1st Edition
Machine Learning and AI in Finance
Foreword
Marcos Lopez de Prado
Introduction
Germán G. Creamer, Gary Kazantsev and Tomaso Aste
1. Universal features of price formation in financial markets: perspectives from deep learning
Justin Sirignano and Rama Cont
2. Far from the madding crowd: collective wisdom in prediction markets
Giulio Bottazzi and Daniele Giachini
3. Forecasting limit order book liquidity supply–demand curves with functional autoregressive dynamics
Ying Chen, Wee Song Chua and Wolfgang Karl Härdle
4. Forecasting market states
Pier Francesco Procacci and Tomaso Aste
5. Encoding of high-frequency order information and prediction of short-term stock price by deep learning
Daigo Tashiro, Hiroyasu Matsushima, Kiyoshi Izumi and Hiroki Sakaji
6. Attention mechanism in the prediction of stock price movement by using LSTM: Evidence from the Hong Kong stock market
Shun Chen and Lei Ge
7. Learning multi-market microstructure from order book data
Geonhwan Ju, Kyoung-Kuk Kim and Dong-Young Lim
8. A non-linear causality test: a machine learning approach for energy futures forecast
Germán G. Creamer and Chihoon Lee
9. The QLBS Q-Learner goes NuQLear: fitted Q iteration, inverse RL, and option portfolios
Igor Halperin
10. Detection of false investment strategies using unsupervised learning methods
Marcos López de Prado and Michael J. Lewis
Biography
Germán G. Creamer is Associate Professor at Stevens Institute of Technology. He is also a visiting scholar at Stern School of Business, NYU; Adjunct Associate Professor, Columbia University and former Senior Manager, American Express.
Gary Kazantsev is the Head of Quant Technology Strategy, Office of the CTO at Bloomberg L. P., New York, USA.
Tomaso Aste is Professor of Complexity Science, Department of Computer Science, University College London, UK.
