Marcos Lopez De Prado's Books

Advances in Financial Machine Learning

By: Marcos Lopez de Prado

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Advances in Financial Machine Learning

By: Marcos Lopez de Prado

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.

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Page Count

406

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119482089

ISBN 13

9781119482086

Machine Learning for Asset Managers

By: Marcos M. López de Prado

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Machine Learning for Asset Managers

By: Marcos M. López de Prado

Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to "learn" complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.

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Page Count

152

Publisher

Cambridge University Press

Published Date

ISBN 10

1108879721

ISBN 13

9781108879729

Causal Factor Investing

Can Factor Investing Become Scientific?

By: Marcos M. López de Prado

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Causal Factor Investing

Can Factor Investing Become Scientific?

By: Marcos M. López de Prado

Virtually all journal articles in the factor investing literature make associational claims, instead of causal claims. This Element analyzes the current state of causal confusion and proposes solutions with the potential to transform factor investing into a truly scientific discipline. This title is also available as Open Access on Cambridge Core.

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Cambridge University Press

Published Date

ISBN 10

1009397303

ISBN 13

9781009397308

Machine Learning for Asset Managers

By: Marcos M. López de Prado

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Machine Learning for Asset Managers

By: Marcos M. López de Prado

Successful investment strategies are specific implementations of general theories. An investment strategy that lacks a theoretical justification is likely to be false. Hence, an asset manager should concentrate her efforts on developing a theory rather than on backtesting potential trading rules. The purpose of this Element is to introduce machine learning (ML) tools that can help asset managers discover economic and financial theories. ML is not a black box, and it does not necessarily overfit. ML tools complement rather than replace the classical statistical methods. Some of ML's strengths include (1) a focus on out-of-sample predictability over variance adjudication; (2) the use of computational methods to avoid relying on (potentially unrealistic) assumptions; (3) the ability to “learn” complex specifications, including nonlinear, hierarchical, and noncontinuous interaction effects in a high-dimensional space; and (4) the ability to disentangle the variable search from the specification search, robust to multicollinearity and other substitution effects.

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Page Count

130

Publisher

Cambridge University Press

Published Date

ISBN 10

1108885403

ISBN 13

9781108885409

Advances in Financial Machine Learning

By: Marcos Lopez de Prado

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Advances in Financial Machine Learning

By: Marcos Lopez de Prado

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Page Count

399

Publisher

John Wiley & Sons

Published Date

ISBN 10

1119482100

ISBN 13

9781119482109

Asset Management: Tools And Issues

By: Frank J Fabozzi,Francesco A Fabozzi,Marcos Lopez De Prado,Stoyan V Stoyanov

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Asset Management: Tools And Issues

By: Frank J Fabozzi,Francesco A Fabozzi,Marcos Lopez De Prado,Stoyan V Stoyanov

Long gone are the times when investors could make decisions based on intuition. Modern asset management draws on a wide-range of fields beyond financial theory: economics, financial accounting, econometrics/statistics, management science, operations research (optimization and Monte Carlo simulation), and more recently, data science (Big Data, machine learning, and artificial intelligence). The challenge in writing an institutional asset management book is that when tools from these different fields are applied in an investment strategy or an analytical framework for valuing securities, it is assumed that the reader is familiar with the fundamentals of these fields. Attempting to explain strategies and analytical concepts while also providing a primer on the tools from other fields is not the most effective way of describing the asset management process. Moreover, while an increasing number of investment models have been proposed in the asset management literature, there are challenges and issues in implementing these models. This book provides a description of the tools used in asset management as well as a more in-depth explanation of specialized topics and issues covered in the companion book, Fundamentals of Institutional Asset Management. The topics covered include the asset management business and its challenges, the basics of financial accounting, securitization technology, analytical tools (financial econometrics, Monte Carlo simulation, optimization models, and machine learning), alternative risk measures for asset allocation, securities finance, implementing quantitative research, quantitative equity strategies, transaction costs, multifactor models applied to equity and bond portfolio management, and backtesting methodologies. This pedagogic approach exposes the reader to the set of interdisciplinary tools that modern asset managers require in order to extract profits from data and processes.

