Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices
Editat de Agostino Capponi, Charles-Albert Lehalleen Limba Engleză Hardback – 31 mai 2023
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Specificații
ISBN-13: 9781316516195
ISBN-10: 1316516199
Pagini: 741
Dimensiuni: 183 x 260 x 37 mm
Greutate: 1.59 kg
Editura: Cambridge University Press
Colecția Cambridge University Press
Locul publicării:Cambridge, United Kingdom
ISBN-10: 1316516199
Pagini: 741
Dimensiuni: 183 x 260 x 37 mm
Greutate: 1.59 kg
Editura: Cambridge University Press
Colecția Cambridge University Press
Locul publicării:Cambridge, United Kingdom
Cuprins
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.
Recenzii
'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
'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
'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
'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
'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
Descriere
Learn how cutting-edge AI and data science techniques are integrated in financial markets from leading experts in the industry.