The latest valuation sweep lit up five names where modeled intrinsic value has broken sharply from where the market is currently pricing risk. This week's divergence list — drawn directly from the FMP DCF Valuation API — shows a cluster of companies whose discounted cash-flow estimates are accelerating far faster than their spot prices.

In this article, we walk through the API mechanics behind the screen and the signals these gaps are throwing off right now.

This Week's Outliers and Their Signals

1) Apollo Global Management NYSE:APO

• DCF Value: $788.20
  
  
• Market Price: $130.6
  
  
• Upside Potential: ~503.5%
  
  

A ~500% modeled upside is rare for a firm of Apollo's scale. It points to a market still pricing private-markets exposure as if deal-flow and exits remain structurally impaired — while the DCF assumes sustained monetization capacity. Yet Apollo's recent $745M asset-backed financing deal with Virgin Atlantic shows the firm continues to originate non-traditional transactions, reinforcing fee durability and funding reach (Press release).

To determine whether the gap is mispricing or justified skepticism, the most telling datasets will be fee-related earnings, realised gains, and insider equity activity. If exit multiples and incentive-fee generation remain resilient, the discrepancy between ~$130 and a modeled ~$788 narrows quickly.

2) Owens Corning Inc NYSE:OC

• DCF Value: $262.65
  
  
• Market Price: $101.83
  
  
• Upside Potential: ~157.93%
  
  

Owens Corning's ~160% upside sits against a backdrop of cyclical pressure: the stock recently touched a 52-week low, reflecting pessimism around housing demand and insulation/roofing volumes. The DCF, however, implies the market may be over-discounting the downturn and underpricing cash-flow durability across composites and specialty materials.

Key reads here include margin progression in the income statement, changes in construction backlogs, and fund-ownership shifts — especially as several institutions have trimmed positions. Any signs of stabilizing volumes or price recovery could flip sentiment sharply.

3) Maximus Inc NYSE:MMS

• DCF Value: $181.05
  
  
• Market Price: $79.8
  
  
• Upside Potential: ~126.88%
  
  

For a government-services provider, a ~127% valuation gap suggests the market may be overlooking contract momentum. Maximus' recent $86M U.S. Air Force award extends its footprint into cyber/technical services, a higher-margin adjacency that could reset its earnings trajectory beyond legacy health-administration work (Press release).

To gauge whether the upside is credible, track contract-award cadence, margin uplift from automation initiatives, and analyst estimate revisions. If Maximus converts new federal wins into multi-year revenue streams, the current ~$80 price could materially understate forward earnings power.

4) Coterra Energy Inc NYSE:CTRA

• DCF Value: $55.41
  
  
• Market Price: $26.34
  
  
• Upside Potential: ~110.36%
  
  

Coterra's ~110% modeled upside reflects tension between strong operational guidance and market skepticism. The company raised 2025 production targets and projected $2B in free cash flow, yet shares remain depressed after a recent earnings miss and continued pressure from activist Kimmeridge demanding board and strategic changes (Q2 and Q3 earnings release).

The valuation signal hinges on whether execution overrides governance noise. Focus on reinvestment rates, breakeven levels, and any board-level shifts. If commodity prices stabilize and Coterra delivers on cash flow, the path toward a ~$55 value becomes significantly more plausible.

5) Franklin Resources Inc NYSE:BEN

• DCF Value: $40.77
  
  
• Market Price: $22.17
  
  
• Upside Potential: ~83.90%
  
  

An ~84% upside for Franklin suggests the market still discounts the firm as a legacy active manager, even as it accelerates into higher-growth adjacencies. Its multi-year AI partnership with Wand AI and improving Q3 revenue/EPS trends point to operating leverage that may not yet be priced in.

To assess whether the DCF is directionally right, track net flows, AUM mix shifts toward alternatives/digital assets, and margin expansion. If Franklin sustains AUM diversification and cost discipline, the gap between $22 and a modeled ~$40 starts to look less theoretical.

Interpreting the Gap Behind the Prices

The five names flashing on this week's screen don't share sectors, size profiles, or catalysts — but they do share one structural signal: the market is reacting to visible pressures while the modeled values are responding to underlying cash-flow durability, contract optionality, or balance-sheet leverage that hasn't fully repriced. The lag isn't noise. It's a timing mismatch between what's happening in the business and what's being discounted in the tape.

Across Apollo, Owens Corning, Maximus, Coterra, and Franklin Resources, the common thread is that the market has anchored to near-term uncertainty — cyclical softness, governance friction, commodity volatility, policy-dependent revenue, or active-management skepticism. But the DCF output is capturing forward cash flow expectations that pull ahead of sentiment. When these gaps cluster across unrelated sectors, it usually means the driver isn't a sector theme — it's a market regime where investors are shortening their horizon while fundamentals are lengthening theirs.

