Advances in Financial Machine Learning
Language: English
Published by Wiley, 2018
- First Edition
- Hardcover
- Used
Seller: Riverside Books and Prints, Cold Spring, NY, U.S.A.Riverside Books and Prints
AbeBooks seller since December 8, 2008
Condition: Used - As new
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Add to basketItem description from seller
Condition: as new.Dust jacket as new. Hardcover. Learn to understand and implement the latest machine learning innovations to improve your investment performance Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that until recently only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest. In the book, readers will learn how to: Structure big data in a way that is amenable to ML algorithms Conduct research with ML algorithms on big dataUse supercomputing methods and back test their discoveries while avoiding false positives Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting. Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance. Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is the most exciting time to adopt a disruptive technology that will transform how everyone invests for generations.…
Seller Inventory # 007364
- Title
- Advances in Financial Machine Learning
- Author
- Lopez De Prado, Marcos
- Publisher
- Wiley
- Publication year
- 2018
- Condition
- As New
- Dust jacket
- As New
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 1119482089
- ISBN 13
- 9781119482086
- Edition
- 1st Edition
- Printing
- 1st Printing
Learn to understand and implement the latest machine learning innovations to improve your investment performance
Machine learning (ML) is changing virtually every aspect of our lives. Today, ML algorithms accomplish tasks that – until recently – only expert humans could perform. And finance is ripe for disruptive innovations that will transform how the following generations understand money and invest.
In the book, readers will learn how to:
- Structure big data in a way that is amenable to ML algorithms
- Conduct research with ML algorithms on big data
- Use supercomputing methods and back test their discoveries while avoiding false positives
Advances in Financial Machine Learning addresses real life problems faced by practitioners every day, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their individual setting.
Written by a recognized expert and portfolio manager, this book will equip investment professionals with the groundbreaking tools needed to succeed in modern finance.
"Synopsis" may belong to another edition of this title.
About the Author
DR. MARCOS LÓPEZ DE PRADO is a principal at AQR Capital Management, and its head of machine learning. Marcos is also a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). SSRN ranks him as one of the most-read authors in economics, and he has published dozens of scientific articles on machine learning and supercomputing in the leading academic journals. Marcos earned a PhD in financial economics (2003), a second PhD in mathematical finance (2011) from Universidad Complutense de Madrid, and is a recipient of Spain's National Award for Academic Excellence (1999). He completed his post-doctoral research at Harvard University and Cornell University, where he teaches a graduate course in financial machine learning at the School of Engineering. Marcos has an Erdös #2 and an Einstein #4 according to the American Mathematical Society.
"About the title" may belong to another edition of this title.
Riverside Books and Prints
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