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514

Publisher

World Scientific

Published Date

ISBN 10

9811225761

ISBN 13

9789811225765

Quantum Machine Learning and Optimisation in Finance

On the Road to Quantum Advantage

By: Antoine Jacquier,Oleksiy Kondratyev,Alexander Lipton,Marcos Lopez de Prado

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Quantum Machine Learning and Optimisation in Finance

On the Road to Quantum Advantage

By: Antoine Jacquier,Oleksiy Kondratyev,Alexander Lipton,Marcos Lopez de Prado

Learn the principles of quantum machine learning and how to apply them While focus is on financial use cases, all the methods and techniques are transferable to other fields Purchase of Print or Kindle includes a free eBook in PDF Key Features Discover how to solve optimisation problems on quantum computers that can provide a speedup edge over classical methods Use methods of analogue and digital quantum computing to build powerful generative models Create the latest algorithms that work on Noisy Intermediate-Scale Quantum (NISQ) computers Book Description With recent advances in quantum computing technology, we finally reached the era of Noisy Intermediate-Scale Quantum (NISQ) computing. NISQ-era quantum computers are powerful enough to test quantum computing algorithms and solve hard real-world problems faster than classical hardware. Speedup is so important in financial applications, ranging from analysing huge amounts of customer data to high frequency trading. This is where quantum computing can give you the edge. Quantum Machine Learning and Optimisation in Finance shows you how to create hybrid quantum-classical machine learning and optimisation models that can harness the power of NISQ hardware. This book will take you through the real-world productive applications of quantum computing. The book explores the main quantum computing algorithms implementable on existing NISQ devices and highlights a range of financial applications that can benefit from this new quantum computing paradigm. This book will help you be one of the first in the finance industry to use quantum machine learning models to solve classically hard real-world problems. We may have moved past the point of quantum computing supremacy, but our quest for establishing quantum computing advantage has just begun! What you will learn Train parameterised quantum circuits as generative models that excel on NISQ hardware Solve hard optimisation problems Apply quantum boosting to financial applications Learn how the variational quantum eigensolver and the quantum approximate optimisation algorithms work Analyse the latest algorithms from quantum kernels to quantum semidefinite programming Apply quantum neural networks to credit approvals Who this book is for This book is for Quants and developers, data scientists, researchers, and students in quantitative finance. Although the focus is on financial use cases, all the methods and techniques are transferable to other areas.

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Page Count

443

Publisher

Packt Publishing Ltd

Published Date

ISBN 10

1801817871

ISBN 13

9781801817875

Causal Factor Investing

Can Factor Investing Become Scientific?

By: Marcos M. López de Prado

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Causal Factor Investing

Can Factor Investing Become Scientific?

By: Marcos M. López de Prado

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Publisher

Cambridge University Press

Published Date

ISBN 10

1009397303

ISBN 13

9781009397308

Quantum Machine Learning and Optimisation in Finance

On the Road to Quantum Advantage

By: Antoine Jacquier,Oleksiy Kondratyev,Alexander Lipton,Marcos Lopez de Prado

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Quantum Machine Learning and Optimisation in Finance

On the Road to Quantum Advantage

By: Antoine Jacquier,Oleksiy Kondratyev,Alexander Lipton,Marcos Lopez de Prado

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Solve jigsaw puzzle

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Page Count

443

Publisher

Packt Publishing Ltd

Published Date

ISBN 10

1801817871

ISBN 13

9781801817875

Asset Management: Tools And Issues

By: Frank J Fabozzi,Francesco A Fabozzi,Marcos Lopez De Prado,Stoyan V Stoyanov

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Asset Management: Tools And Issues

By: Frank J Fabozzi,Francesco A Fabozzi,Marcos Lopez De Prado,Stoyan V Stoyanov

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N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

514

Publisher

World Scientific

Published Date

ISBN 10

9811225761

ISBN 13

9789811225765

Book's by Marcos Lopez De Prado

Advances in Financial Machine Learning

Advances in Financial Machine Learning

Machine Learning for Asset Managers

Machine Learning for Asset Managers

Causal Factor Investing

Causal Factor Investing

Machine Learning for Asset Managers

Machine Learning for Asset Managers

Advances in Financial Machine Learning

Advances in Financial Machine Learning

Asset Management: Tools And Issues

Asset Management: Tools And Issues

Quantum Machine Learning and Optimisation in Finance

Quantum Machine Learning and Optimisation in Finance

Causal Factor Investing

Causal Factor Investing

Quantum Machine Learning and Optimisation in Finance

Quantum Machine Learning and Optimisation in Finance

Asset Management: Tools And Issues

Asset Management: Tools And Issues

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