Credential program on the practitioner path
Certificate in Quantitative Portfolio Management
Six courses and a capstone on building portfolios with numbers: mean-variance and its failures, robust optimisation, Black-Litterman views, factors, attribution and risk.
Mean-variance optimisation is easy to write down and hard to use. Feed it estimated returns and it builds extreme, unstable portfolios that fall apart out of sample. Quantitative portfolio management is the craft of making optimisation useful: better inputs, sensible constraints, views expressed with honest uncertainty, and measurement that tells you whether the process added anything.
What does the certificate cover?
Modern portfolio theory and its limits; optimisation with constraints, shrinkage and robust methods; the Black-Litterman model; factor exposures and risk budgeting; performance measurement and attribution; and value at risk for portfolio oversight.
Who is it for?
Portfolio managers and analysts, wealth and pension professionals who want quantitative methods, and engineers joining asset management firms.
The courses in this program
- 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.
- 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.
- 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.
- Factor Investing How equity factors are defined, built and tested: value, momentum, size, quality and low volatility, the construction of long-short factor portfolios, combining factors, and the multiple-testing problem that hides behind hundreds of published factors.
- Performance Measurement for Trading Strategies The statistics used to judge trading strategies and managers: the Sharpe ratio, the Sortino, Calmar and information ratios, drawdown, attribution, and the uncertainty in every one of them.
- Value at Risk How value at risk is calculated, tested and misused: historical simulation, variance-covariance and Monte Carlo methods, volatility-weighted variants, time scaling, regulatory backtesting, and what VaR does not tell you.
What should you know before you start?
The courses in this program build on the courses below. They are not part of the program and not included in its price. If you have not studied this material, take them first. Admissions can tell you whether your background already covers them: [email protected].
- Python for Finance A Python for finance course with the Python every later course assumes: loading and cleaning market data with pandas, computing returns and risk with NumPy, plotting, and writing code another person can rerun.
- Probability for Quantitative Finance The probability every quant course assumes: distributions, expectation, conditioning and the limit theorems, with each idea tested on real return data.
- Statistics for Finance and Trading Estimation, testing and regression for financial data, with the traps that make trading research go wrong: dependence, fat tails and testing too many ideas.
- The Capital Asset Pricing Model The model that defines beta and the idea of a risk premium: the CAPM's assumptions and formula, estimating beta from data, using the model for cost of capital and performance, and the empirical tests that led to factor models.
What is the portfolio management capstone?
You build and run a multi-asset portfolio process: estimate inputs, express views with Black-Litterman, optimise under realistic constraints, rebalance with costs and report performance and risk to an investment committee. The report is judged on whether a committee could approve or reject the process from it.
Is this credential accredited?
No. It is an unaccredited Institute credential. It records that you passed every assessed component listed on this page, marked by the Institute's faculty. It is not a university degree and is not issued by Carnegie Mellon University. Each credential carries a verification code that an employer can check with [email protected].
Questions applicants ask
Is this a CFA preparation course?
No. It overlaps with parts of the CFA curriculum on portfolio theory, but it is a practical, code-based program and does not prepare for any exam.
Which tools are used?
Python with NumPy, pandas and open-source optimisation libraries. No commercial software is required.
How long does the certificate take?
150 hours of study. At eight hours a week, about five months.