Introduction
$7 trillion. That's roughly how much the U.S. has spent on military operations since 2001 alone. But beyond the direct costs, every intervention sends ripples through financial markets, creating winners, losers and a trail of economic consequences that most investors never see coming.
In this project, we decode those ripples. By aligning 29 carefully selected U.S. military interventions spanning 58 years with daily stock data from 5,053 companies across 15 sectors, we reveal how geopolitical shocks translate into market volatility, sector rotations and measurable abnormal returns.
Our focus isn't on judging the interventions themselves... It's on understanding the economic footprint they leave behind. Which sectors suffer? Which ones thrive? And perhaps most importantly: Do the empirical insights align with public opinion?
Our Analysis at a Glance
What We Expect to Find
Before diving into the data, we started with common assumptions about how markets react to military interventions. Here are the hypotheses we set out to test.
H1 Energy Dominance
"The Energy sector will be hit hardest by military interventions because wars disrupt oil supply chains."
Wars in oil-rich regions like the Middle East historically trigger oil price spikes and supply fears.
H2 Immediate Panic
"Markets will crash immediately when wars start, with panic selling across all sectors."
Uncertainty and fear should trigger immediate risk-off behavior from investors.
H3 Middle East Focus
"Interventions in the Middle East will have the strongest market effects due to oil dependency."
The region controls major oil reserves and strategic chokepoints like the Strait of Hormuz.
H4 Objectives Matter
"The objective of an intervention shapes market reaction, regime changes will rattle markets more than limited protective missions."
Aggressive regime changes signal prolonged instability, while protective missions are seen as limited in scope.
H5 Intensity-Impact
"High-intensity wars will cause bigger market crashes. More violence means more economic damage."
Intuition suggests that deadlier, longer conflicts should produce proportionally larger market reactions.
H6 Duration Matters
"Longer wars will cause more cumulative damage to markets than short, decisive operations."
Extended uncertainty and resource drain should compound negative effects over time.
Now let's evaluate those assumptions against 58 years of market data.
Finance 101
New to finance? Here's everything you need to understand our analysis.
CAPM, Capital Asset Pricing Model
Before we dive into the metrics, let's understand the foundation of our analysis. CAPM is a model that predicts what a stock should return based on how the overall market is doing.
The Weather Forecast Analogy
Think of CAPM like a weather forecast for stocks. Just as meteorologists predict tomorrow's temperature based on historical patterns and current conditions, CAPM predicts a stock's "expected return" based on how it has historically moved with the market. If the forecast says 70°F but it's actually 85°F, that 15° difference is "abnormal", something unusual happened. Same idea with stock returns.
How It Works
Expected Return = Risk-Free Rate + β × (Market Return - Risk-Free Rate)
The key ingredient is β (beta), a number that measures how sensitive a stock is to market movements.
A β of 1.5 means the stock typically moves 1.5× as much as the market. We calculate β using "peacetime"
(non-intervention) days, so we can isolate what's truly abnormal during wars.
Basic Stock Metrics
Adjusted Close
The stock's closing price after accounting for corporate actions like stock splits and dividends. We use this instead of raw prices because it makes returns comparable over long time periods.
Volume
The number of shares traded in a day. High volume means lots of buying and selling activity, often indicating strong investor interest or reaction to news.
Return
The percentage change in price from one day to the next. A +2% return means the stock gained 2% in value, a -3% return means it lost 3%.
Volatility
How much a stock's price swings up and down. High volatility = big price swings (risky). Low volatility = stable prices (safer). We measure this using a 20-day rolling window.
The Roller Coaster Analogy
Imagine two roller coasters: one with gentle hills and smooth turns, another with steep drops and sharp twists. Both might end at the same height, but the ride is very different. Volatility measures how wild the ride is, not where you end up, but how bumpy the journey. During wars, we expect more "wild rides" as investors react to uncertainty.Event Study Metrics
To measure the impact of military interventions, we compare what actually happened to what we expected to happen based on normal market behavior. The following metrics allow us to isolate and analyze these anomalies.
AR, Abnormal Return
The difference between a stock's actual return and the return it was expected to achieve based on its normal behavior relative to the market on a normal day (no intervention). It isolates the specific impact of an event (like a war) by removing general market trends.
