Machine Learning and Data Sciences for Financial Markets

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.