Machine learning (ML) is progressively reshaping the fields of
quantitative finance and algorithmic trading. ML tools are increasingly
adopted by hedge funds and asset managers, notably for alpha signal
generation and stocks selection. The technicality of the subject can
make it hard for non-specialists to join the bandwagon, as the jargon
and coding requirements may seem out of reach. Machine Learning for
Factor Investing: R Version bridges this gap. It provides a
comprehensive tour of modern ML-based investment strategies that rely on
firm characteristics.
The book covers a wide array of subjects which range from economic
rationales to rigorous portfolio back-testing and encompass both data
processing and model interpretability. Common supervised learning
algorithms such as tree models and neural networks are explained in the
context of style investing and the reader can also dig into more complex
techniques like autoencoder asset returns, Bayesian additive trees, and
causal models.
All topics are illustrated with self-contained R code samples and
snippets that are applied to a large public dataset that contains over
90 predictors. The material, along with the content of the book, is
available online so that readers can reproduce and enhance the examples
at their convenience. If you have even a basic knowledge of quantitative
finance, this combination of theoretical concepts and practical
illustrations will help you learn quickly and deepen your financial and
technical expertise.
