Read more
Informationen zum Autor 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 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. Klappentext "Leveraging the research efforts of more than 60 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 in quantitative finance over the past 40 years and modern techniques generated by the current revolution in data sciences and artificial intelligence. The text is structured around three main areas: "Interacting with investors and asset owners," which covers robo-advisors and price formation; "Towards better 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 behind the theory"-- Zusammenfassung Written by more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets, and explores connections with data science and more traditional approaches. This is an invaluable resource for researchers and graduate students in financial engineering, as well as practitioners in the sector. Inhaltsverzeichnis 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 mat...
