Specialist track, course code PM

Portfolio Management

From mean-variance theory to optimizers 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 and the CAPM covers diversification, the efficient frontier, the tangency portfolio and beta. Portfolio Optimization, Risk Parity and Black-Litterman moves to practice: covariance estimation, shrinkage, constraints and turnover penalties; the strategic mix of asset classes and the risk parity portfolios that pension funds run; and 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

The mathematics, statistics, data and market knowledge every later course assumes.

Modern Portfolio Theory and the CAPM 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. It also teaches the capital asset pricing model. Foundation8 hours of material$360

Practitioner courses in this track

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

Portfolio Optimization, Risk Parity and Black-Litterman Portfolio construction that survives real data: estimating covariance with shrinkage, constrained and robust optimization, risk budgeting and risk parity, hierarchical risk parity, and the costs of rebalancing. It also teaches asset allocation and risk parity and the Black-Litterman model. Practitioner12 hours of material$720

Credentials that use this track

Questions applicants ask

Why do mean-variance optimizers give extreme weights?

Because they treat estimated returns and covariances as exact. Small errors in expected returns produce large swings in weights. Portfolio Optimization, Risk Parity and Black-Litterman teaches the standard remedies: shrinkage, constraints, robust optimization 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.