This repository contains a portfolio analysis exercise developed within the course Wealth Management: Investment Risk Management and Optimization in R taught by Professor Thomas Kurnicki at Hult International Business School in San Francisco during the 2023/2024 academic year.

⚠️⚠️ IMPORTANT DISCLAIMER: MY CODING CONTRIBUTION IS LIMITED TO THE FIRST PART OF THE PROJECT, COVERING RETURNS, RISK, AND PERFORMANCE ANALYSIS. THE SECTIONS ON FAMA-FRENCH MODELS, EFFICIENT FRONTIER, AND THE OPTIMIZATION TOOL RELIED ON CODE PROVIDED AS COURSE MATERIAL. I USED THESE TOOLS TO GENERATE THE RELEVANT OUTPUTS AND THEN FOCUSED ON INTERPRETING THE RESULTS TO COMPLETE THE ANALYSIS.

📌 Overview

The analysis focuses on a portfolio including:

IXN – iShares Global Tech ETF

QQQ – NASDAQ 100 ETF

IEF – iShares 7–10 Year Treasury Bond ETF

VNQ – Vanguard Real Estate ETF

GLD – SPDR Gold Shares ETF

📈 Key Analyses

Return Analysis (12, 18, 24 months)

Risk Analysis (σ, Tracking Error, Correlation, Betas with CAPM)

Performance Ratios (Sharpe, Sortino, Treynor)

Asset allocation suggestions

Final optimized portfolio

🛠 Models & Tools

The Fama & French Model, Efficient Frontier, and Optimization Tool were provided as course material.

🚀 Results

Original Portfolio: Sharpe Ratio 1.22 – Annualized Return 14.6%

Optimized Portfolio (IXN, QQQ, GLD): Sharpe Ratio 2.58 – Annualized Return 30.8%

Gold (GLD) confirmed as an effective diversifier (remember this is a 2024 project).