debt_leverage_profile
Net debt/EBITDA ~4-5x; 20% rate rise increases annual interest expense ~$15-20M given ~$1.5B floating-rate debt exposure
Inferred
Agent_Inference
interest_rate_sensitivity
Roughly 60-70% of debt is floating-rate (LIBOR/SOFR-linked); 20% rate increase adds ~$18M annual interest burden, compressing net income ~15%
Inferred
Agent_Inference
geopolitical_supply_exposure
High intensity; OPEC+ supply decisions and Russia-Ukraine conflict drive price volatility.
Medium
GICS-commodity-overlay-v1
supply_chain_dependency
Strait of Hormuz (Persian Gulf crude flows) and Turkish Straits/Bosphorus (Black Sea crude exports, especially Russian Urals)
Inferred
Agent_Inference
international_expansion_readiness
Revenues denominated in USD globally; minimal sovereign currency devaluation risk as tanker charter rates are USD-settled worldwide
Inferred
Agent_Inference
geographic_footprint
Operations span Mediterranean, North Sea, Americas, Asia-Pacific; USD-denominated contracts insulate against local currency devaluation in all major revenue markets
Inferred
Agent_Inference
commodity_exposure_profile
High intensity; commodities: Crude Oil, Natural Gas, Coal, Refined Petroleum Products, Uranium, Steel (equipment); geopolitical: OPEC+ supply decisions and Russia-Ukraine conflict drive price volatility.
Medium
GICS-commodity-overlay-v1
vendor_lock_dependency_score
Shipyard concentration risk (Korean/Chinese yards for newbuilds) and single-fuel-type dependency; no vendor exceeds 30% of operational input cost
Inferred
Agent_Inference
business_model_type_primary
Asset-heavy maritime shipping; zero cloud infrastructure dependency — operations run on vessel management software and satellite communications, not public cloud
Inferred
Agent_Inference
business_model_type_secondary
Physical tanker fleet operator; cloud termination would disrupt back-office IT but not core vessel operations or revenue generation
Inferred
Agent_Inference
switching_cost_profile
Minimal API coupling risk; uses standard maritime software (Veson IMOS, DNV tools); no proprietary API lock-in with single provider
Inferred
Agent_Inference
howey_test_risk_index
Low Howey Test risk; revenue from physical tanker chartering (time/spot charters) — clearly a service/asset model, not an investment contract
Inferred
Agent_Inference
regulatory_burden_tier
High
Medium
GICS-regulatory-overlay-v1
data_sovereignty_risk
Low GDPR/CCPA exposure; handles crew HR data and limited commercial counterparty data; not a data-intensive consumer-facing business
Inferred
Agent_Inference
antitrust_exposure_flag
Low antitrust risk; TEN holds ~1-2% of global tanker capacity; shipping freight markets are highly fragmented and globally competitive
Inferred
Agent_Inference
regulatory_exposure_profile
High burden; regimes: EPA, FERC, DOE, CFTC, OSHA, SEC; Accelerating emissions mandates and methane rules threaten capex economics.
Medium
GICS-regulatory-overlay-v1
revenue_model_type
~40-50% recurring (time charters, multi-year contracts), ~50-60% spot/transactional; mix shifts with market cycle favoring spot in strong freight markets
Inferred
Agent_Inference
monetization_vector
Vessel day-rate monetization via time charters and voyage charters; revenue scales directly with fleet size and prevailing tanker freight rates
Inferred
Agent_Inference
pricing_architecture
Freight rate pricing driven by Baltic exchange benchmarks; TEN is price-taker in spot market, price-setter only in negotiated time charters with oil majors
Inferred
Agent_Inference
pricing_power_rating
Moderate; limited individual pricing power as rates are market-determined, but fleet quality and oil-major relationships support slight premium over spot indices
Inferred
Agent_Inference
target_gross_margin_bracket
Vessel operating margin typically 40-55%; time-charter equivalent earnings minus OPEX/day of ~$8,000-$12,000 per vessel depending on vessel class
Inferred
Agent_Inference
churn_vulnerability_index
No free-rider problem; tanker capacity is rivalrous and excludable; churn risk exists if time-charter customers (oil majors) revert to spot at renewal
Inferred
Agent_Inference
headcount_cost_structure
Revenue growth is asset-linear, not headcount-linear; doubling fleet requires proportional crew/vessel staff but minimal shore-side headcount increase
Inferred
Agent_Inference
marginal_cost_of_growth
Marginal cost of growth is high capital (vessel acquisition ~$60-100M/unit) but low incremental overhead; sublinear headcount scaling at corporate level
Inferred
Agent_Inference
franchise_compliance_risk
Not applicable; TEN does not operate a franchise model
Inferred
Agent_Inference
customer_acquisition_metric
At 10x scale (~500 vessels), CAC remains near zero (broker-mediated spot/charter markets); unit economics compress due to market impact on freight rates
Inferred
Agent_Inference
network_effect_present
No meaningful network effects; fleet size provides commercial credibility but does not create demand-side network externalities typical of platform businesses
Inferred
Agent_Inference
asset_efficiency_ratio
AI displacement risk is low for core vessel operations; AI may reduce shore-based administrative headcount ~10-15% but cannot displace physical shipping assets
Inferred
Agent_Inference
recession_resistance_tier
Tier 3 (moderate cyclicality); oil demand is relatively inelastic in mild recessions, but severe demand destruction (2008/2020) causes sharp freight rate declines
Inferred
Agent_Inference
customer_segment_primary
Major international oil companies and trading houses (Shell, BP, Total, Vitol) representing ~60-70% of time-charter contracted revenue
Inferred
Agent_Inference
customer_segment_secondary
National oil companies and independent refiners representing ~30-40% of voyage charter and spot revenue; higher concentration risk per voyage
Inferred
Agent_Inference
characteristic_occupations
["11-0000 Management Occupations", "13-0000 Business and Financial Operations Occupations", "15-0000 Computer and Mathematical Occupations", "17-0000 Architecture and Engineering Occupations", "19-0000 Life, Physical, and Social Science Occupations", "23-0000 Legal Occupations", "41-0000 Sales and Related Occupations", "43-0000 Office and Administrative Support Occupations", "47-0000 Construction and Extraction Occupations", "49-0000 Installation, Maintenance, and Repair Occupations", "53-0000 Transportation and Material Moving Occupations"]
High
SOC-2018/GICS-overlay
agent_automatable_labor_share
0.33 (HIL — ~33% of characteristic roles agent-automatable)
Medium
SOC-2018 + agentic-exposure-v1
capital_expenditure_profile
Capital actively reallocated toward LNG-capable and dual-fuel vessels (future-state); legacy single-fuel Aframax/Suezmax fleet being selectively recycled
Inferred
Agent_Inference
sec_cik
0001166663
High
SEC-EDGAR
ticker
TEN
High
SEC-EDGAR