That's where deeper cross-endpoint analysis matters. When modeled values from the DCF Valuation API are paired with forward indicators — like segment-level revenue trajectories from the Income Statement Bulk API, capital structure shifts from the Balance Sheet Statement API, or trend inflections in Institutional Ownership and Insider Trades endpoints — the context behind each mispricing becomes clearer. For example, Apollo's fee-income momentum or Maximus' contract-award pipeline looks different when aligned against analyst consensus from the Analyst Estimates API. Owens Corning's downside compression looks more mechanical once compared to historical margin bands from the Key Metrics API.

Patterns emerge when these datasets are stacked: some gaps reflect genuine dislocation, others stem from overly cautious pricing, and a few resemble early-cycle recognition that hasn't yet reached consensus. Understanding which bucket a name falls into often requires stepping back to the broader valuation logic that underpins these models — the same framework outlined in FMP's discussion of discounted cash flow mechanics. In that light, the lag becomes less about isolated anomalies and more about how the market digests information relative to fundamental drivers tracked across the FMP ecosystem. It's a quiet reminder that valuation spreads are rarely static; they're signals of where sentiment has yet to converge with the data.

How to Build a Live DCF Signal Pipeline

A one-off DCF pull is useful, but it doesn't tell you where valuation gaps are forming in real time. To turn the calculation into an actual signal, you need a process that refreshes intrinsic values, lines them up against current prices, and elevates the widest spreads automatically.

Before wiring anything together, make sure you've generated your API key.

Step 1. Query the DCF Valuation API

Start by hitting the DCF Advanced endpoint and pulling the modeled fair value alongside the latest market price. Both numbers come back in the same payload, which removes the usual hassle of joining valuation data with a separate price feed. A typical response looks like:

Sample response

[

{

"symbol": "AAPL",

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

"dcf": 147.27,

"Stock Price": 231.80

}

]

Step 2. Compute the Upside

With those two fields in hand, convert the spread into a percentage so names can be ranked consistently:

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

In the example above, the result lands around -36%, indicating the stock is pricing above modeled value. Flip that into positive territory and you're looking at a discount — the basis for a potential signal.

Step 3. Scale It into a Screening Loop

The real value comes from applying this formula across a broad ticker list. Run the same request and calculation on every symbol you care about, save the outputs, and sort them by upside. Once this loop is running, the workflow shifts from a manual check to a live screen that continuously highlights where the market and the intrinsic model are most misaligned.

From Prototype to Scaled Coverage

The smoothest way to stand up a valuation workflow is to start small and validate the core loop. The Basic plan is ideal for this stage — enough access to test the DCF pipeline, shape your output format, and stress-check a limited watchlist before scaling beyond a pilot environment. Once the process is steady and you want broader U.S. equity reach or deeper history, stepping up to the Starter plan is the cleanest progression, and it doesn't require reworking the underlying logic.

Teams that need to run screens across multiple markets or keep valuation models updating throughout the day generally move straight into the Premium plan, which adds U.K. and Canadian coverage and the call volume required for continuous, high-frequency refresh cycles.

When a Desk Workflow Becomes Firmwide Infrastructure

Once a valuation model starts delivering consistent signal at the desk level, the real opportunity is no longer in tweaking the code — it's in turning that workflow into something the entire organization can rely on. What begins as one analyst's tool often becomes the backbone for shared dashboards, unified data feeds, and common calculation logic. That shift reduces fragmentation: instead of every team rebuilding its own version, they work from a single source of truth.

As soon as multiple groups lean on the same output, standardization becomes a competitive advantage. Research, portfolio management, and risk all benefit when the conversation moves away from reconciling numbers and toward interpreting what the signal actually implies. Governance features — traceable assumptions, version history, and auditable transformations — aren't overhead; they're what allow the workflow to persist through personnel changes and support decision-making across mandates.

Analysts who build these systems often become internal champions for data discipline, not because they set out to own the process, but because their tools quietly become firmwide infrastructure. When that happens, it's usually time to put the workflow on a platform built for shared use. For teams formalizing that transition, the Enterprise Plan offers a straightforward way to anchor a proven desk model inside a governed, scalable architecture without altering the logic that made it effective.

Where Valuation Gaps Hint at Positioning Shifts

When you track valuation as a moving spread rather than a static metric, the shifts often reveal positioning changes before they show up in sentiment. The FMP DCF Valuation API makes those inflections visible, highlighting where recognition is starting to catch up to fundamentals. It's a small edge, but in fast-moving markets, early clarity often matters more than precision.

Expand your watchlist with our previous deep dive: Price vs. Consensus: Target Gaps via FMP API (Nov 3-7)