The Student Grade Analogy
Imagine a student who usually scores 80% on tests. If they score 90% after studying with a new method, the "abnormal" improvement is +10%, that's the effect of the new method. Similarly, if a stock usually returns 1% when the market returns 1%, but during a war it returns 3%, the abnormal return is +2%. That extra 2% is what we attribute to the war's impact.Negative AR = Stock did worse than expected
CAR, Cumulative Abnormal Return
The sum of all abnormal returns over a period (like the 20 days after an intervention starts). This shows the total impact of an event, not just the day-to-day changes.
The Savings Account Analogy
Think of CAR like tracking your savings over a month. Each day you might save a little more or less than planned (daily AR). At month's end, you add up all those differences to see your total "abnormal" savings. A CAR of +5% means the sector gained 5% more than expected over the entire event window, like finding an extra $50 in your account after a month of small daily surprises.AV, Abnormal Volatility
Similar to abnormal return, but for volatility. It measures whether a stock became more or less volatile than expected during an event. Higher AV means more uncertainty and risk.
The Heart Rate Analogy
Your resting heart rate might be 70 bpm. During a scary movie, it jumps to 100 bpm, that extra 30 bpm is "abnormal" heart rate caused by the movie. Similarly, if a stock normally swings 2% daily but during a war it swings 5%, the abnormal volatility is +3%. It tells us how much more "nervous" the market became because of the event.CAV, Cumulative Abnormal Volatility
The sum of abnormal volatility over a period. A high CAV indicates sustained uncertainty throughout an event, while a low CAV suggests markets remained calm.
The Stress Test Analogy
Imagine tracking your stress levels during a month-long project. Some days are more stressful than expected, others less. CAV is like adding up all those "extra stress" days. A high CAV means the market was consistently more jittery than normal throughout the war, like a month where every day felt more stressful than usual. A low or negative CAV means the market handled the uncertainty surprisingly well.Understanding the 15 Sectors
Our analysis groups stocks into 15 sectors. Here's what each one contains and why it might react differently to military interventions.
Energy
Oil, gas, coal and renewable energy companies.
Examples: ExxonMobil, Chevron, ConocoPhillipsTechnology
Software, hardware, semiconductors and IT services.
Examples: Apple, Microsoft, NVIDIAHealth Care
Pharmaceuticals, biotech, hospitals and medical devices.
Examples: Johnson & Johnson, Pfizer, UnitedHealthFinancial Services
Banks, insurance, investment firms and credit services.
Examples: JPMorgan, Berkshire Hathaway, VisaConsumer Cyclical
Retail, autos, hotels, restaurants, things people buy when times are good.
Examples: Amazon, Tesla, Home DepotConsumer Defensive
Essential goods people buy regardless of economy, food, beverages, household products.
Examples: Procter & Gamble, Coca-Cola, WalmartConsumer Staples
Food, beverages, tobacco and household essentials, stable demand sectors.
Examples: Costco, PepsiCo, Colgate-PalmoliveIndustrials
Aerospace, defense, machinery, construction and transportation.
Examples: Boeing, Lockheed Martin, CaterpillarBasic Materials
Mining, chemicals, metals and paper, raw materials for everything.
Examples: Dow, Freeport-McMoRan, NucorCommunication Services
Media, entertainment, social platforms and interactive services.
Examples: Google (Alphabet), Meta, DisneyUtilities
Electric, gas and water utilities, essential services with stable demand.
Examples: Duke Energy, Southern Company, NextEraReal Estate
REITs, property management and real estate development.
Examples: American Tower, Prologis, Simon PropertyTelecommunications
Telecom carriers, wireless services and network infrastructure.
Examples: AT&T, Verizon, T-MobileConsumer Discretionary
Non-essential goods and services, luxury items, entertainment, travel.
Examples: Nike, Starbucks, Booking HoldingsMiscellaneous
Diversified companies and conglomerates that span multiple industries.
