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Advances in Financial Machine Learning Hardcover – February 21, 2018
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“In his new book Advances in Financial Machine Learning, noted financial scholar Marcos López de Prado strikes a well-aimed karate chop at the naive and often statistically overfit techniques that are so prevalent in the financial world today. He points out that not only are business-as-usual approaches largely impotent in today’s high-tech finance, but in many cases they are actually prone to lose money. But López de Prado does more than just expose the mathematical and statistical sins of the finance world. Instead, he offers a technically sound roadmap for finance professionals to join the wave of machine learning. What is particularly refreshing is the author's empirical approach — his focus is on real-world data analysis, not on purely theoretical methods that may look pretty on paper but which in many cases are largely ineffective in practice. The book is geared to finance professionals who are already familiar with statistical data analysis techniques, but it is well worth the effort for those who want to do real state-of-the-art work in the field.”
- Dr. David H. Bailey, former Complex Systems Lead, Lawrence Berkeley National Laboratory. Co-discoverer of the BBP spigot algorithm.
“Finance has evolved from a compendium of heuristics based on historical financial statements to a highly sophisticated scientific discipline relying on computer farms to analyze massive data streams in real time. The recent highly impressive advances in machine learning (ML) are fraught with both promise and peril when applied to modern finance. While finance offers up the non-linearities and large data sets upon which ML thrives, it also offers up noisy data and the human element which presently lie beyond the scope of standard ML techniques. To err is human but if you really want to f**k things up, use a computer. Against this background, Dr. López de Prado has written the first comprehensive book describing the application of modern ML to financial modeling. The book blends the latest technological developments in ML with critical life lessons learned from the author's decades of financial experience in leading academic and industrial institutions. I highly recommend this exciting book to both prospective students of financial ML and the professors and supervisors who teach and guide them.”
- Prof. Peter Carr, Chair of the Finance and Risk Engineering Department, NYU Tandon School of Engineering.
“Marcos is a visionary who works tirelessly to advance the finance field. His writing is comprehensive and masterfully connects the theory to the application. It is not often you find a book that can cross that divide. This book is an essential read for both practitioners and technologists working on solutions for the investment community.”
- Landon Downs, President and co-Founder, 1QBit.
“Academics who want to understand modern investment management need to read this book. In it, Marcos Lopez de Prado explains how portfolio managers use machine learning to derive, test and employ trading strategies. He does this from a very unusual combination of an academic perspective and extensive experience in industry allowing him to both explain in detail what happens in industry and to explain how it works. I suspect that some readers will find parts of the book that they do not understand or that they disagree with, but everyone interested in understanding the application of machine learning to finance will benefit from reading this book.”
- Prof. David Easley, Cornell University. Chair of the NASDAQ-OMX Economic Advisory Board.
“For many decades, finance has relied on overly simplistic statistical techniques to identify patterns in data. Machine learning promises to change that by allowing researchers to use modern non-linear and highly-dimensional techniques, similar to those used in scientific fields like DNA analysis and astrophysics. At the same time, applying those machine learning algorithms to model financial problems would be dangerous. Financial problems require very distinct machine learning solutions. Dr. López de Prado's book is the first one to characterize what makes standard machine learning tools fail when applied to the field of finance, and the first one to provide practical solutions to unique challenges faced by asset managers. Everyone who wants to understand the future of finance should read this book.”
- Prof. Frank Fabozzi, EDHEC Business School. Editor of The Journal of Portfolio Management.
“This is a welcome departure from the knowledge hoarding that plagues quantitative finance. López de Prado defines for all readers the next era of finance: industrial scale scientific research powered by machines.”
- John Fawcett, Founder and CEO, Quantopian.
“Marcos has assembled in one place an invaluable set of lessons and techniques for practitioners seeking to deploy machine learning techniques in finance. If machine learning is a new and potentially powerful weapon in the arsenal of quantitative finance, Marcos' insightful book is laden with useful advice to help keep a curious practitioner from going down any number of blind alleys, or shooting oneself in the foot.”
