# Supervised learning for factor investing

This page contains teaching material (**R** code) for
**introductory** courses on supervised learning applied to
factor investing.

## Sessions

The links below lead to **html** notebooks and
**pdf** slides. The original **Rmd** files can
be downloaded hereafter.

**Economic foundations**: asset pricing anomalies,
characteristics-based investing;

**html
notebook** -
**slides**

**Portfolio strategies**: portfolio back-testing
framework;

**html
notebook** -
**slides**

**Penalized regressions & sparse portfolios**:
penalised regressions for minimumn variance portfolios and for robust
forecasts;

**html
notebook** -
**slides**

**Data preparation**: Feature engineering and
labelling with a focus on categorical data;

**html
notebook** -
**slides**

**Decision trees**: Simple trees, random forests and
boosted trees;

**html
notebook** -
**slides**

**Neural networks**: Multilayer perceptron and
recurrent networks (**G**ated **R**ecurrent
**U**nits);

**html
notebook** -
**slides**

**Validating & tuning**: Performance metrics and
hyper-parameter adjustment;

**html
notebook** -
**slides**

**Extensions**: SVMs, ensemble learning,
interpretability and deflated Sharpe ratios;

**html
notebook** -
**slides**

## Material

Datasets (in R format - they end in February 2021):

**Base**
(small): 30 firms, 7 features, year 2000 onwards;

**Large**:
~900 firms, 10 features (including GHG emissions from 2011 on), year
1995 onwards;

**DISCLAIMER**: the **data** and
**code** are meant for pedagogical use only.