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🛢️ Chevron × Hess — $53B M&A Valuation Analysis

Full investment banking-style valuation and deal analysis of Chevron's acquisition of Hess Corporation — six methodologies, Monte Carlo simulation, LBO, and accretion/dilution modeling, built entirely in Python.

Python Jupyter License: MIT Data

Replicates the analytical framework of a bulge-bracket M&A pitch book — applied to one of the most strategically significant energy deals of 2023. Announced October 2023 · All-stock deal · $171/share offer.

📊 Methodologies · 🔍 Key Findings · 📈 Visualizations · 📄 Reports · 🚀 Quick Start


⚡ Deal at a Glance

Parameter Detail
Deal Chevron Corporation acquires Hess Corporation
Announced October 2023
Deal Value $53 billion (all-stock)
Offer Price $171 per share
Strategic Rationale Guyana deepwater reserves (Stabroek Block) + Bakken consolidation
Accretion / Dilution 6.9% dilutive to CVX EPS at deal close
Synergy Breakeven ~$2.6B in annual synergies required
Analysis Scope 49-cell notebook · 8 sections · 15+ IB-grade charts

🔍 Key Findings

Verdict: A long-duration growth bet on Guyana, not a near-term earnings story.

  • Chevron’s $171/share offer implies a modest ~10% premium to Hess’s unaffected price — within historical upstream M&A range but well below precedent mega-deal premiums
  • Cash-flow methodologies (DCF, LBO) value Hess significantly below the offer price — quantifying the “Guyana premium” embedded for undeveloped offshore reserves in the Stabroek Block
  • Market-based methodologies (trading comps, precedent transactions) bracket the deal price; the implied premium range ($168–$183) nearly matches the offer, suggesting the market was already pricing in a takeout
  • The deal is 6.9% dilutive to CVX EPS at close, requiring ~$2.6B in annual synergies to break even on an accretion basis — confirming this is a reserve acquisition, not a margin play

📊 Valuation Methodologies

Six independent methodologies implemented end-to-end in Python:

# Methodology Key Output
1 Trading Comparable Companies EV/EBITDA, EV/Production, P/E across E&P peers (COP, DVN, MRO, APA, OVV)
2 Precedent Transactions Historical upstream M&A multiples with control premium analysis
3 DCF with Monte Carlo 5-year UFCF projection, WACC discounting, 10,000-path probabilistic output
4 Leveraged Buyout (LBO) Sponsor return framework implying floor valuation
5 Accretion / Dilution & Synergy Analysis Pro forma CVX EPS impact + breakeven synergy threshold
6 Premium Analysis Implied premium vs. unaffected price: 1-day, 1-week, 52-week benchmarks

All six outputs are aggregated into a Football Field chart showing the full valuation range vs. the $171 deal price.


📈 Visualizations

15+ professional-grade charts styled after IB deliverables:

Chart What It Reveals
Football Field Valuation Full range across all 6 methods vs. deal price — where the Guyana premium sits
DCF Sensitivity Heatmap WACC vs. terminal growth rate — how sensitive implied price is to macro assumptions
Monte Carlo Distribution 10,000-path probability distribution of DCF outputs under parameter uncertainty
UFCF Waterfall Bridge EBITDA-to-FCF conversion showing capex and working capital drag
Tornado Chart Ranked single-variable sensitivity — which DCF input drives valuation most
Trading Comps Scatter EV/EBITDA vs. EV/Production for peer E&P universe
Precedent Transactions Historical deal multiples with Hess deal highlighted
Accretion / Dilution CVX EPS impact across synergy scenarios — where breakeven lies
WTI Oil Price Chart Crude annotated with deal milestones and macro events
Sector Performance Hess vs. CVX vs. XLE since deal announcement

📄 Downloadable Reports

Three full-length research documents included in the repository:

Report Description
Chevron_Hess_$53B_Deal_Analysis.pdf Full deal analysis — valuation, synergies, strategic rationale
Chevron_Hess_Mega_Deal_White_Paper.pdf Executive white paper — deal thesis and market context
Chev_Hess.pdf Supplementary analysis

🗂️ Repository Structure

hess-chevron-valuation-analysis/
│
├── 📓 hess_chevron_analysis.ipynb     # Main notebook — 49 cells, 8 sections
├── 📁 config/
│   ├── constants.py                   # Hardcoded financials, deal terms, precedent comps
│   └── fallback_data.py               # Cached peer data for offline execution
├── 📁 utils/
│   ├── styling.py                     # IB-grade chart functions (11 chart types)
│   └── data_fetch.py                  # yfinance / FRED wrappers with fallback logic
├── 📁 docs/                           # Supporting documentation
├── 📄 Chevron_Hess_$53B_Deal_Analysis.pdf
├── 📄 Chevron_Hess_Mega_Deal_White_Paper.pdf
├── 📄 Chev_Hess.pdf
├── requirements.txt
└── .env.example                       # FRED API key template

🗃️ Data Sources

Source API Key Provides
Yahoo Finance (yfinance) ❌ Not required Peer trading data, WTI crude, XLE sector ETF
FRED ✅ Free key IG credit spreads, Henry Hub gas, Fed Funds rate
SEC EDGAR (10-K filings) ❌ Not required Hess historical financials FY2020–FY2022
Damodaran Online ❌ Not required Beta estimates, ERP, cost of capital inputs

FRED API key is free — get one at fred.stlouisfed.org. The notebook runs fully without it using cached fallback data.


🚀 Quick Start

git clone https://github.com/DogInfantry/hess-chevron-valuation-analysis.git
cd hess-chevron-valuation-analysis
pip install -r requirements.txt
cp .env.example .env          # Add your FRED API key (optional)
jupyter notebook hess_chevron_analysis.ipynb

🛠️ Technology Stack

Modeling      │ pandas · numpy · scipy
Visualization │ matplotlib · seaborn
Live Data     │ yfinance · fredapi
Environment   │ python-dotenv
Runtime       │ Python 3.8+ · Jupyter Notebook

⚠️ Disclaimer

This analysis is for educational and demonstrative purposes only. It does not constitute investment advice, a solicitation, or a recommendation to buy or sell any security. All data is sourced from public filings and market data providers.


Deal Announced: October 2023  ·  Deal Value: $53B  ·  Status: ✅ Complete

Built to IB pitch book standards · Python-native · No paid data required

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A full-scale corporate finance and valuation framework of the Chevron-Hess Mega-Deal. Built entirely in Python, showcasing data driven DCF, LBO, and synergy analysis for strategic M&A evaluation.

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