- Ross Garon, Head of Cubist Systematic Strategies. Managing Director, Point72 Asset Management.
“The first wave of quantitative innovation in finance was led by Markowitz optimization. Machine Learning is the second wave and it will touch every aspect of finance. López de Prado’s Advances in Financial Machine Learning is essential for readers who want to be ahead of the technology rather than being replaced by it.”
- Prof. Campbell Harvey, Duke University. Former President of the American Finance Association.
“How does one make sense of todays’ financial markets in which complex algorithms route orders, financial data is voluminous, and trading speeds are measured in nanoseconds? In this important book, Marcos López de Prado sets out a new paradigm for investment management built on machine learning. Far from being a “black box” technique, this book clearly explains the tools and process of financial machine learning. For academics and practitioners alike, this book fills an important gap in our understanding of investment management in the machine age.”
- Prof. Maureen O’Hara, Cornell University. Former President of the American Finance Association.
“Marcos López de Prado has produced an extremely timely and important book on machine learning. The author's academic and professional first-rate credentials shine through the pages of this book - indeed, I could think of few, if any, authors better suited to explaining both the theoretical and the practical aspects of this new and (for most) unfamiliar subject. Both novices and experienced professionals will find insightful ideas, and will understand how the subject can be applied in novel and useful ways. The Python code will give the novice readers a running start, and will allow them to gain quickly a hands-on appreciation of the subject. Destined to become a classic in this rapidly burgeoning field.”
- Prof. Riccardo Rebonato, EDHEC Business School. Former Global Head of Rates and FX Analytics at PIMCO.
“A tour de force on practical aspects of machine learning in finance brimming with ideas on how to employ cutting edge techniques, such as fractional differentiation and quantum computers, to gain insight and competitive advantage. A useful volume for finance and machine learning practitioners alike.”
- Dr. Collin P. Williams, Head of Research, D-Wave Systems.
From the Back Cover
Today's machine learning (ML) algorithms have conquered the major strategy games, and are routinely used to execute tasks once only possible by a limited group of experts. Over the next few years, ML algorithms will transform finance beyond anything we know today. Advances in Financial Machine Learning was written for the investment professionals and data scientists at the forefront of this evolution.
This one-of-a-kind, practical guidebook is your go-to resource of authoritative insight into using advanced ML solutions to overcome real-world investment problems. It demystifies the entire subject and unveils cutting-edge ML techniques specific to investing. With step-by-step clarity and purpose, it quickly brings you up to speed on fully proven approaches to data analysis, model research, and discovery evaluation. Then, it shines a light on the nuanced details behind innovative ways to extract informative features from financial data. To streamline implementation, it gives you valuable recipes for high-performance computing systems optimized to handle this type of financial data analysis.
Advances in Financial Machine Learning crosses the proverbial divide that separates academia and the industry. It does not advocate a theory merely because of its mathematical beauty, and it does not propose a solution just because it appears to work. The author transmits the kind of knowledge that only comes from experience, formalized in a rigorous manner.
This turnkey guide is designed to be immediately useful to the practitioner by featuring code snippets and hands-on exercises that facilitate the quick absorption and application of best practices in the real world.
Stop guessing and profit off data by:
- Tackling today's most challenging aspects of applying ML algorithms to financial strategies, including backtest overfitting
- Using improved tactics to structure financial data so it produces better outcomes with ML algorithms
- Conducting superior research with ML algorithms as well as accurately validating the solutions you discover
- Learning the tricks of the trade from one of the largest ML investment managers
Put yourself ahead of tomorrow's competition today with Advances in Financial Machine Learning.
