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not the full analysis (lenses, memo, scores and the AI panel arrive with the first report).
Report history · DAR
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What this page is: Delvantic's full research page for
Darling Ingredients Inc. (DAR) —
AI-driven forensic equity research: mechanical valuation models (DCF, EPV, anchored-PE, scenario)
plus three independent AI lenses (Quality / Value / Sentiment). Everything below is rendered
server-side; you are not missing content that requires JavaScript. All scores are
predictions and research opinions, not financial advice.
Page map (sections in order; each card carries a stable
reference-name attribute you can cite):
profile-header / price-overview — company profile, live quote, market cap
extended-analysis — the core: three AI lens reads with findings, scores, and the analyst memo
market-narrative / ai-findings / gpt-critique — narrative context, cross-model findings, and an adversarial critique of our own analysis (near the end of the document)
Members-only sections (render as login gates for anonymous readers): price-history, income-trend, key-metrics, financials (statement tables), insider-trading. The analysis above is public; the raw data tables require a free account.
More for machine readers: site briefing at
/llms.txt ·
any ticker resolves at delvantic.com/stock/TICKER ·
raw inputs are public-company filings and market data (via licensed data feeds);
every model, score, lens read, and prediction on this page is Delvantic's own analysis.
Darling Ingredients Inc.
DARNYSE
Consumer Defensive · Packaged Foods
Irving, TX 75038, United States
darlingii.com
Updated Oct 26, 8:38am
Darling Ingredients Inc. is a global developer and producer of sustainable natural ingredients made from edible and inedible bio-nutrients. The company collects and transforms animal by-products, used cooking oil, and food residuals into products for feed, food, fuel, and specialty applications. Its current business spans feed ingredients and services, food ingredients and nutrition, and fuel ingredients, supplying materials such as proteins, fats, collagen, gelatin, and renewable fuel feedstocks. Darling Ingredients also provides customized solutions for industries including animal nutrition, pet food, pharmaceuticals, fertilizer, and bioenergy. Through its circular-economy model, the company turns low-value raw materials into essential inputs used across agriculture, food processing, industrial products, and renewable energy markets.
Runs with full report
Not yet researched. A narrative history — founding, leadership, inflection points, and how the company has behaved through prior macro stress — is generated when the full report runs.
Price Overview
Current data · timestamped when a report runs
$66.55
Change
+1.63 (+2.51%)
Day Range
$65.13 – $67.36
52-Week Range
$29.15 – $69.98
50-Day MA
$63.30
200-Day MA
$54.51
Volume
110,479.00
Deep Analysis
Deep Analysis Engine
Pre-flight intelligence scans the company first, then routes to the right analytical methods.
Pre-Flight Intelligence
Business Segments
Peer Assessment
Method Routing
Market Thesis
Warnings
0Company Classification— What type of company is this?
1Fetch profile, key metrics, ratios, 3 years of income + cash flow
2Score company against 6 archetypes using multi-dimensional signal scoring
3Detect sector mismatch (e.g., TSLA in auto but valued as tech)
4Select valuation approach: which methods to use, skip, and weight
5Identify secondary traits for hybrid/borderline companies
Layer 0 runs first and determines what type of company this is: Mature Earner, High-Growth Profitable, Narrative/Platform, Pre-Profit Growth, Deep Value/Turnaround, or Dividend/Income.
Different company types need fundamentally different valuation methods. A utility and Tesla cannot be valued the same way.
The classification drives which valuation methods are used and how they're weighted in the final synthesis.
1Industry Landscape— Where is the industry headed?
1Load company profile and identify sector/industry
2Discover up to 8 industry peers
3Fetch 3 years of income statements for each peer
4Compute industry-wide revenue and earnings growth (median CAGR)
5Analyse gross, operating, and net margin trends across the industry
6Score tailwind/headwind signals and determine industry outlook
2Company Momentum— Where is this company trending?
1Fetch 3 years of income, balance sheet, and cash flow statements
5EPV = after-tax adjusted earnings / cost of capital + excess cash
What is EPV? — Bruce Greenwald's model: what is the company worth if it never grows again? Uses normalized operating income divided by cost of capital.
Why it matters: EPV is a floor. If the stock trades below EPV, you're getting future growth for free — the market is pricing the company as if it will shrink. If above EPV, you're paying a premium for expected growth.
Excess cash is added on top (cash minus short-term debt) — that's money shareholders could theoretically receive today.
