Two complementary optimization exercises in R: quantitative portfolio optimization using real hedge fund return data, and linear programming applied to a resource allocation problem.
Minimum variance and efficient frontier construction using the EDHEC hedge fund index returns — a real-world dataset spanning Convertible Arbitrage (CA), CTA Global (CTAG), Distressed Securities (DS), and Emerging Markets (EM) strategies.
Techniques:
- Unconstrained minimum variance portfolio (via quadratic programming)
- Constrained optimization (fully invested, long-only)
- Efficient frontier visualization
- Risk-return tradeoff analysis across hedge fund strategies
A foundational linear programming problem: optimizing crop allocation (Parsnips vs Kale) across land and budget constraints to maximize profit. Step-by-step formulation from decision variables to interpreted solution.
Techniques:
- Decision variable definition
- Objective function formulation
- Constraint identification and inequality setup
- Feasible region visualization
- Solver implementation and solution interpretation
R tidyverse tidyquant tbl2xts broom gt lpSolve Quarto
Virginia Commonwealth University · MS Business (Financial Analytics) · FIRE 540 Instructor: Prof. Larry Tentor