An Introduction to Machine Learning in Quantitative Finance
The initial part of the book introduces ML more broadly with the financial applications appearing as the book develops, and the foundational models are introduced and discussed mathematically while the more advanced ones are only briefly presented.
Abstract
The book ‘An Introduction to Machine Learning in Quantitative Finance’ (Ni et al. 2021) by Ni, Dong, Zheng and Yu targets the popular field of machine learning (ML) in finance. This topic has been gaining increasing attention in the mathematical finance research community, among quantitative finance practitioners and in graduate studies. There are presently many books in ML but not many specific to finance. This book is not designed to be a reference book but an entry point to ML. Broadly speaking ML algorithms are commonly divided into categories (according to purpose) with the main ones being supervised learning, unsupervised learning and reinforcement learning (there are more). This book’s main content is supervised learning tools (Chs 2– 5) with two chapters on unsupervised learning (Chs 6–7) and a topical one on reinforcement learning (Ch. 8). The final chapter (Ch. 9) is a beginning-to-end run-through of a case study stemming from a Kaggle†competition on a financial problem. It covers all stages of the problem from data pre-processing, feature generation, model training, tuning and selection, to the final prediction. The initial part of the book introduces ML more broadly with the financial applications appearing as the book develops. The foundational models are introduced and discussed mathematically while the more advanced ones are only briefly presented. The approach gives the reader a solid basis and broad view of available techniques without overwhelming them with mathematical rigor. Methods presented in the first five chapters are summarized by tables providing a pro/con discussion. It is unfortunate that this systematic approach is lost past Chapter 5. Overall the proofs presented rely on calculus, statistics and probability, and not necessarily on the stochastic analysis needed for traditional mathematical finance. This means that an MSc student starting a financial program could pick up this book on their first day. The book has an accompanying GitHub repository with commented code that can be downloaded and tested by the reader. The approach to code is not the usual ‘informatics’