4cAnchored PE— Industry PE adjusted for growth differential
1Load Layer 1 (industry peers) and Layer 3 (growth projections)
2Compute PE for each peer (price / EPS) and take the median
3Calculate growth differential: company growth vs industry growth
4Apply growth premium to industry median PE (capped at 3x)
5Fair value = trailing EPS × adjusted PE
4dReverse DCF— What growth is the market pricing in?
1Load Layer 3 projections and fetch current FCF + market cap
2Binary search: find growth rate where DCF model = current price (50 iterations)
3Compare implied growth to projected growth from Layer 3
4Determine signal: underpriced, fairly priced, or overpriced
How it works: Instead of estimating fair value, this flips the question — "what growth rate would justify today's price?"
Uses binary search to find the FCF growth rate that makes the DCF model equal the current market cap. Then compares that implied growth to projected growth from Layer 3.
The gap is the signal: Implied < Projected → market underprices the growth (potential upside) Implied > Projected → market expects more growth than the data supports (risky) Implied ≈ Projected → price fairly reflects expected growth
4eRevenue-Based DCF— For growth/narrative companies (skip if mature earner)
1Project revenue forward using blended growth rate
2Apply target net margin trajectory (converges to industry median over 5 years)
3Convert projected net income to FCF at 70% conversion rate
4Discount back at CAPM rate — same math as DCF but revenue-driven
Not applicable for Mature Earner companies
4fAnchored P/S— Price-to-Sales peer comparison (skip if mature earner)
1Compute P/S for each peer (price / revenue per share)
2Take industry median P/S
3Apply growth differential premium (same formula as Anchored PE)
4Fair value = revenue per share × adjusted P/S
Not applicable for Mature Earner companies
4gScenario Analysis— Bull / Base / Bear (skip if mature earner)
1Define 3 scenarios: Bull (1.5x growth), Base (1.0x), Bear (0.5x)
2Run revenue-DCF for each scenario with adjusted margins
3Probability-weight: 25% bull + 50% base + 25% bear
4Report fair value range (bear floor to bull ceiling)
Not applicable for Mature Earner companies
4hDividend Discount Model— For dividend/income stocks only
1Compute annual dividend per share from dividend history
2Calculate dividend growth rate (CAGR over 3-5 years)
3Gordon Growth Model: DPS × (1 + g) / (r - g)
4Check payout sustainability
Not applicable for Mature Earner companies
4iBook Value Analysis— For deep value / turnaround stocks only
1Book value, tangible book, and NCAV (Graham liquidation value) per share
2Weighted fair value from all three measures
3Flag if trading below book, tangible, or NCAV
Not applicable for Mature Earner companies
4jInsider Activity— Are insiders buying or selling?
1Fetch last 50 insider transactions from SEC filings
2Filter to last 12 months of activity
3Categorise buys vs sells and compute total dollar values
4Score insider sentiment: heavy buying (+2) to heavy selling (-2)
5Identify notable transactions (top 5 by value)
4fCash Flow Quality— How trustworthy is the FCF?
1Compare free cash flow to net income (accrual ratio) over 3 years
2Measure FCF consistency (coefficient of variation)
3Check operating cash flow vs net income ratio
4Assess capex intensity (capex as % of operating cash)
5Flag negative FCF years and quality concerns
4gDebt Maturity Risk— Can it handle its debt?
1Extract debt structure: total, short-term, long-term, cash position
2Compute interest coverage ratio (operating income / interest expense)
3Calculate debt-to-FCF ratio (years to pay off all debt)
4Check short-term debt coverage (cash vs near-term obligations)
5Track debt trajectory over 3 years (deleveraging, stable, increasing)
3Run 25 DCF scenarios and compute fair value for each
4Count how many scenarios show fair value above current price
5Score robustness: fragile → very robust
4lSector Demand Cycle— Is the sector in a boom, steady state, or contraction?
1Analyse capex acceleration across all peers — are companies investing heavily?
2Measure revenue acceleration breadth — is growth widespread or isolated to one company?
3Check margin health under growth — is demand healthy (pricing power) or pressured?
4Compare sector vs market performance — is capital flowing into this sector?
5Check analyst estimate revision trends — is consensus shifting up or down?
6Determine demand cycle phase: boom, expansion, steady, slowdown, or contraction
5AI Investigation— Adaptive research engine (Claude)
1Gather all financial data + signal layer results into a comprehensive brief
2Pass 1 — "The Story": Claude identifies what's unusual/risky/noteworthy about THIS company
3Generate 6-8 targeted investigation questions with reasoning (not generic templates)
4Execute investigation: web search (if API available) or Claude knowledge base
5Pass 2 — "The Analysis": Synthesise findings across 6 dimensions with investigation answers
6Full audit trail: every question, query, source, and reasoning timestamped
Two-pass AI investigation. Pass 1: Claude reads all financial data and identifies what's unusual about this specific company — the questions an investor needs answered. Pass 2: Each question is investigated (via web search when available, Claude knowledge otherwise), then everything is synthesised into a 6-dimension analysis.
