Advances In Financial Machine Learning

Advances in Financial Machine Learning
Publisher John Wiley & Sons
Release Date
Category Business & Economics
Total Pages 400
ISBN 9781119482116
Rating 5/5 from 1 reviews
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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.

Advances in Financial Machine Learning
  • Author : Marcos Lopez de Prado
  • Publisher : John Wiley & Sons
  • Release Date : 2018-01-23

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

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Advances in Financial Machine Learning
  • Author : Marcos Lopez de Prado
  • Publisher : John Wiley & Sons
  • Release Date : 2018-02-21

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

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Advances in Financial Machine Learning
  • Author : Marcos Lopez de Prado
  • Publisher : John Wiley & Sons
  • Release Date : 2018-02-02

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

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Machine Learning for Asset Managers
  • Author : Marcos M. López de Prado
  • Publisher : Cambridge University Press
  • Release Date : 2020-04-22

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

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Machine Learning in Finance
  • Author : Matthew F. Dixon,Igor Halperin,Paul Bilokon
  • Publisher : Springer Nature
  • Release Date : 2020-07-01

This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial

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Machine Learning for Financial Engineering
  • Author : László Györfi,György Ottucsák,Harro Walk
  • Publisher : World Scientific
  • Release Date : 2012

Preface v 1 On the History of the Growth-Optimal Portfolio M. M. Christensen 1 2 Empirical Log-Optimal Portfolio Selections: A Survey L. Györfi Gy. Ottucsáak A. Urbán 81 3 Log-Optimal Portfolio-Selection Strategies with Proportional Transaction Costs L. Györfi H. Walk 119 4 Growth-Optimal Portfoho Selection with Short Selling and Leverage M. Horváth

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Machine Learning for Finance
  • Author : Jannes Klaas
  • Publisher : Unknown
  • Release Date : 2019-05-30

Plan and build useful machine learning systems for financial services, with full working Python code Key Features Build machine learning systems that will be useful across the financial services industry Discover how machine learning can solve finance industry challenges Gain the machine learning insights and skills fintech companies value most

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Artificial Intelligence in Finance
  • Author : Yves Hilpisch
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2020-10-14

The widespread adoption of AI and machine learning is revolutionizing many industries today. Once these technologies are combined with the programmatic availability of historical and real-time financial data, the financial industry will also change fundamentally. With this practical book, you'll learn how to use AI and machine learning to discover

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Financial Signal Processing and Machine Learning
  • Author : Ali N. Akansu,Sanjeev R. Kulkarni,Dmitry M. Malioutov
  • Publisher : John Wiley & Sons
  • Release Date : 2016-04-20

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management

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Big Data and Machine Learning in Quantitative Investment
  • Author : Tony Guida
  • Publisher : John Wiley & Sons
  • Release Date : 2019-03-25

Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how

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Hands On Machine Learning for Algorithmic Trading
  • Author : Stefan Jansen
  • Publisher : Packt Publishing Ltd
  • Release Date : 2018-12-31

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key Features Implement machine learning algorithms to build, train, and validate algorithmic models Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions Develop neural networks for algorithmic trading to perform

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Machine Learning and Data Science Blueprints for Finance
  • Author : Hariom Tatsat,Sahil Puri,Brad Lookabaugh
  • Publisher : "O'Reilly Media, Inc."
  • Release Date : 2020-10-01

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement

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Machine Learning for Factor Investing  R Version
  • Author : Guillaume Coqueret,Tony Guida
  • Publisher : CRC Press
  • Release Date : 2020-08-31

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers, notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon, as the

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Advances in Artificial Intelligence
  • Author : Cyril Goutte,Xiaodan Zhu
  • Publisher : Springer Nature
  • Release Date : 2020-05-05

This book constitutes the refereed proceedings of the 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, which was planned to take place in Ottawa, ON, Canada. Due to the COVID-19 pandemic, however, it was held virtually during May 13–15, 2020. The 31 regular papers and 24 short papers presented together with 4 Graduate Student Symposium

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Empirical Asset Pricing
  • Author : Turan G. Bali,Robert F. Engle,Scott Murray
  • Publisher : John Wiley & Sons
  • Release Date : 2016-02-26

“Bali, Engle, and Murray have produced a highly accessible introduction to the techniques and evidence of modern empirical asset pricing. This book should be read and absorbed by every serious student of the field, academic and professional.” Eugene Fama, Robert R. McCormick Distinguished Service Professor of Finance, University of Chicago

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