Examples: Various holding companies and diversified firmsWhy Sectors Matter for War Analysis
Different sectors react differently to military interventions. Energy might seem most vulnerable to Middle East conflicts, but our data reveals surprises. Industrials (including defense contractors) often benefit from increased military spending. Consumer Defensive sectors are typically stable during crises, but showed unexpected volatility. Understanding these 15 sectors helps explain why our hypotheses were sometimes confirmed and sometimes challenged.
Data & Methods
Datasets
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Military Intervention Project (MIP)
376 U.S. military interventions from 1776 to 2017 (Fletcher Center for Strategic Studies), including start/end dates, objectives, target countries, hostility indicators and fatalities. -
NASDAQ Stock Market Data (Kaggle)
Daily OHLCV prices (Open, High, Low, Close, Adjusted Close, Volume) for 5,324 stocks, covering 1962-2020. -
US Stock Metrics & Company Info (Kaggle)
Sector and industry classifications merged from multiple sources (NASDAQ screener, US stock metrics dataset) to build a comprehensive stock universe. -
Symbols Metadata
Security names and exchange information for ticker symbol mapping and validation.
Methodology
- Data Loading & Integration
Load and merge stock market data with sector/industry classifications - Data Cleaning & Outlier Handling
Remove ETFs, handle missing values, standardize formats and filter suspicious returns - Volume-Weighted Sector Aggregation
Aggregate stock-level returns and volatility to sector level using volume weights - MIP Data Processing
Clean intervention records, standardize dates and map objectives to canonical categories - PCA Intensity Index
Combine duration, hostility and fatalities into a single intensity score (0-1) - Event Window Construction
Define pre/during/post windows around each intervention - Overlap Resolution
Keep non-overlapping interventions by prioritizing the most intense - Event Panel Merge
Link daily sector data with intervention phases for causal analysis - CAPM-Based Abnormal Returns
Estimate expected returns from normal days to isolate war-driven effects
Methodology Deep Dive
Explore Our Data
Interactive visualizations of our stock market and military intervention datasets
Stock Market Universe
Our dataset includes 5,053 U.S. stocks spanning 15 economic sectors and 278 industries. Here's how they're distributed:
Sector → Industry Hierarchy
Click on a sector to explore its industries. Size represents number of stocks.
Hypothesis: Size ≠ Sensitivity
The treemap reveals a striking asymmetry between sector size and geopolitical sensitivity. Financial Services dominates our universe at 25.6% (1,293 stocks), followed by Health Care (16.6%) and Industrials (11.2%). Yet our central hypothesis posits that Energy, representing only 5.2% of stocks, may exhibit the most pronounced market reactions to military interventions.
This counterintuitive relationship stems from the fundamental economics of military operations: Interventions frequently target oil-producing regions (Middle East, North Africa, Venezuela), disrupt global supply chains and trigger commodity price shocks. The 1973 Yom Kippur War and subsequent OPEC embargo, the 1990 Gulf War's impact on Kuwaiti oil fields and the 2003 Iraq invasion all demonstrate how geopolitical events in energy-rich regions propagate through petroleum markets to affect sector returns.
Sector Return Correlations
Pearson correlation of daily returns between sectors. Blue = positive correlation, Red = negative.
Correlation Structure & Identification Strategy
The correlation matrix reveals a sparse, low correlation structure across most sector pairs. This is precisely what we need for causal identification, if sectors moved in lockstep, we couldn't distinguish war-specific effects from general market noise.
Strongest Correlations
- Health Care ↔ Communication Services: ρ = 0.30
- Real Estate ↔ Industrials: ρ = 0.31
- Consumer Discretionary ↔ Communication Services: ρ = 0.28
- Technology ↔ Real Estate: ρ = 0.27
Even the strongest correlations are moderate, indicating good sector independence.
Most Independent Sectors
- Basic Materials: Near-zero with all sectors (ρ ≈ 0.02-0.04)
- Consumer Defensive: Low correlation across the board (ρ ≈ 0.08-0.19)
- Energy: Largely independent (ρ ≈ 0.09-0.28)
- Miscellaneous: Minimal co-movement with most sectors
These sectors act as natural hedges during geopolitical shocks.
Why this matters for our analysis: The low inter-sector correlations (median ρ ≈ 0.15-0.20) mean that when we observe abnormal returns in Energy during a Middle East intervention, we can be confident this reflects sector-specific exposure to oil supply risks, not a spurious correlation with broader market movements.
