Machine Learning for Factor Investing R Version

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  • Author : Guillaume Coqueret
  • Publisher : CRC Press
  • Pages : 321 pages
  • ISBN : 1000176762
  • Rating : /5 from reviews
CLICK HERE TO GET THIS BOOK >>>Machine Learning for Factor Investing R Version

Download or Read online Machine Learning for Factor Investing R Version full in PDF, ePub and kindle. this book written by Guillaume Coqueret and published by CRC Press which was released on 31 August 2020 with total page 321 pages. We cannot guarantee that Machine Learning for Factor Investing R Version book is available in the library, click Get Book button and read full online book in your kindle, tablet, IPAD, PC or mobile whenever and wherever You Like. 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 jargon and coding requirements may seem out of reach. Machine Learning for Factor Investing: R Version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns, Bayesian additive trees, and causal models. All topics are illustrated with self-contained R code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material, along with the content of the book, is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance, this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.

Machine Learning for Factor Investing R Version

Machine Learning for Factor Investing  R Version
  • Author : Guillaume Coqueret,Tony Guida
  • Publisher : CRC Press
  • Release : 31 August 2020
GET THIS BOOK Machine Learning for Factor Investing R Version

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 jargon and coding requirements may seem out of reach. Machine Learning for Factor Investing: R Version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that

Big Data and Machine Learning in Quantitative Investment

Big Data and Machine Learning in Quantitative Investment
  • Author : Tony Guida
  • Publisher : John Wiley & Sons
  • Release : 12 December 2018
GET THIS BOOK Big Data and Machine Learning in Quantitative Investment

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 of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The

Factor Investing

Factor Investing
  • Author : Emmanuel Jurczenko
  • Publisher : Elsevier
  • Release : 17 October 2017
GET THIS BOOK Factor Investing

This new edited volume consists of a collection of original articles written by leading industry experts in the area of factor investing. The chapters introduce readers to some of the latest research developments in the area of equity and alternative investment strategies.Each chapter deals with new methods for constructing and harvesting traditional and alternative risk premia, building strategic and tactical multifactor portfolios, and assessing related systematic investment performances. This volume will be of help to portfolio managers, asset owners

Computational Science ICCS 2019

Computational Science     ICCS 2019
  • Author : João M. F. Rodrigues,Pedro J. S. Cardoso,Jânio Monteiro,Roberto Lam,Valeria V. Krzhizhanovskaya,Michael H. Lees,Jack J. Dongarra,Peter M.A. Sloot
  • Publisher : Springer
  • Release : 07 June 2019
GET THIS BOOK Computational Science ICCS 2019

The five-volume set LNCS 11536, 11537, 11538, 11539 and 11540 constitutes the proceedings of the 19th International Conference on Computational Science, ICCS 2019, held in Faro, Portugal, in June 2019. The total of 65 full papers and 168 workshop papers presented in this book set were carefully reviewed and selected from 573 submissions (228 submissions to the main track and 345 submissions to the workshops). The papers were organized in topical sections named: Part I: ICCS Main Track Part II: ICCS Main Track; Track of Advances in High-Performance Computational Earth Sciences: Applications

Machine Learning

Machine Learning
  • Author : Claude Sammut,Achim Hoffmann
  • Publisher : Morgan Kaufmann
  • Release : 20 April 2021
GET THIS BOOK Machine Learning

Proceedings of the annual International Conferences on Machine Learning, 1988-present. Current volume: ICML 2002: 19th International Conference on Machine Learning. Submissions are expected that describe empirical, theoretical, and cognitive-modeling research in all areas of machine learning. Submissions that present algorithms for novel learning tasks, interdisciplinary research involving machine learning, or innovative applications of machine learning techniques to challenging, real-world problems are especially encouraged.

R Data Analysis and Visualization

R  Data Analysis and Visualization
  • Author : Tony Fischetti,Brett Lantz,Jaynal Abedin,Hrishi V. Mittal,Bater Makhabel,Edina Berlinger,Ferenc Illes,Milan Badics,Adam Banai,Gergely Daroczi
  • Publisher : Packt Publishing Ltd
  • Release : 24 June 2016
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Master the art of building analytical models using R About This Book Load, wrangle, and analyze your data using the world's most powerful statistical programming language Build and customize publication-quality visualizations of powerful and stunning R graphs Develop key skills and techniques with R to create and customize data mining algorithms Use R to optimize your trading strategy and build up your own risk management system Discover how to build machine learning algorithms, prepare data, and dig deep into data

Operational Expert System Applications in the Far East

Operational Expert System Applications in the Far East
  • Author : Jae Kyu Lee
  • Publisher : Pergamon
  • Release : 20 April 1991
GET THIS BOOK Operational Expert System Applications in the Far East

Part of a series on expert systems around the world. Operational Expert System Applications in the Far East covers various aspects of expert system applications in the Far East. Some of the industries this book reports on include: Steel, electro-mechanical, power, automobile, oil, paper, airline and diagnosis.

Behavioral Investment Management An Efficient Alternative to Modern Portfolio Theory

Behavioral Investment Management  An Efficient Alternative to Modern Portfolio Theory
  • Author : Greg B. Davies,Arnaud de Servigny
  • Publisher : McGraw Hill Professional
  • Release : 05 January 2012
GET THIS BOOK Behavioral Investment Management An Efficient Alternative to Modern Portfolio Theory

A Powerful New Portfolio-Management Standard for an Investing World in Disarray “Three years of losses turn many smart investors with 30-year horizons into frightened investors with three-year horizons, driven to poor decisions by cognitive errors and misleading emotions. Greg B. Davies and Arnaud de Servigny combine great expertise from research and practice into smart portfolios that overcome cognitive errors and misleading emotions and drive investors to their long term goals.” —MEIR STATMAN, Glenn Klimek Professor of Finance, Santa Clara University,