Machine Learning and Data Sciences for Financial Markets -

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Résumé :
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--

Sommaire:
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.