Machine Learning and AI in Finance
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The main contribution of this book is the presentation of new theoretical and applied AI perspectives to find solutions to unsolved finance questions. This volume proposes an optimal model for the volatility smile, for modelling high-frequency liquidity demand and supply and for the simulation of market microstructure features. Other new AI developments explored in this book includes building a universal model for a large number of stocks, developing predictive models based on the average price of the crowd, forecasting the stock price using the attention mechanism in a neural network, clustering multivariate time series into different market states, proposing a multivariate distance nonlinear causality test and filtering out false investment strategies with an unsupervised learning algorithm.
Machine Learning and AI in Finance explores the most recent 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.
The chapters in this book were originally published as a special issue of the Quantitative Finance journal.
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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.
