Ruy M. Ribeiro

Teaching

Quantitative Finance

A course in portfolio construction

UndergraduateInsperTaught in English

A quantitative portfolio-construction course in 14 sessions: return and risk measurement, portfolio construction, and full strategy backtesting, with worked Python examples throughout. Students build portfolios, estimate factor models, hunt for alpha, and run a semester-long Fund Project under real backtesting discipline.

Topics

  • Mean-variance optimization, rebalancing, and estimation error
  • Covariance and factor models
  • Momentum, reversals, and the investment horizon
  • CAPM, multifactor models, APT, and pricing tests
  • Characteristics versus covariances; finding alpha
  • The Black-Litterman model
  • Event studies and strategy examples (betting against beta, quality minus junk, macro momentum)
  • Backtesting and evaluation

Audience

Advanced undergraduate students.

Materials

A complete undergraduate textbook (~270 pp., 14 chapters with exercises and a full answer key), 14 lecture decks, four team projects plus the Fund Project.

Chapters map one-to-one onto sessions, with worked Python examples throughout and end-of-chapter exercises with a full answer key. Graded work is built around four team projects (portfolios, the frontier, factor covariance, momentum and Black-Litterman) plus a semester-long Fund Project with its own rules and deadline calendar.

Book Preface and Table of Contents Show

Quantitative Finance: An Advanced Undergraduate Introduction to Portfolio Construction — the course textbook.

From the preface. This book is an advanced undergraduate introduction to quantitative finance, built around one practical question: how do we turn data about asset returns into a sensible portfolio, and how do we judge whether a strategy is real? It is the companion to the graduate text Quantitative Finance: A Course in Portfolio Construction, and follows the same arc, from the mean-variance problem and why it is hard in practice, through risk modelling, return signals, asset pricing, and real systematic strategies, to evaluation.

The reader has had introductory probability and statistics (expectation, variance, covariance, regression) and basic linear algebra. Calculus is used lightly. No prior finance course is required, but the pace and depth assume a motivated student in the final years of an undergraduate degree.

The course is organized as a quantitative asset-management "firm." Students work in teams, each managing a portfolio of 30 S&P 500 names and analysing the index and common factors. Theory is introduced when a portfolio problem demands it; every idea is something you can compute in Python (numpy, pandas, cvxpy, statsmodels).

Contents

Part I. The Portfolio Problem

  1. Introduction and Statistical Foundations
  2. Mean-Variance Optimization and the Frontier
  3. Rebalancing, Diversification and Estimation Error

Part II. Risk and Factor Models

  1. Covariance and Factor Models

Part III. Return Predictability and Signals

  1. Momentum
  2. Reversals and the Investment Horizon

Part IV. Asset Pricing

  1. The Capital Asset Pricing Model
  2. Multifactor Models, APT and Pricing Tests
  3. Characteristics versus Covariances

Part V. Views, Alpha, and Events

  1. Finding Alpha and Valuation
  2. The Black-Litterman Model
  3. Event Studies

Part VI. Strategies and Synthesis

  1. Strategy Examples: Betting Against Beta, Quality, and Macro Momentum
  2. Backtesting, Evaluation, and Synthesis

Appendices: Solutions to Selected Exercises · True/False Answer Key

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