Advances in Financial Machine Learning - by Marcos Lopez de Prado (Hardcover) (2024)

About the Book

"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. Readers will learn how to structure Big data in a way that is amenable to ML algorithms; how to conduct research with ML algorithms on that data; how to use supercomputing methods; how to backtest your discoveries while avoiding false positives. The book addresses real-life problems faced by practitioners on a daily basis, and explains scientifically sound solutions using math, supported by code and examples. Readers become active users who can test the proposed solutions in their particular 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"--

Book Synopsis

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.

From the Back Cover

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

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.

Advances in Financial Machine Learning - by  Marcos Lopez de Prado (Hardcover) (2024)

FAQs

What are the advances in ML? ›

With the recent and accelerated advances in machine learning (ML), machines can understand natural language, engage in conversations, draw images, create videos and more. Modern ML models are programmed and trained using ML programming frameworks, such as TensorFlow, JAX, PyTorch, among many others.

What is financial machine learning? ›

ML technology is often used in finance to support investment decisions by identifying risks based on historical data and probability statistics. It can also be used to weigh possible outcomes and develop risk management strategies.

Is ML really AI? ›

Machine learning is a subset of artificial intelligence that automatically enables a machine or system to learn and improve from experience. Instead of explicit programming, machine learning uses algorithms to analyze large amounts of data, learn from the insights, and then make informed decisions.

Why is ML so famous? ›

Machine Learning Use Cases

Advancements in AI for applications like natural language processing (NLP) and computer vision (CV) are helping industries like financial services, healthcare, and automotive accelerate innovation, improve customer experience, and reduce costs.

What is the future of machine learning in finance? ›

By leveraging data in finance, machine learning models can analyze millions of transactions to detect subtle patterns indicating any fraud faster and also more accurately than humans. Banks use these analyses to catch fraudulent transactions in real time, reducing fraud losses.

How is AI used in finance? ›

Artificial intelligence (AI) in finance helps drive insights for data analytics, performance measurement, predictions and forecasting, real-time calculations, customer servicing, intelligent data retrieval, and more.

Which bank uses machine learning? ›

Wells Fargo was the first US bank to create an ML-driven customer assistant for Facebook Messenger. It was one of the first to develop machine learning applications in banking. Bank of America was one of the first banks to introduce a virtual ML-powered assistant within a mobile app.

What is advance machine learning? ›

It is a form of artificial intelligence. Advanced machine learning calls for sophisticated programming that includes statistical analysis and generative adversarial networks to find the best path to learning.‎

What's new in machine learning? ›

Low-Code/No-Code Machine Learning for Cost Cutting. Low-code/no-code (LCNC) machine learning platforms allow non-AI experts to create AI applications from predefined components. They democratize access to artificial intelligence and machine learning.

What is the future of ML? ›

Fortune Business Insights has recently published an article, estimating that the machine learning industry will reach nearly \$226 billion by 2030—a massive growth compared to the \$19.2 billion in 2022. We could barely imagine what it would all be like in 2050.

What are the latest advancements in deep learning? ›

Recent trends in deep learning include using more extensive datasets and more sophisticated architectures, as well as incorporating interaction between different types of neural networks and other AI technologies, such as natural language processing and decision trees.

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