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.

• ForewordMarcos Lopez de PradoIntroductionGermán G. Creamer, Gary Kazantsev and Tomaso Aste1. Universal features of price formation in financial markets: perspectives from deep learningJustin Sirignano and Rama Cont2. Far from the madding crowd: collective wisdom in prediction marketsGiulio Bottazzi and Daniele Giachini3. Forecasting limit order book liquidity supply–demand curves with functional autoregressive dynamicsYing Chen, Wee Song Chua and Wolfgang Karl Härdle4. Forecasting market statesPier Francesco Procacci and Tomaso Aste5. Encoding of high-frequency order information and prediction of short-term stock price by deep learningDaigo Tashiro, Hiroyasu Matsushima, Kiyoshi Izumi and Hiroki Sakaji6. Attention mechanism in the prediction of stock price movement by using LSTM: Evidence from the Hong Kong stock market Shun Chen and Lei Ge7. Learning multi-market microstructure from order book dataGeonhwan Ju, Kyoung-Kuk Kim and Dong-Young Lim8. A non-linear causality test: a machine learning approach for energy futures forecastGermán G. Creamer and Chihoon Lee9. The QLBS Q-Learner goes NuQLear: fitted Q iteration, inverse RL, and option portfoliosIgor Halperin10. Detection of false investment strategies using unsupervised learning methodsMarcos López de Prado and Michael J. Lewis

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.

• ForewordMarcos Lopez de PradoIntroductionGermán G. Creamer, Gary Kazantsev and Tomaso Aste1. Universal features of price formation in financial markets: perspectives from deep learningJustin Sirignano and Rama Cont2. Far from the madding crowd: collective wisdom in prediction marketsGiulio Bottazzi and Daniele Giachini3. Forecasting limit order book liquidity supply–demand curves with functional autoregressive dynamicsYing Chen, Wee Song Chua and Wolfgang Karl Härdle4. Forecasting market statesPier Francesco Procacci and Tomaso Aste5. Encoding of high-frequency order information and prediction of short-term stock price by deep learningDaigo Tashiro, Hiroyasu Matsushima, Kiyoshi Izumi and Hiroki Sakaji6. Attention mechanism in the prediction of stock price movement by using LSTM: Evidence from the Hong Kong stock market Shun Chen and Lei Ge7. Learning multi-market microstructure from order book dataGeonhwan Ju, Kyoung-Kuk Kim and Dong-Young Lim8. A non-linear causality test: a machine learning approach for energy futures forecastGermán G. Creamer and Chihoon Lee9. The QLBS Q-Learner goes NuQLear: fitted Q iteration, inverse RL, and option portfoliosIgor Halperin10. Detection of false investment strategies using unsupervised learning methodsMarcos López de Prado and Michael J. Lewis

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.

• ForewordMarcos Lopez de PradoIntroductionGermán G. Creamer, Gary Kazantsev and Tomaso Aste1. Universal features of price formation in financial markets: perspectives from deep learningJustin Sirignano and Rama Cont2. Far from the madding crowd: collective wisdom in prediction marketsGiulio Bottazzi and Daniele Giachini3. Forecasting limit order book liquidity supply–demand curves with functional autoregressive dynamicsYing Chen, Wee Song Chua and Wolfgang Karl Härdle4. Forecasting market statesPier Francesco Procacci and Tomaso Aste5. Encoding of high-frequency order information and prediction of short-term stock price by deep learningDaigo Tashiro, Hiroyasu Matsushima, Kiyoshi Izumi and Hiroki Sakaji6. Attention mechanism in the prediction of stock price movement by using LSTM: Evidence from the Hong Kong stock market Shun Chen and Lei Ge7. Learning multi-market microstructure from order book dataGeonhwan Ju, Kyoung-Kuk Kim and Dong-Young Lim8. A non-linear causality test: a machine learning approach for energy futures forecastGermán G. Creamer and Chihoon Lee9. The QLBS Q-Learner goes NuQLear: fitted Q iteration, inverse RL, and option portfoliosIgor Halperin10. Detection of false investment strategies using unsupervised learning methodsMarcos López de Prado and Michael J. Lewis