📊 Monte Carlo Financial Simulation

📌 Business Context

Financial decisions are made under uncertainty. This project applies Monte Carlo simulation — a powerful quantitative finance technique — to model portfolio risk, revenue scenarios and investment decisions across 10,000 simulated outcomes, enabling data driven risk aware decision making.

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🎯 Project Objectives

• Simulate stock portfolio performance under uncertainty
• Calculate Value at Risk (VaR) at 95% and 99% confidence levels
• Model 3 year revenue scenarios with probability distributions
• Compare risk vs return across investment strategies
• Quantify probability of financial outcomes

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📊 Simulation Parameters

Parameter	Value
Simulations Run	10,000 iterations
Portfolio Stocks	3 assets
Trading Days	252 (1 year)
Initial Investment	$100,000
Base Revenue	$5,000,000
Forecast Horizon	3 years

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📈 Simulation Results

💼 Portfolio Simulation

Metric	Value
Average Portfolio Value	$112,375.52
Median Portfolio Value	$111,539.76
Standard Deviation	$13,389.81
Best Case Outcome	+$87,714.10
Worst Case Outcome	-$26,803.41

⚠️ Value at Risk (VaR)

Confidence Level	Maximum Loss
VaR 95%	-$7,874.24
VaR 99%	-$14,429.20

📊 3 Year Revenue Scenarios

Scenario	Revenue
Base Revenue	$5,000,000
Average Forecast	$6,655,143
Optimistic (95th)	$8,090,967
Pessimistic (5th)	$5,323,249
Probability of Growth	98.3%

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🔬 Methodology

1️⃣ Portfolio Simulation

• Modeled 3 stocks with different risk/return profiles
• Simulated 252 daily returns using normal distribution
• Applied geometric Brownian motion for price paths
• Aggregated 10,000 portfolio outcomes

2️⃣ Value at Risk (VaR)

• Calculated VaR at 95% and 99% confidence levels
• VaR represents maximum expected loss under normal conditions
• Used historical simulation approach with Monte Carlo

3️⃣ Revenue Scenario Analysis

• Simulated 3 year revenue growth under uncertainty
• Applied random normal distribution to annual growth rates
• Identified optimistic, pessimistic and base case scenarios

4️⃣ Investment Decision Analysis

• Compared Growth vs Conservative investment strategies
• Quantified risk adjusted returns for each option
• Calculated probability of positive returns

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🔍 Key Insights

💰 Portfolio Performance

• Average portfolio grows from $100,000 to $112,375 (+12.4%)
• 95% confidence maximum loss limited to $7,874
• 99% confidence maximum loss limited to $14,429
• Strong positive skew indicates more upside than downside

📊 Revenue Outlook

• 98.3% probability of revenue growth over 3 years
• Average 3 year revenue of $6.66M vs base of $5M (+33%)
• Optimistic scenario reaches $8.09M (+62% growth)
• Even pessimistic scenario shows modest growth to $5.32M

⚖️ Risk vs Return

• Portfolio standard deviation of $13,390 shows manageable risk
• Growth investment offers higher return with proportionally higher risk
• Conservative investment provides more predictable outcomes
• Diversification across 3 assets reduces overall portfolio volatility

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💡 Business Recommendations

• Proceed with growth strategy — 98.3% probability of revenue increase
• Set risk limits using VaR — maximum acceptable loss at 99% = $14,429
• Plan for pessimistic scenario — budget should account for $5.32M floor
• Diversify portfolio — 3 asset mix reduces concentration risk
• Review assumptions quarterly — update volatility inputs with market data

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🛠️ Tools & Technologies

Tool	Usage
Python	Simulation engine and analysis
NumPy	Random number generation and statistics
pandas	Results aggregation and export
matplotlib	Distribution and scenario visualizations

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📂 Repository Structure

monte-carlo-financial-simulation/
│
├── data/
│   └── simulation_results.csv    # Key simulation outputs
├── python/
│   └── monte_carlo_simulation.ipynb  # Full simulation notebook
├── reports/
│   ├── portfolio_distribution.png    # Portfolio outcome distribution
│   ├── revenue_scenarios.png         # Revenue scenario chart
│   └── investment_comparison.png     # Investment comparison chart
└── README.md

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🚀 Outcome

This project demonstrates quantitative finance expertise combined with statistical simulation skills, delivering risk insights suitable for Quantitative Analyst, Risk Analyst, FP&A and Data Science roles.

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All simulations use synthetic financial parameters for demonstration purposes.

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