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Machine Learning And Data Sciences For Financial Markets: A Guide To Contemporary Practices

AIMachine Learning And Data Sciences For Financial Markets: A Guide To Contemporary Practices

Charles-Albert Lehalle and Agostino Capponi (editors) have sent earlier this week to Cambridge University Press the manuscript of Machine Learning And Data Sciences For Financial Markets: A Guide To Contemporary Practices. The book should be available in min-2022.

It includes 800 pages by 60 contributors on how to use machine learning (ML) either to improve the quality of the service, the risk management, or the link with the real economy in Financial Markets.

The editors wanted to show the continuity between quantitative finance and machine learning in this field because leveraging on existing scientific progress is far better than restarting from scratch a data only-driven approach.

The contents of the book are as follows:

I Interacting with investors and asset owners

1 Robo advisors and automated recommendation

1.1 Foreword: Robo-advising as a technological platform for optimization and recommendations (Lisa L Huang)

1.2 New Frontiers of Robo-Advising: Consumption, Saving, Debt Management, and Taxes (Francesco D’Acunto, Alberto G Rossi)

1.3 Robo-Advising: Less AI and More XAI? (Milo Bianchi, Marie Brière)

1.4 Robo-Advisory (Adam Grealish, Petter N. Kolm)

1.5 Recommender Systems for Corporate Bond Trading (Dominic Wright, Artur Henrykowski, Jacky Lee, Luca Capriotti)

2 How learned flows form prices

2.1 Foreword: Price Impact: Information revelation or self-fulfilling prophecies? (Jean-Philippe Bouchaud)

2.2 Order Flow and Price Formation (Fabrizio Lillo)

2.3 Learning in equilibrium (Umut Cetin)

2.4 Deciphering Flows (Daniel Giamouridis, Georgios V. Papaioannou, Brice Rosenzweig)

II Towards better risk intermediation

3 High Frequency Finance

3.1 Foreword: Challenges For Learning In Trading (Robert Almgren)

3.2 Reinforcement Learning methods (Olivier Guéant)

3.3 Learning By Trading (Sophie Laruelle)

3.4 Deep Reinforcement Learning for Algorithmic Trading (Álvaro Cartea, Sebastian Jaimungal, Leandro Sánchez-Betancourt)

4 Advanced optimization techniques

4.1 Foreword: Advanced optimization techniques for banks and asset managers (Paul Bilokon, Matthew F. Dixon, Igor Halperin)

4.2 Data-centric methods (Blanka Horvath, Aitor Muguruza Gonzalez, Mikko S. Pakkanen)

4.3 Asset Pricing and Investment with Big Data (Markus Pelger)

4.4 Portfolio Construction Using Stratified Models (Jonathan Tuck, Shane Barratt, Stephen Boyd)

5 New frontiers for stochastic control in finance

5.1 Foreword: Machine Learning and Applied Mathematics: a hide-and-seek game? (‪Gilles Pagès)

5.2 Curse of optimality, and how to break it (Xun Yu Zhou)

5.3 Deep Learning for Mean Field Games and Mean Field Control with Applications to Finance (René Carmona and Mathieu Laurière)

5.4 Reinforcement Learning for Mean Field Games, with Applications to Economics

5.5 NN based algorithms for PDEs (Maximilien Germain, Huyên Pham, Xavier Warin)

5.6 GANs, MFGs and SDEs (Haoyang Cao, Xin Guo)

III Connections with the real economy

6 Nowcasting with alternative data

6.1 Foreward: Nowcasting Is Coming (Michael Recce)

6.2 Preselection in ML for macro nowcasting (Laurent Ferrara, Anna Simoni)

6.3 Big Data and ML for Macro Nowcasting (Apurv Jain)

6.4 Nowcasting Financials and Product Sales with Alternative Data (Michael Fleder, Devavrat Shah)

6.5 NLP in Finance (Prabhanjan Kambadur, Gideon Mann, Amanda Stent)

6.6 Recurrent satellite imaging (Carlo de Franchis, Sébastien Drouyer, Gabriele Facciolo, Rafael Grompone von Gioi, Charles Hessel, Jean-Michel Morel)

7 Biases and model risks of data driven learning

7.1 Foreword: Towards the ideal mix between data and models (Mathieu Rosenbaum)

7.2 Pricing Model Complexity: The Case for Volatility-Managed Portfolios (Brian Clark, Akhtar Siddique, Majeed Simaan)

7.3 Bayesian Deep Fundamental Factor Models (Matthew F. Dixon, Nicholas G. Polson)

7.4 Black-box model risk in finance (Samuel N. Cohen, Derek Snow, Lukasz Szpruch)

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