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Machine Learning and AI in Finance

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artificial intelligence
AUC Score
Basel Iii
Bellman Optimality Equation
BSM Model
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deep learning applications
Deep Learning Model
Deep Neural Network
Energy Futures
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financial markets
financial time series modelling
Granger Causality
Heston Model
innovative machine learning
Lead Lag Relationship
Limit Order Book
Linear Granger Causality Test
machine learning
market microstructure analysis
MLP Model
Nonlinear Granger Causality Test
Order Book Data
Order Book Information
Out-of Sample Forecast Accuracy
Out-of Sample Prediction Accuracy
Precision Matrix
predictive modelling for stock prices
quantitative finance
Recurrent Neural Network
Silhouette Score
Single Task Training
Sr
Tar Model
unsupervised learning finance
Var Error Correction Model
volatility prediction models

Product details

  • ISBN 9780367703325
  • Weight: 400g
  • Dimensions: 210 x 297mm
  • Publication Date: 06 Apr 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

The significant amount of information available in any field requires a systematic and analytical approach to select the most critical information and anticipate major events. During the last decade, the world has witnessed a rapid expansion of applications of artificial intelligence (AI) and machine learning (ML) algorithms to an increasingly broad range of financial markets and problems. Machine learning and AI algorithms facilitate this process understanding, modelling and forecasting the behaviour of the most relevant financial variables.

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.
About

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.