Machine learning and data sciences for financial markets : a guide to contemporary practices

1 Online-Ressource (xxii, 719 Seiten)

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.

10.1017/9781009028943 [DOI]

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