Specialist track, course code PM

Portfolio Management

From mean-variance theory to optimisers that survive estimation error: how quantitative portfolios are constructed and rebalanced.

A portfolio is more than a list of good ideas. The way positions are weighted, constrained and rebalanced often matters more to the result than the ideas themselves, and the mathematics of combining risky assets is the oldest part of quantitative finance. Portfolio management, done quantitatively, is the study of those choices.

What does the portfolio management track cover?

Modern Portfolio Theory covers diversification, the efficient frontier and the tangency portfolio. Portfolio Optimization moves to practice: covariance estimation, shrinkage, constraints, turnover penalties and risk budgeting. The Black-Litterman Model, at Researcher level, covers the Bayesian method for combining market equilibrium with an investor’s views.

Where does this track lead?

To the Certificate in Quantitative Portfolio Management, which adds factor investing, performance measurement and Value at Risk and ends in a capstone portfolio built, rebalanced and reported out of sample.

Foundation courses in this track

Short courses that fix the mathematics, statistics and code every later course assumes.

Modern Portfolio Theory Markowitz's framework from first principles: expected return and covariance, the benefit of diversification, the efficient frontier, the minimum variance and tangency portfolios, and the capital market line, all computed in Python. Foundation16 study hours$290

Practitioner courses in this track

Lab-based courses. You build, test and cost real strategies and systems on market data.

Portfolio Optimization Portfolio construction that survives real data: estimating covariance with shrinkage, constrained and robust optimisation, risk budgeting and risk parity, hierarchical risk parity, and the costs of rebalancing. Practitioner30 study hours$790

Researcher courses in this track

Proof-level courses. Stochastic models, estimation theory and the literature behind each method.

The Black-Litterman Model The Black-Litterman model in full: reverse optimisation to implied equilibrium returns, expressing views, the Bayesian combination and its derivation, the choice of tau and the view uncertainty matrix, and extensions to views on factors. Researcher20 study hours$790

Credentials that use this track

Questions applicants ask

Why do mean-variance optimisers give extreme weights?

Because they treat estimated returns and covariances as exact. Small errors in expected returns produce large swings in weights. Portfolio Optimization and the Black-Litterman course teach the standard remedies: shrinkage, constraints, robust optimisation and Bayesian blending of views.

Is this track for discretionary portfolio managers?

It suits anyone who builds or oversees portfolios. The methods are quantitative, but the outputs are weights and risk reports a discretionary manager also uses.