Marion, NY 14505, United States
IPO 1998
senecafoods.com
Updated Jun 27, 12:16am
Price
$170.38
Market Cap
$1.2B
Employees
2,800
Beta
-0.04
Avg Volume
142,167
CEO
Paul L. Palmby
Business Description
Seneca Foods Corporation stands as a key provider of pre-packaged fruits and vegetables, distributing its products throughout the United States and globally. The company's diverse product range includes produce in canned, frozen, and bottled forms, as well as jarred fruits, snack chips, and various other food items. These offerings are marketed under Seneca's own private labels and through a selection of national and regional brands that it either owns or holds licenses for, such as Seneca, Libby's, Aunt Nellie's, Cherryman, Green Valley, and READ. Beyond its branded goods, Seneca Foods also offers contract packing services for canned and frozen vegetables. The corporation's activities further encompass the sale of cans and can ends, alongside dedicated trucking and aircraft operations. Its wide-ranging customer base includes grocery retailers like supermarkets, mass merchandisers, limited assortment stores, club stores, and dollar stores. Additionally, it supplies food service distributors, restaurant chains, industrial markets, other food packagers, and international export clients in 90 countries. Federal, state, and local governments also procure Seneca's products for school and other essential feeding initiatives. Founded in 1949, Seneca Foods Corporation's headquarters are located in Marion, New York.
Business History
Not yet researched. Click the button to generate a narrative history — founding, leadership, inflection points, and how the company has behaved through prior macro stress.
Price History (1 Year)
Last updated: Jun 27, 2026 12:16am (8h ago)
Key Metrics
SourceAPI: Fmp
FetchedJun 27, 2026 12:16am (9h ago)
ExpiresJun 28, 2026 12:16am
FreshnessRecent
API Direct from providerCALC Derived from statements
Rating scale · direction-aware
P/E Ratio (Price per dollar of earnings)
Stock Price / EPS (Diluted)
—
Stock Price: $170.38 EPS (Diluted): N/A
EPS not available in income statement
P/B Ratio (Price vs net asset value)
API
Stock Price / Book Value Per Share
1.37
Stock Price: $170.38 Total Equity: N/A Shares: N/A
Equity not available in balance sheet
EV/EBITDA (Total value vs operating profit)
API
Enterprise Value / EBITDA
6.58
Market Cap: $1.16B Total Debt: $0.00 Cash: $0.00 EBITDA: N/A
EBITDA not available
Enterprise Value (Takeover price (cap + debt - cash))
API
Market Cap + Total Debt - Cash
$1.3B
Market Cap: $1.16B Total Debt: $0.00 Cash: $0.00
P/S Ratio (Price per dollar of revenue)
API
Stock Price / Revenue Per Share
0.62
Stock Price: $170.38 Revenue: N/A Shares: N/A
EV/Sales (Total value vs revenue — works when P/E can't)
API
0.76
Gross Margin (Revenue left after direct costs)
API
Gross Profit / Revenue
13.9%
Gross Profit: N/A Revenue: N/A
Operating Margin (Revenue left after all operations)
Missing from API: Operating Income, Tax Rate, Equity
Current Ratio (Can it pay short-term bills)
API
Current Assets / Current Liabilities
5.06
Current Assets: N/A Current Liabilities: N/A
Debt/Equity (Leverage — debt vs equity)
Total Debt / Total Equity
—
Short-Term Debt: $0.00 Long-Term Debt: $0.00 Total Debt: $0.00 Total Equity: N/A
Zero debt — this company carries no debt obligations. Strongest possible score.
Rev/Share (Top-line per share)
Revenue / Shares Outstanding
—
Revenue: N/A Shares: N/A
Missing from API: Revenue, Shares
Book Value/Share (Net assets per share)
(Total Assets - Total Liabilities) / Shares
—
Total Equity: N/A Shares: N/A
Missing from API: Total Equity, Shares
FCF/Share (Real cash generated per share)
(Operating Cash Flow + CapEx) / Shares
—
Operating CF: N/A CapEx: $0.00 Shares: N/A
Missing from API: Operating CF, Shares
Div Yield (Annual income from holding)
API
Last Annual Dividend / Stock Price
0.0%
Last Dividend: N/A Stock Price: $170.38
Payout Ratio (Earnings paid out as dividends)
Dividends Paid / Net Income
—
Dividends Paid: N/A Net Income: N/A
Dividends paid not available in cash flow statement
Industry Benchmarks
What is Rating Reliability?
How trustworthy the rating next to it is, on a 0–10 scale. Starts at 10 and loses points when the
system spots issues that can mislead the label — e.g. a negative P/E (math says "cheap", reality
says "losing money"), a peer benchmark where most companies are unprofitable, or a profit-based
metric applied to a pre-profit growth company. A low score means the rating word is technically
correct but the comparison behind it is shaky. Hover the score to see what specifically pulled it
down.
Compares SENEA against LLM-researched typical ranges for its
industry. One research call per industry, cached indefinitely — every stock in the same industry
reuses the same baseline.
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 via FMP
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
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
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)
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
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
4jInsider Activity— Are insiders buying or selling?
1Fetch last 50 insider transactions from FMP
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.
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.
Income Statement
Requires paid FMP plan for this ticker
Analyst Estimates (Annual)
Last updated: Jun 27, 2026 12:16am (8h ago)
Metric
2028
2029
2030
2031
Revenue
$1.9B
$1.9B – $1.9B
$1.9B
$1.9B – $1.9B
$1.8B
$1.8B – $1.8B
$1.8B
$1.8B – $1.8B
EBITDA
$323.9M
$323.9M – $323.9M
$330.6M
$330.6M – $330.6M
$307.3M
$307.3M – $307.3M
$313.8M
$313.8M – $313.8M
Net Income
$96.2M
$96.2M – $96.2M
$104.0M
$104.0M – $104.0M
$90.3M
$90.3M – $90.3M
$95.1M
$95.1M – $95.1M
EPS
—
—
—
—
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 SENEA — it's generated by the pipeline (market-narrative step).
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=SENEA 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?
