Guillaume Coqueret

Teaching

Supervised learning for factor investing

Teaching material (R code) for introductory courses on supervised learning applied to factor investing.

Sessions

HTML notebooks and PDF slides for each session.

  1. Economic foundations: asset pricing anomalies, characteristics-based investing Notebook Slides
  2. Portfolio strategies: portfolio back-testing framework Notebook Slides
  3. Penalized regressions & sparse portfolios: penalised regressions for minimum variance portfolios and for robust forecasts Notebook Slides
  4. Data preparation: feature engineering and labelling with a focus on categorical data Notebook Slides
  5. Decision trees: simple trees, random forests and boosted trees Notebook Slides
  6. Neural networks: multilayer perceptron and recurrent networks (Gated Recurrent Units) Notebook Slides
  7. Validating & tuning: performance metrics and hyper-parameter adjustment Notebook Slides
  8. Extensions: SVMs, ensemble learning, interpretability and deflated Sharpe ratios Notebook Slides

Material

Datasets in R format (they end in 2025):

The data and code are meant for pedagogical use only.