersion can help illuminate whether the modeled intrinsic value is being challenged by evolving assumptions or simply overshadowed by near-term risk aversion.

Coterra Energy Inc (NYSE: CTRA)

DCF Value: $57.29 — Market Price: $25.52 → Upside Potential: ~+124%

Coterra's appearance on the screen highlights how equity pricing in the energy complex has diverged from modeled intrinsic value, even as balance sheets and free cash flow profiles have structurally improved versus prior cycles. The DCF gap here reflects a market that continues to apply conservative assumptions to commodity-linked cash flows, despite capital discipline and lower breakeven economics across large U.S. producers.

The signal does not hinge on near-term oil or gas price moves, but on how persistently the market discounts normalized cash generation when volatility remains elevated. Coterra's diversified exposure across oil, gas, and natural gas liquids further complicates simplistic valuation shortcuts, often leading to blended multiples that obscure underlying asset economics. To evaluate whether this disconnect narrows or widens over time, cash flow statements and realized pricing data—alongside hedging disclosures—offer the clearest lens into how operational cash generation tracks against the assumptions embedded in the DCF model.

Maximus Inc (NYSE: MMS)

DCF Value: $185.78 — Market Price: $88.23 → Upside Potential: ~+111%

Maximus rounds out the screen as an example of how government services and outsourcing models are being repriced despite relatively stable demand drivers. With the market price sitting at less than half of the DCF-implied value, the signal points to skepticism around contract timing, margin normalization, and funding visibility—rather than a breakdown in the underlying service model.

The valuation gap here is particularly sensitive to assumptions around renewal cycles and operating leverage, which tend to unfold gradually rather than quarter to quarter. As a result, short-term price movements can diverge sharply from longer-horizon cash flow expectations. To better interpret this disconnect, backlog disclosures, segment revenue trends, and historical margin performance—drawn from income statements and contract data—provide the most relevant context for tracking how fundamentals evolve relative to the DCF signal.

South State Corp (NYSE: SSB)

DCF Value: $135.06 — Market Price: $97.25 → Upside Potential: ~+39%

South State's valuation gap is more moderate in absolute terms, but notable given the broader backdrop for regional banks. With a DCF-implied value roughly 40% above the current market price, the signal reflects how balance sheet-heavy businesses continue to trade under a cloud of macro and regulatory uncertainty, even as institution-specific fundamentals diverge meaningfully.

Here, the gap appears tied to how the market is pricing duration, deposit sensitivity, and credit normalization rather than to a single earnings datapoint. Regional banks remain subject to sector-wide risk compression, often overriding differences in asset mix or funding stability. To assess whether the pricing discount is grounded in fundamentals or sentiment, loan growth trends, net interest margin evolution, and credit quality metrics from the balance sheet and income statement would be central inputs to track alongside the DCF output.

Reading the Signal Beneath the Price Action

Viewed together, these five names reflect a shared condition rather than a set of isolated mispricings. The gaps cluster around businesses with long-duration cash flows—payment infrastructure, energy assets, bank balance sheets, biotech pipelines, and government contracts—where valuation is highly sensitive to how future economics are discounted.

What's most consistent across the group is the pace of repricing. In several cases, underlying operating metrics have moved only marginally, while equity prices have absorbed a much larger adjustment. That divergence is where DCF-based analysis becomes most useful: not as a directional call, but as a way to distinguish between changing assumptions and changing fundamentals. When modeled intrinsic values remain relatively stable while prices compress in tandem, the signal reflects shifts in risk tolerance and capital allocation more than near-term execution. For context on how those assumptions flow through a DCF framework, this guide provides a clear reference point.

The interpretation sharpens further when DCF outputs are viewed alongside complementary data—analyst expectations, cash flow durability, margin behavior, and balance sheet flexibility. Triangulating those inputs across datasets available through FMP helps frame whether a valuation gap stems from weakening fundamentals or from how the market is choosing to discount them. In that sense, the gap itself isn't the conclusion—it's the starting point for identifying where future data is likely to matter more than prevailing narratives.

Turning DCF Snapshots Into a Live, Repeatable Signal

A point-in-time DCF read is useful for spotting misalignment in the moment, but it breaks down quickly once prices, estimates, and assumptions start moving. To understand whether a valuation gap is stabilizing, widening, or closing, the workflow has to be repeatable. That means recalculating intrinsic values on a schedule, matching them against current market prices, and tracking how the spread evolves as new information is absorbed.

