Machine Learning and AI in Finance (eBook, ePUB)

Redaktion: Creamer, German; Aste, Tomaso; Kazantsev, Gary

• Format: ePub

Produktbeschreibung

This book explores advances in the application of innovative machine learning and artificial intelligence models to predict financial time series, to simulate the structure of the financial markets, to explore nonlinear causality models, to test investment strategies and to price financial options.

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 130
• Erscheinungstermin: 5. April 2021
• Englisch
• ISBN-13: 9781000372045
• Artikelnr.: 60925368

Autorenporträt

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.

Inhaltsangabe

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