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Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporar
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Specificaties
- Objectstaat
- Book Title
- Machine Learning and Data Sciences for Financial Markets: A Guide
- ISBN
- 1316516199
- Subject Area
- Business & Economics, Mathematics
- Publication Name
- Machine Learning and Data Sciences for Financial Markets : a Guide to Contemporary Practices
- Item Length
- 10.2 in
- Publisher
- Cambridge University Press
- Subject
- Finance / General, Applied
- Publication Year
- 2023
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 1.5 in
- Item Width
- 7.2 in
- Number of Pages
- 741 Pages
Over dit product
Product Information
Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.
Product Identifiers
Publisher
Cambridge University Press
ISBN-10
1316516199
ISBN-13
9781316516195
eBay Product ID (ePID)
8058632786
Product Key Features
Publication Name
Machine Learning and Data Sciences for Financial Markets : a Guide to Contemporary Practices
Format
Hardcover
Language
English
Subject
Finance / General, Applied
Publication Year
2023
Type
Textbook
Subject Area
Business & Economics, Mathematics
Number of Pages
741 Pages
Dimensions
Item Length
10.2 in
Item Height
1.5 in
Item Width
7.2 in
Additional Product Features
LCCN
2023-016903
Lc Classification Number
Hg104
Reviews
'Agostino Capponi and Charles-Albert Lehalle have edited an excellent book that addresses important questions regarding the application of machine learning and data science techniques to the challenging field of finance. I highly recommend this book to readers interested in our field.' Marcos López de Prado, Abu Dhabi Investment Authority & Cornell University, 'Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices' comes at a critical time in the financial markets. The amount of machine readable data available to practitioners, the power of the statistical models they can build, and the computational power available to train them keeps growing exponentially. AI and machine learning are increasingly embedded into every aspect of the investing process. The common curriculum, however, both in finance and in applications of machine learning, lags behind. This book provides an excellent and very thorough overview of the state of the art in the field, with contributions by key researchers and practitioners. The monumental work done by the editors and reviewers shows in the wide diversity of current topics covered - from deep learning for solving partial differential equations to transformative breakthroughs in NLP. This book, which I cannot recommend highly enough, will be useful to any practitioner or student who wishes to familiarize themselves with the current state of the art and build their careers and research on a solid foundation.' Gary Kazantsev, Bloomberg and Columbia University, 'Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices' comes at a critical time in the financial markets. The amount of machine readable data available to practitioners, the power of the statistical models they can build, and the computational power available to train them keeps growing exponentially. AI and machine learning are increasingly embedded into every aspect of the investing process. The common curriculum, however, both in finance and in applications of machine learning, lags behind. This book provides an excellent and very thorough overview of the state of the art in the field, with contributions by key researchers and practitioners. The monumental work done by the editors and reviewers shows in the wide diversity of current topics covered - from deep learning for solving partial differential equations to transformative breakthroughs in NLP. This book, which I cannot recommend highly enough, will be useful to any practitioner or student who wishes to familiarize themselves with the current state of the art and build their careers and research on a solid foundation.' Gary Kazantsev, Bloomberg and Columbia Universityze themselves with the current state of the art and build their careers and research on a solid foundation.' Gary Kazantsev, Bloomberg and Columbia Universityze themselves with the current state of the art and build their careers and research on a solid foundation.' Gary Kazantsev, Bloomberg and Columbia Universityze themselves with the current state of the art and build their careers and research on a solid foundation.' Gary Kazantsev, Bloomberg and Columbia University, 'Beginning with the 1973 publication of the Black-Scholes formula, mathematical models coupled with computing revolutionized finance. We are now witnessing a second revolution as larger-scale computing makes data science and machine learning methods feasible. This book demonstrates that the second revolution is not a departure from, but rather a continuation of, the first revolution. It will be essential reading for researchers in quantitative finance, whether they were participants in the first revolution or are only now joining the fray.' Steven E. Shreve, Carnegie Mellon University, 'Agostino Capponi and Charles-Albert Lehalle have edited an excellent book tackling the most important topics associated with the application of machine learning and data science techniques to the challenging field of finance, including robo-advisory, high-frequency trading, nowcasting, and alternative data. I highly recommend this book to any reader interested in our field, regardless of experience or background.' Marcos López de Prado, Abu Dhabi Investment Authority & Cornell University
Table of Content
Interacting with Investors and Asset Owners: Part I. Robo-advisors and Automated Recommendation: 1. Introduction to Part I. Robo-advising as a technological platform for optimization and recommendations; 2. New frontiers of robo-advising: consumption, saving, debt management, and taxes; 3. Robo-advising: less AI and more XAI? Augmenting algorithms with humans-in-the-loop; 4. Robo-advisory: from investing principles and algorithms to future developments; 5. Recommender systems for corporate bond trading; Part II. How Learned Flows Form Prices: 6. Introduction to Part II. Price impact: information revelation or self-fulfilling prophecies?; 7. Order flow and price formation; 8. Price formation and learning in equilibrium under asymmetric information; 9. Deciphering how investors' daily flows are forming prices; Towards Better Risk Intermediation: Part III. High Frequency Finance: 10. Introduction to Part III; 11. Reinforcement learning methods in algorithmic trading; 12. Stochastic approximation applied to optimal execution: learning by trading; 13. Reinforcement learning for algorithmic trading; Part IV. Advanced Optimization Techniques: 14. Introduction to Part IV. Advanced optimization techniques for banks and asset managers; 15. Harnessing quantitative finance by data-centric methods; 16. Asset pricing and investment with big data; 17. Portfolio construction using stratified models; Part V. New Frontiers for Stochastic Control in Finance: 18. Introduction to Part V. Machine learning and applied mathematics: a game of hide-and-seek?; 19. The curse of optimality, and how to break it?; 20. Deep learning for mean field games and mean field control with applications to finance; 21. Reinforcement learning for mean field games, with applications to economics; 22. Neural networks-based algorithms for stochastic control and PDEs in finance; 23. Generative adversarial networks: some analytical perspectives; Connections with the Real Economy: Part VI. Nowcasting with Alternative Data: 24. Introduction to Part VI. Nowcasting is coming; 25. Data preselection in machine learning methods: an application to macroeconomic nowcasting with Google search data; 26. Alternative data and ML for macro nowcasting; 27. Nowcasting corporate financials and consumer baskets with alternative data; 28. NLP in finance; 29. The exploitation of recurrent satellite imaging for the fine-scale observation of human activity; Part VII. Biases and Model Risks of Data-Driven Learning: 30. Introduction to Part VII. Towards the ideal mix between data and models; 31. Generative Pricing model complexity: the case for volatility-managed portfolios; 32. Bayesian deep fundamental factor models; 33. Black-box model risk in finance; Index.
Dewey Decimal
332.0285631
Dewey Edition
23
Illustrated
Yes
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