Название: Machine Learning for Factor Investing: R VersionАвтор: Guillaume Coqueret, Tony Guida
Издательство: Chapman and Hall/CRC
Год: 2021
Страниц: 342
Язык: английский
Формат: pdf (true)
Размер: 31,8 MB
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. We provide hands-on R code samples that show how to apply the concepts and tools on a realistic dataset which we share to encourage reproducibility. 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.
The book can serve scholars or researchers who need a manual with a broad spectrum of references both on recent asset pricing issues and on machine learning algorithms applied to money management. While the book covers mostly common methods, it also shows how to implement more exotic models, like causal graphs, Bayesian additive trees, and hybrid autoencoders. The book assumes basic knowledge in algebra (matrix manipulation), analysis (function differentiation, gradients), optimization (first and second order conditions, dual forms), and statistics (distributions, moments, tests, simple estimation method like maximum likeli hood). A minimal financial culture is also required: simple notions like stocks, accounting quantities (e.g., book value) will not be defined in this book. Lastly, all examples and illustrations are coded in R. A minimal culture of the language is sufficient to understand the code snippets which rely heavily on the most common functions of the tidyverse, and piping.
Why R? The supremacy of Python as the dominant ML programming language is a widespread belief. This is because almost all applications of deep learning (which is as of 2020 one of the most fashionable branches of ML) are coded in Python via Tensorflow or Pytorch. The fact is that R has a lot to offer as well. First of all, let us not forget that one of the most influencial textbooks in ML is written by statisticians who code in R. Moreover, many statistics-orientated algorithms (e.g., BARTs in Section 9.5) are primarily coded in R and not always in Python. The R offering in Bayesian packages in general and in Bayesian learning in particular is probably unmatched.
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