Methodological Implication: Our CAPM-based abnormal return framework estimates each sector's β (market sensitivity) using only "peacetime" days. The low residual correlations confirm that after removing the common market factor, sector returns are approximately independent, satisfying a key assumption for valid event study inference.
Military Interventions Dataset
29 non-overlapping U.S. military interventions used in our causal analysis (filtered from 376 in the MIP dataset):
Intervention Objectives
Intensity Distribution
Strategic Objectives Analysis
The distribution reveals that "Maintain/Build Regime" dominates with 16 interventions (55%), reflecting Cold War-era containment policies and post-9/11 nation-building efforts. "Protect Military/Diplomatic Interests" accounts for 8 interventions (28%), typically shorter operations to secure U.S. assets or personnel.
Hypothesis: We expect "Remove Regime" interventions (3 cases) to produce the largest market impacts, since these operations involve maximum military commitment, prolonged uncertainty and fundamental geopolitical restructuring (e.g., Iraq 2003, Panama 1989).
Intensity Distribution Analysis
The histogram exhibits a pronounced right-skew (median = 0.289), indicating that the majority of U.S. military interventions were low-intensity operations. Over 75% of interventions fall below intensity 0.4, representing limited engagements such as shows of force, evacuations and peacekeeping missions.
The long right tail captures high-intensity conflicts: Vietnam (0.91), Afghanistan (1.0) and the Lebanese Civil War (0.73). These outliers, though rare, are hypothesized to drive the most significant and persistent market effects.
Geographic Distribution of Interventions
Countries targeted by U.S. military interventions. Red indicates high intervention count, blue indicates low. Click on a country to see intervention details.
Geographic Hotspots & the Oil Connection
The globe reveals clear geographic clustering: Asia (Vietnam, Korea, Afghanistan), the Middle East (Iraq, Syria, Lebanon, Iran), Latin America (Nicaragua, Panama, Haiti) and Africa (Libya, Somalia, Egypt). Many of these regions sit atop critical oil reserves or control strategic chokepoints (Strait of Hormuz, Suez Canal).
The Energy Hypothesis: We expect interventions in oil-producing regions to generate outsized Energy sector reactions. Historical precedent supports this: The 1973 Yom Kippur War triggered the OPEC embargo and a 300% oil price surge, Operation Desert Storm (1990) saw crude spike 130% on Kuwait invasion fears.
Explore the Globe: Click on any country to see which interventions occurred there, their intensity scores and strategic objectives.
Key Findings Preview
Overall Market Impact
On average, aggregate stock prices fall by approximately 7% following military interventions, indicating a broad risk-off reaction in the market. The Vietnam War alone produced a cumulative market impact of -91% over its 8-year duration.
Energy Sector: Small but Mighty
Despite representing only 5.2% of stocks, the Energy sector exhibits the most extreme and variable responses. During the Gulf of Sidra Incident, Energy showed +8.6% abnormal returns; during the Contra Affair, it dropped -69%. This volatility reflects direct exposure to oil supply disruptions in intervention regions.
Sector Heterogeneity
Responses vary dramatically by sector. Consumer Cyclical consistently underperforms (mean AR: -7.2%), while Utilities shows defensive characteristics with near-zero average abnormal returns. Financial Services, the largest sector, exhibits moderate sensitivity with high variance across interventions.
Market Anticipation
Trading volume is elevated before intervention dates, suggesting markets anticipate military actions through geopolitical tensions. Our event study shows abnormal volume already elevated at t=-10, with no sharp spike at t=0, indicating interventions are largely priced in.
Intensity Matters
Our PCA-based intensity index reveals a clear relationship: higher-intensity interventions produce larger absolute market impacts. The Vietnam War (intensity: 0.91) and War in Afghanistan (intensity: 1.0) show the most pronounced effects, while low-intensity operations like border skirmishes produce minimal market disruption.
Persistent Effects
Abnormal trading volume remains above baseline after interventions end, reflecting sustained portfolio adjustments. Effects persist for 20+ trading days post-intervention, with gradual mean reversion rather than immediate normalization.