Full Product Details
Author: Guillaume Coqueret ,
Tony Guida
Publisher: Taylor & Francis Ltd
Imprint: Chapman & Hall/CRC
Weight: 0.839kg
ISBN: 9780367545864
ISBN 10: 0367545861
Pages: 342
Publication Date: 01 September 2020
Audience:
College/higher education
,
Professional and scholarly
,
Tertiary & Higher Education
,
Professional & Vocational
Format: Paperback
Publisher's Status: Active
Availability: In Print
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Reviews
This book is the perfect one for any data scientist on financial markets. It is well written, with lots of illustrations, examples, pieces of code, tips on the different statistical package available to perform the various algos. This book requires for sure a strong knowledge in quantitative finance and Machine Learning, so it cannot be put in any hands. But for those who are familiar with quantitative finance, this book can be a reference, as Hull's book is as regards to derivatives products. I liked the good and detailed analysis of the different Machine Learning algos, and the different examples used throughout the book. This book is perfect for assets managers having to run backtests and searching for innovative ways to enhance the return of their portfolios. I spent quite a good time reading this manuscript, and I would recommend it. (Frederic Girod, Union of European Football Associations) This book is the perfect one for any data scientist on financial markets. It is well written, with lots of illustrations, examples, pieces of code, tips on the different statistical package available to perform the various algos. This book requires for sure a strong knowledge in quantitative finance and Machine Learning, so it cannot be put in any hands. But for those who are familiar with quantitative finance, this book can be a reference, as Hull's book is as regards to derivatives products. I liked the good and detailed analysis of the different Machine Learning algos, and the different examples used throughout the book. This book is perfect for assets managers having to run backtests and searching for innovative ways to enhance the return of their portfolios. I spent quite a good time reading this manuscript, and I would recommend it. -Frederic Girod, Union of European Football Associations
This book is the perfect one for any data scientist on financial markets. It is well written, with lots of illustrations, examples, pieces of code, tips on the different statistical package available to perform the various algos. This book requires for sure a strong knowledge in quantitative finance and Machine Learning, so it cannot be put in any hands. But for those who are familiar with quantitative finance, this book can be a reference, as Hull's book is as regards to derivatives products. I liked the good and detailed analysis of the different Machine Learning algos, and the different examples used throughout the book. This book is perfect for assets managers having to run backtests and searching for innovative ways to enhance the return of their portfolios. I spent quite a good time reading this manuscript, and I would recommend it. (Frederic Girod, Union of European Football Associations)
Author Information
Guillaume Coqueret is associate professor of finance and data science at EMLYON Business School. His recent research revolves around applications of machine learning tools in financial economics. Tony Guida is executive director at RAM Active Investments. He serves as chair of the machineByte think tank and is the author of Big Data and Machine Learning in Quantitative Investment.