Praise for ADVANCES in FINANCIAL MACHINE LEARNING
"Dr. López de Prado has written the first comprehensive book describing the application of modern ML to financial modeling. The book blends the latest technological developments in ML with critical life lessons learned from the author's decades of financial experience in leading academic and industrial institutions. I highly recommend this exciting book to both prospective students of financial ML and the professors and supervisors who teach and guide them." PROF. PETER CARR, Chair of the Finance and Risk Engineering Department, NYU Tandon School of Engineering
"Financial problems require very distinct machine learning solutions. Dr. López de Prado's book is the first one to characterize what makes standard machine learning tools fail when applied to the field of finance, and the first one to provide practical solutions to unique challenges faced by asset managers. Everyone who wants to understand the future of finance should read this book." PROF. FRANK FABOZZI, EDHEC Business School; Editor of The Journal of Portfolio Management
"Marcos has assembled in one place an invaluable set of lessons and techniques for practitioners seeking to deploy machine learning methods in finance. Marcos's insightful book is laden with useful advice to help keep a curious practitioner from going down any number of blind alleys, or shooting oneself in the foot." ROSS GARON, Head of Cubist Systematic Strategies; Managing Director, Point72 Asset Management
"The first wave of quantitative innovation in finance was led by Markowitz optimization. Machine learning is the second wave and it will touch every aspect of finance. López de Prado's Advances in Financial Machine Learning is essential for readers who want to be ahead of the technology rather than being replaced by it." PROF. CAMPBELL HARVEY, Duke University; Former President of the American Finance Association
"The author's academic and professional first-rate credentials shine through the pages of this book indeed, I could think of few, if any, authors better suited to explaining both the theoretical and the practical aspects of this new and (for most) unfamiliar subject. Destined to become a classic in this rapidly burgeoning field." PROF. RICCARDO REBONATO, EDHEC Business School; Former Global Head of Rates and FX Analytics at PIMCO
Top customer reviews
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Marcos’ book is refreshing as it is written for the actual practitioner with concrete real-world examples, applications, and easy-to-follow code. It has exercises at the end of each chapter for the reader to quiz himself on whether he understands the content. It covers all the necessary topics to understand how machine learning is transforming not only the world, but the financial landscape. While it is not for a beginner, it is a valuable read for someone who has some exposure and wants to understand how ML is applied in finance.
The purpose of this book is not to explain how to apply Deep Learning to make money, but rather to lay a solid foundation of how to invest in a scientifically rigorous fashion given the modern machine learning toolset and access to PBs of data. In many cases, rather than focussing on the specifics of any given model, Dr. Lopez de Prado focuses on generating and selecting useful features.
The book, which is a hybrid of a textbook and a manual, explains using both formal mathematics and empirical evidence why many of the assumptions about Machine Learning applied to the financial world are wrong and follows through with rigorous and practical solutions. For example, one of the most common false assumptions addressed in the book is that of IID samples in financial time series data.
Dr. Lopez de Prado manages to pull together ideas from a wide spectrum of academic disciplines including mathematics, econometrics, machine learning, computer science, information theory, and physics to build a strong scientific basis upon which to algorithmically invest. Despite the diversity of subject matter, the book progresses well, building on and reusing early themes and then exploring domain specific topics like market microstructure and quantum computing. Source code to implement many of the methods is provided as a practical toolkit to test out the claims presented. The thorough use of references is particularly helpful as it keeps the content fairly short and to the point.
Speed reading not recommended. Using a programming analogy, the mathematical notation is more reminiscent of the explicit verbosity of C++ than that of python (which is used in the book and is meant to be concise). It's not much of a problem but be aware the information content is dense.
Something that's mentioned but not explored is how to make use of “alternative datasets”. Given many of the advances in the wider realm of ML have been around data you don’t get from exchanges, it would be nice if some helpful pointers or references for dealing with alternative data were included. That said, it's not the end of the world given the wealth of resources online for analyzing text, image, and video data.
Buy this book if you're an experienced programmer getting into Finance or a Financial Professional looking to strengthen your algorithmic understanding. It is densely packed with a wealth of practical methods and breaks down and offers alternatives to faulty investing science.
Most recent customer reviews
Good to finally find a book written by someone who actually uses Machine Learning every day
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