Equity Machine Factor Models

Miquel Noguer i Alonso, Vincent Zoonekynd

Abstract:

We examine in this paper the training and test set performance of
several equity factor models with a dataset of 20 years of data, 1,200
stocks and 100 factors.
First, we examine several models to forecast expected returns, which
can be used as baselines for more complex models: linear regression,
linear regression with an L1 penalty (lasso), constrained linear regression,
xgboost and artificial neural networks.
Second, we present a unified framework for portfolio construction,
leveraging machine learning for the whole pipeline, from the factor
data to the portfolio weights, which scales to a large number of
assets and predictors. The results we obtain are interesting and non
trivial to interpret; non linear models models offer a more balanced
outcome considering test set Sharpe ratio and turnover but linear
unconstrained models show a good performance in the test set. We
introduce a model-free reinforcement learning model, which uses factors
to find the portfolio weights maximizing the information ratio.