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Machine Learning for Factor Investing: Python Version [Mīkstie vāki]

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  • Formāts: Paperback / softback, 340 pages, height x width: 254x178 mm, weight: 740 g, 15 Tables, black and white; 80 Line drawings, color; 7 Line drawings, black and white; 1 Halftones, color; 81 Illustrations, color; 7 Illustrations, black and white
  • Sērija : Chapman and Hall/CRC Financial Mathematics Series
  • Izdošanas datums: 08-Aug-2023
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 0367639726
  • ISBN-13: 9780367639723
Citas grāmatas par šo tēmu:
  • Mīkstie vāki
  • Cena: 92,42 €
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Machine Learning for Factor Investing: Python VersionPalielināt
  • Formāts: Paperback / softback, 340 pages, height x width: 254x178 mm, weight: 740 g, 15 Tables, black and white; 80 Line drawings, color; 7 Line drawings, black and white; 1 Halftones, color; 81 Illustrations, color; 7 Illustrations, black and white
  • Sērija : Chapman and Hall/CRC Financial Mathematics Series
  • Izdošanas datums: 08-Aug-2023
  • Izdevniecība: Chapman & Hall/CRC
  • ISBN-10: 0367639726
  • ISBN-13: 9780367639723
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780367639723.html
Keywords:
"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: Python 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 supervisedlearning 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 areillustrated with self-contained Python 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 (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: Python 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 Python 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 (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.

Recenzijas

"Machine learning is considered promising for investment management applications, yet the associated low signal to noise ratio presents a high bar for improving on the incumbent quant asset management tooling. The book of Coqueret and Guida is a treat for those who do not want to lose sight of the machine learning forest for the trees. Whether you are an academic scholar or a finance practitioner, you will learn just what you need to rigorously investigate machine learning techniques for factor investing applications, along with plenty of useful code snippets." -Harald Lohre, Executive Director of Research at Robeco and Honorary Researcher at Lancaster University Management School

"Written by two experts on quantitative finance, this book covers everything from basic materials to advanced techniques in the field of quantitative investment strategies: data processing, alpha signal generation, portfolio optimization, backtesting and performance evaluation. Concrete examples related to asset management problems illustrate each machine learning technique, such as neural network, lasso regression, autoencoder or reinforcement learning. With more than 20 coding exercises and solutions provided in Python, this publication is a must for both students, academics and professionals who are looking for an up-to-date technical exposition on quantitative asset management from basic smart beta portfolios to enhanced alpha strategies including factor investing." -Thierry Roncalli, Head of Quantitative Portfolio Strategy at Amundi Institute, Amundi Asset Management

Part
1. Introduction
1. Notations and data
2. Introduction
3. Factor
investing and asset pricing anomalies
4. Data preprocessing Part
2. Common
supervised algorithms
5. Penalized regressions and sparse hedging for minimum
variance portfolios
6. Tree-based methods
7. Neural networks
8. Support
vector machines
9. Bayesian methods Part
3. From predictions to portfolios
10. Validating and tuning
11. Ensemble models
12. Portfolio backtesting Part
4. Further important topics
13. Interpretability
14. Two key concepts:
causality and non-stationarity
15. Unsupervised learning
16. Reinforcement
learning Part
5. Appendix
17. Data description
18. Solutions to exercises
Guillaume Coqueret is associate professor of finance and data science at EMLYON Business School. His recent research revolves around applications of machine learning tools in financial economics.

Tony Guida is co-head of Systematic Macro at RAM Active Investments. He is the editor and co-author of Big Data and Machine Learning in Quantitative Investment.