Machine Learning in Finance: From Theory to Practice

The authors cut through the hype and rebranding that litters the field of machine learning. They discuss models that are relevant to finance. Most importantly for professionals, they ground their discussion in concepts that will be familiar to statisticians and numerical analysts (quants, in other words). Some of the work presented in this book is new, particularly the sections on inverse reinforcement learning. This will be an excellent resource for a graduate course. Those students who do not have the math background will likely be motivated to get it. There are well-designed Python notebooks that present examples of the analysis. Students with the ability to work with the concepts presented in this book would be welcome in any serious quant shop.

An amazing and comprehensive presentation of many different relevant and useful concepts. The finance industry -- trading, asset management, risk management, banking, etc -- is most likely going to look much different in the not too distant future and much of this change is going to come from applications of this book's concepts. The authors also do a great job of demonstrating that these "black boxes" are actually not mysterious and overly complicated but rather fairly intuitive and implementable. If anyone has ever seen the movie "AlphaGO" and was wondering how that type of paradigm shift would apply to finance, the next step is to buy this book.

Brand new but I anticipate this will become THE textbook on the subject that many instructors will use to teach around the world. The book nicely builds up throughout the chapters. I find it great to include multiple choice questions, exercises and an extra instructor booklet available to assist in the classroom. And what a great idea to share Python code to make it all that more practical! I find the insights on inverse reinforcement learning particularly interesting.

As a first edition it is a bit less polished than I would like, but the topic coverage can't be found anywhere else. Top 1 of 1 books covering deep learning in finance. Looking forward to picking up future iterations of this book.

This product arrived in excellent condition.

This book serves as high-level overview of a bunch of important statistical, ML and econometric concepts. So if you're already familiar with statistical learning and neural nets, it's a great guidebook and it introduces some relevant finance application.

Great book Where can I find the source codes?

This book represents a very comprehensive guide to Machine Learning techniques in Finance and serves remarkably well both the students of quantitative and computational finance, as well as a large cross-section of industry’s practitioners. As someone who worked in this field for several decades, and wrote a book on this topic, I understand very well what it takes to put together a comprehensive guide on such a subject. Very rare are the books on this topic that address properly at the same time both the theoretical aspects of the problem at hand, as well as exemplifying these concepts with meaningful practical examples. A book on application of ML to Finance is not necessary about providing tons of Py codes, and this one serves the reader very well. But it should be about setting up a strong foundation for the theories and concepts that underlie the ML machinery, as well as exemplifying these concepts with model reasoning that is applicable to real world problems. And the reader is served again extremely well from this perspective. In my opinion this book introduces several novelties in a very crowded and sometimes over published field: - A very elegant and well documented exposition on financial times series modelling, especially regarding the use of RNNs and Kalman filtering techniques; - A mathematically sound and well exemplified section on the use of Reinforcement Learning, specifically promoting the use of Inverse Reinforcement Learning and Imitation Learning as modern tools for Optimal Control; - Blending the ever-powerful set of Bayesian thinking into the world of financial ML, by developing an intuition for the role of functional regularization in the classical statistical setting. I read this book and followed its Py code examples with great pleasure, and I strongly recommend it to anyone that is interested in applying these modern concepts, while having a solid understanding of the Mathematics that supports it. Enjoy it!

Traditionally finance industry uses mathematical approaches on so-called from "quantitative finance" perspective. Dixon-Halperin-Bilokon's refreshing book does not only capture specialised usage of machine learning in finance but it also serves as a machine learning reference book. They treat chapters in great substance with carefully covering basic concepts in a non-superficial manner.

The content is great but the book arrived damaged. The hard cover of the book is fold and in bad condition.

This book covers a vast number of highly relevant machine learnings topics in an accessible manner (even for non-ML experts) and illustrates their application to finance and other fields with numerous examples in the book and additional exercises or coding applications (Python) for the interested reader. The exposition throughout the books is clear and consistent with plenty of colourful illustrations to reinforce the concepts. The level of prerequisite knowledge is kept to a minimum where possible -- although having an undergraduate degree in maths, physics, statistics, or a related quantitative field will certainly help in studying this book. Being a finance professional with a quantitative background, for me this book provides a deep insight into how ML can be used across various hot topics in quant finance (e.g. algorithmic trading), but also other non-financial disciplines. Impressive work by the authors who showcase their extensive knowledge in the field -- a must buy!

Now that I've integrated bayesian modeling in my work (as in my presentation "The Right Kind of Volatility" at QuantMinds 2020), I can appreciated more how this book takes its time guiding the reader through the steps of choosing and judging models. The chapters on Reinforcement Learning are more advanced, but worth the time spent learning (I was inspired by the QLBS approach to Black and Scholes). Examples and code make it an outstanding book for those interested in learning more about financial modeling in the 20s.

I just started to read the book and I have found it to be very informative for people with interests and background in quantitative finance. Machine Learning, Artificial Intelligence and specially Reinforcement Learning is currently a focus point of research as there has been interesting breakthroughs, e.g. DeepMind's AlphaGo. Financial industry is also benefiting from the machine learning advancements, specially when non-traditional alternative data are available, e.g. sentiment-based trading or natural language processing. The book authors have extensive experience and background in quantitative finance. The book aims to presents the machine learning subject for quantitative finance professionals and graduate students in quantitative disciplines, e.g. Mathematics, Physics, Statistics. The book is divided to three parts: Machine Learning with Cross-Sectional Data, Sequential Learning, and Sequential Data with Decision-Making. Each part encompasses relevant topics presented in a few chapters where each chapters is accompanied by corresponding reference aiding interested readers to dive into the chapter's material. The book is also accompanied with a collection of Python codes to further facilitate the learning process. For readers with knowledge of option pricing, optimal hedging the reinforcement learning part of the book provides the dynamic programming approach toward relevant classical option pricing problems through reinforcement learning closely resembling the celebrated Black-Scholes-Merton model. Overall, the book is valuable resource for Quants to become acquainted with the emerging Machine Learning Applications in Finance. The book should be helpful to the whole Machine Learning, and Artificial Intelligence community, and in particular to quants community in financial industry.