Capponi A., Lehalle C.-A. (eds.) Machine Learning and Data Sciences for Financial Markets: A Guide to Contemporary Practices

Cambridge University Press, 2023. — 741 p. — ISBN: 978-1-316-51619-5.Machine learning, Artificial Intelligence (AI), and Data Science (DS) pervades every aspect of our everyday lives. Many of the techniques developed by the Computer Science community are becoming increasingly used in the area of financial engineering, ranging from the use of Deep Learning methods for hedging and risk management through the exploitation of AI techniques for investment or design of trading systems. These techniques are also having enormous implications for the operations of financial markets. It is thus not surprising to see increasingly the proliferation of AI research groups or recently created “AI Labs” at major banks, centered around topics of key relevance to financial services. Those include, among others, explainable AI, human-machine interaction, and DS methods for extracting information from data and using it to support investment decisions. The integration of AI methods in the decision-making process may also have unintended or unanticipated consequences, especially in a sector like finance, where bad intermediation of risk can spread over the whole economy.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 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.Data-centric methodology, Machine Learning, and Deep Learning, in particular, can greatly facilitate various computational and modeling tasks in quantitative finance. In this chapter, we first demonstrate how supervised learning can help us implement and calibrate option pricing models that have previously been hard to deploy due to their analytical intractability. Secondly, we illustrate how we can discover optimal hedging strategies and arbitrage-free prices in a model-free fashion via the recent unsupervised deep hedging approach. As the availability of high-quality training samples underpins these data-centric methods, we finally outline recent work in the nascent field of market data generators, which are used to generate realistic, yet synthetic, market data for the training of financial Machine Learning algorithms.INTERACTING WITH INVESTORS AND ASSET OWNERS.
Part I Robo Advisors and Automated Recommendation
Introduction to Part I. Robo-advising as a Technological Platform for Optimization and Recommendations.
New Frontiers of Robo-Advising: Consumption, Saving, Debt Management, and Taxes.
Robo-Advising: Less AI and More XAI? Augmenting Algorithms with Humans-in-the-Loop.
Robo-advisory: From investing principles and algorithms to future developments.
Recommender Systems for Corporate Bond Trading.
Part II How Learned Flows Form Prices
Introduction to Part II. Price Impact: Information Revelation or Self-Fulfilling Prophecies?
Order Flow and Price Formation.
Price Formation and Learning in Equilibrium under Asymmetric Information.
Deciphering How Investors’ Daily Flows are Forming Prices.
TOWARDS BETTER RISK INTERMEDIATION.
Part III High-Frequency Finance
Introduction to Part III.
Reinforcement Learning Methods in Algorithmic Trading.
Stochastic Approximation Applied to Optimal Execution: Learning by Trading.
Reinforcement Learning for Algorithmic Trading.
V Advanced Optimization Techniques.
Introduction to Part IV. Advanced Optimization Techniques for Banks and Asset Managers.
Harnessing Quantitative Finance by Data-Centric Methods.
Asset Pricing and Investment with Big Data.
Portfolio Construction Using Stratified Models.
Part V New Frontiers for Stochastic Control in Finance
Introduction to Part V. Machine Learning and Applied Mathematics: a Game of Hide-and-Seek?
The Curse of Optimality, and How to Break it?
Deep Learning for Mean Field Games and Mean Field Control with Applications to Finance.
Reinforcement Learning for Mean Field Games, with Applications to Economics.
Neural Networks-Based Algorithms for Stochastic Control and PDEs in Finance.
Generative Adversarial Networks: Some Analytical Perspectives.
Part VI Nowcasting with Alternative Data
Introduction to Part VI. Nowcasting is Coming.
Data Preselection in Machine Learning Methods: An Application to Macroeconomic Nowcasting with Google Search Data.
Alternative data and ML for macro nowcasting.
Nowcasting Corporate Financials and Consumer Baskets with Alternative Data.
NLP in Finance.
The Exploitation of Recurrent Satellite Imaging for the Fine-Scale.
Observation of Human Activity.
Part VII Biases and Model Risks of Data-Driven Learning
Introduction to Part VII. Towards the Ideal Mix between Data and Models.
Generative Pricing Model Complexity: The Case for Volatility Managed Portfolios.
Bayesian Deep Fundamental Factor Models.
Black-Box Model Risk in Finance.