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Machine Learning and Data Sciences for Financial Markets
A Guide to Contemporary Practices

£100.00

Lisa L. Huang, Francesco D'Acunto, Alberto G. Rossi, Milo Bianchi, Marie Brière, Adam Grealish, Petter N. Kolm, Dominic Wright, Artur Henrykowski, Jacky Lee, Luca Capriotti, Jean-Philippe Bouchaud, Fabrizio Lillo, Umut Çetin, Daniel Giamouridis, Georgios V. Papaioannou, Brice Rosenzweig, Robert Almgren, Olivier Guéant, Sophie Laruelle, Álvaro Cartea, Sebastian Jaimungal, Leandro Sánchez-Betancourt, Paul Bilokon, Matthew F. Dixon, Igor Halperin, Blanka Horvath, Aitor Muguruza Gonzalez, Mikko S. Pakkanen, Markus Pelger, Jonathan Tuck, Shane Barratt, Stephen Boyd, Gilles Pagès, Xun Yu Zhou, René Carmona, Mathieu Laurière, Andrea Angiuli, Jean-Pierre Fouque, Maximilien Germain, Huyên Pham, Xavier Warin, Haoyang Cao, Xin Guo, Michael Recce, Laurent Ferrara, Anna Simoni, Apurv Jain, Michael Fleder, Devavrat Shah, Prabhanjan Kambadur, Gideon Mann, Amanda Stent, Carlo de Franchis, Sébastien Drouyer, Gabriele Facciolo, Rafael Grompone von Gioi, Charles Hessel, Jean-Michel Morel, Mathieu Rosenbaum, Brian Clark, Akhtar Siddique, Majeed Simaan, Matthew F. Dixon, Nicholas G. Polson, Samuel N. Cohen, Derek Snow, Lukasz Szpruch
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  • Date Published: June 2023
  • availability: Available
  • format: Hardback
  • isbn: 9781316516195

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About the Authors
  • 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.

    • Provides concrete applications illustrating how machine learning solves problems faced in financial markets
    • Places a special focus on alternative data and nowcasting, showing how to use alternative data to improve the efficiency of quantitative models
    • Addresses the specificities of explainable AI and biases in learning for financial applications
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    Reviews & endorsements

    '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

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    Product details

    • Date Published: June 2023
    • format: Hardback
    • isbn: 9781316516195
    • length: 741 pages
    • dimensions: 260 x 183 x 37 mm
    • weight: 1.67kg
    • availability: Available
  • Table of Contents

    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.

  • Editors

    Agostino Capponi, Columbia University, New York
    Agostino Capponi is Associate Professor in the Department of Industrial Engineering and Operations Research at Columbia University. He conducts research in financial technology and market microstructure. His work has been recognized with the NSF CAREER Award, and a JP Morgan AI Research award. Capponi is a co-editor of Management Science and Mathematics and Financial Economics. He is a Council member of the Bachelier Financial Society, and recently served as Chair of the SIAM-FME and INFORMS Finance.

    Charles-Albert Lehalle, Abu Dhabi Investment Authority
    Charles-Albert Lehalle is Global Head of Quantitative R&D at Abu Dhabi Investment Authority and Visiting Professor at Imperial College London. He has a Ph.D. in machine learning, was previously Head of Data Analytics at CFM, and held different Global Head positions at Crédit Agricole CIB. Recognized as an expert in market microstructure, Lehalle is often invited to present to regulators and policy-makers.

    Contributors

    Lisa L. Huang, Francesco D'Acunto, Alberto G. Rossi, Milo Bianchi, Marie Brière, Adam Grealish, Petter N. Kolm, Dominic Wright, Artur Henrykowski, Jacky Lee, Luca Capriotti, Jean-Philippe Bouchaud, Fabrizio Lillo, Umut Çetin, Daniel Giamouridis, Georgios V. Papaioannou, Brice Rosenzweig, Robert Almgren, Olivier Guéant, Sophie Laruelle, Álvaro Cartea, Sebastian Jaimungal, Leandro Sánchez-Betancourt, Paul Bilokon, Matthew F. Dixon, Igor Halperin, Blanka Horvath, Aitor Muguruza Gonzalez, Mikko S. Pakkanen, Markus Pelger, Jonathan Tuck, Shane Barratt, Stephen Boyd, Gilles Pagès, Xun Yu Zhou, René Carmona, Mathieu Laurière, Andrea Angiuli, Jean-Pierre Fouque, Maximilien Germain, Huyên Pham, Xavier Warin, Haoyang Cao, Xin Guo, Michael Recce, Laurent Ferrara, Anna Simoni, Apurv Jain, Michael Fleder, Devavrat Shah, Prabhanjan Kambadur, Gideon Mann, Amanda Stent, Carlo de Franchis, Sébastien Drouyer, Gabriele Facciolo, Rafael Grompone von Gioi, Charles Hessel, Jean-Michel Morel, Mathieu Rosenbaum, Brian Clark, Akhtar Siddique, Majeed Simaan, Matthew F. Dixon, Nicholas G. Polson, Samuel N. Cohen, Derek Snow, Lukasz Szpruch

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