Full audit trail: Every question generated, every search query, every source hit, and all reasoning is logged with timestamps. Expand the investigation log to see exactly what was asked and why.
If Claude API is unavailable, this layer is gracefully skipped and the synthesis proceeds using quantitative methods only.
5bThesis Evaluation— What does the market believe? (narrative/platform stocks only)
1Gather all financial data + classification + reverse DCF implied growth
2Send to Claude: evaluate the market's thesis for this narrative stock
3Break down required revenue by business line to justify the price
4Assess historical precedent — has any company achieved this growth?
5Determine conviction requirements and price sensitivity
For narrative/platform stocks, fair value is meaningless. Instead, this layer asks: what does the market believe, and is that belief reasonable?
Claude evaluates: what each business line needs to generate, historical precedent for the implied growth, what must go right, what could go wrong, and price sensitivity (where it becomes a no-brainer vs clearly overpriced).
Verdict is one of: Reasonable Premium, High Conviction Required, Priced for Perfection, or Disconnected from Fundamentals.
Not applicable for Mature Earner companies
6Valuation Synthesis— Weighted verdict from all methods (requires Layer 4)
1Load cached results from all Layer 4 sub-processes (valuation methods + signals)
2Compute base weighted composite: 50% DCF + 25% EPV + 25% Anchored PE
3Extract signal adjustment factors: growth rate, discount rate, PE premium
4Re-run DCF with adjusted growth + discount rate → signal-adjusted DCF fair value
5Re-run Anchored PE with adjusted premium → signal-adjusted PE fair value
7Apply residual confidence shift + red flag overrides → final verdict
How the composite works: Three valuation methods each produce a fair value. They're weighted: 50% DCF, 25% EPV, 25% Anchored PE.
Signal feedback loop: Strong signals now adjust the fair value itself — not just the verdict threshold. AI-identified tailwinds increase the DCF growth rate, hostile macro raises the discount rate, and competitive moat strength adjusts the PE premium. EPV stays untouched as the growth-agnostic floor.
Adjustment limits: Growth rate ±10pp, discount rate ±2pp, PE premium ±0.15 — prevents runaway estimates while still allowing signals to materially move the fair value.
Method agreement matters: When all three methods point the same direction, the signal is strong. When they disagree, treat the result with caution.
The verdict isn't "buy" or "sell" — it's whether the current price already reflects the company's fundamentals.
computed not applicable not yet run1 computed · 6 not applicable · 17 not yet run
Narrative Economics
The story the market is telling about this stock — the intangible X-factor
(founder mythology, cult dynamics, TAM-of-imagination) that moves price beyond
what cash flows alone explain. After Shiller, Narrative Economics.
No narrative profile yet for DAR — it's generated by the pipeline (market-narrative step).
Claude Reading
Claude Opus reviews the pipeline's raw data + every prior verdict, then writes an independent take.
No AI findings yet for DAR — they're generated by the
pipeline (ai-findings step) when a report is run on this ticker.
Starting analysis…
Safe to close this tab — it keeps running on the server. Come back anytime.
GPT Reading
GPT reads the identical raw briefing blind — one of the Big-3 independent readings — and commits to its own verdict.
No GPT reading yet for DAR — it's generated by the
pipeline (gpt-critique step) after the other Big-3 seats, when a
report is run on this ticker.
Grok Reading
Grok (xAI) reads the identical raw briefing blind — one of the Big-3 independent readings — and commits to its own verdict.
No Grok reading yet for DAR — it's generated by the
pipeline (grok-critique step) alongside the other Big-3 seats, when a
report is run on this ticker.
Advanced Analysis
Forensic deep-dive · separate lenses
A separate, manually-run forensic pipeline (dilution, earnings quality, liquidity → two scored lenses → the play). Hasn't been run for this ticker yet.
Community AI Feedback
1 Copy this and paste it into any AI (ChatGPT, Claude, Gemini…)
Please visit this website https://delvantic.com/stocks/companies/?t=DAR and scan all of its content for the stock knowledge. Do you agree or disagree with any of their findings? If so, which aspects? Are there any improvements or things that they missed or overlooked?