Before you start, make sure your API key is ready.

Step 1. Query the DCF Valuation API

The workflow begins with the DCF endpoint. This endpoint is designed to streamline the process by returning both the modeled intrinsic value and the live market price in a single response. By keeping valuation and price data in the same payload, it eliminates the need to stitch together multiple datasets before analysis.

Sample response

[

{

"symbol": "AAPL",

"date": "2025-02-04",

"dcf": 147.27,

"Stock Price": 231.80

}

]

Step 2. Compute the Upside

With both fields in hand, the next step is to normalize the gap. Converting the difference between DCF and market price into a percentage allows the results to be compared across names with very different share prices:

Upside % = (DCF - Stock Price) / Stock Price × 100

In the example above, the calculation produces roughly -36%, indicating the stock is trading above the modeled intrinsic value. Positive figures flag the opposite condition—where price sits below DCF—which is the core signal this screen is designed to capture.

Step 3. Scale It into a Screening Loop

The workflow becomes materially more useful once this logic is applied at scale. Running the DCF endpoint across a defined universe, calculating the percentage spread for each symbol, storing the results, and ranking them by upside converts a static check into a living screen. When automated on a recurring cadence, the process continuously surfaces where price and intrinsic value are drifting further apart or beginning to converge, making it easier to monitor valuation pressure as market conditions shift.

Scaling a Tested Valuation Loop Across Broader Coverage

Valuation workflows tend to scale most effectively when they're expanded deliberately, not all at once. The Basic plan is typically where the loop gets proven out—enough capacity to run the DCF process, validate calculations, and test storage and ranking logic across a focused set of names. At this stage, the goal isn't coverage for its own sake, but confidence that the mechanics behave as expected under repeat use.

Once that foundation is in place, broader coverage becomes a practical next step rather than a structural change. The Starter plan extends the same workflow across a wider U.S. equity universe and deeper history, without requiring adjustments to how the loop is built. The data expands; the process stays intact. That continuity is what allows screens to scale without introducing unnecessary complexity.

For desks running frequent refreshes or monitoring multiple regions, the workflow usually settles into the Premium plan. Higher request limits and access to U.K. and Canadian equities support continuous, multi-market execution, shifting the loop from a periodic scan to an always-on reference. At that point, the system is less about individual queries and more about maintaining a consistent valuation signal as inputs update throughout the day.

When Analyst Tools Become Shared Infrastructure

Once a valuation workflow proves reliable on a single desk, its relevance tends to expand quickly. What begins as a focused solution to streamline one analyst's process often exposes a broader gap across teams: inconsistent assumptions, duplicated models, and parallel spreadsheets attempting to answer the same questions in slightly different ways. At that stage, the conversation naturally shifts from individual efficiency to institutional consistency.

Analysts are often the catalysts for that transition. By formalizing a workflow that consistently pairs live prices with repeatable intrinsic value logic, they create a reference point others can build around. As adoption widens, the benefits become structural rather than incremental—shared dashboards replace one-off files, valuation logic becomes transparent and versioned, and teams spend less time reconciling numbers and more time interpreting what they mean. Governance follows naturally when inputs, transformations, and outputs are standardized rather than improvised.

When a process reaches that level of usage, it typically needs infrastructure designed for shared access, auditability, and controlled scaling. For teams formalizing a workflow that has already proven itself in practice, the Enterprise Plan often becomes the practical home—supporting firmwide deployment without forcing a rewrite of the logic that made the system effective in the first place.

Using Valuation Dislocations to Frame What Comes Next

Viewed collectively, these valuation breaks function less as conclusions and more as markers—signaling where market pricing has moved faster than the underlying cash-flow math. Anchored by signals from the FMP DCF Valuation API, the exercise reframes attention toward areas where future data updates are likely to carry more informational weight than prevailing narratives. The real signal emerges over time, as these gaps either persist, compress, or reshape in response to changing assumptions.

Expand your watchlist with our previous deep dive: Weekly Signals Desk | Price vs Target Gaps Emerging via the FMP API (Dec 8-12)

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.