Natural Computing in Computational Finance : Volume 4

Anthony Brabazon

ISBN 10: 364223335X / ISBN 13: 9783642233357

Published by Springer, Springer Spektrum, 2011

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This book follows on from Natural Computing in Computational Finance Volumes I, II and III. As in the previous volumes of this series, the book consists of a series of chapters each of which was selected following a rigorous, peer-reviewed, selection process. The chapters illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics. The applications explored include option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation. While describing cutting edge applications, the chapters are written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computational finance and economics. which was selected following a rigorous, peer-reviewed, selection process. The chapters illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics. The applications explored include option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation. While describing cutting edge applications, the chapters are written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computational finance and economics. The applications explored include option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation. While describing cutting edge applications, the chapters are written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computational finance and economics. written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computational finance and economics.

Seller Inventory # 9783642233357

Bibliographic Details

• Title: Natural Computing in Computational Finance : Volume 4
• Publisher: Springer, Springer Spektrum
• Publication Date: 2011
• Language: English
• ISBN 10: 364223335X
• ISBN 13: 9783642233357
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 18D millimeters 241 millimeters width by 160 millimeters height by 18 millimeters depth
• Weight: 527 grams

About this title

Synopsis

This book follows on from Natural Computing in Computational Finance  Volumes I, II and III.   As in the previous volumes of this series, the  book consists of a series of  chapters each of 

which was selected following a rigorous, peer-reviewed, selection process.  The chapters illustrate the application of a range of cutting-edge natural  computing and agent-based methodologies in computational finance and economics. 

The applications explored include  option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading,  corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation.  While describing cutting edge applications, the chapters are 

written so that they are accessible to a wide audience. Hence, they should be of interest  to academics, students and practitioners in the fields of computational finance and  economics.  

which was selected following a rigorous, peer-reviewed, selection process.  The chapters illustrate the application of a range of cutting-edge natural  computing and agent-based methodologies in computational finance and economics. 

The applications explored include  option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation.  While describing cutting edge applications, the chapters are 

written so that they are accessible to a wide audience. Hence, they should be of interest  to academics, students and practitioners in the fields of computational finance and  economics.  

The applications explored include  option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading,  corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation.  While describing cutting edge applications, the chapters are 

written so that they are accessible to a wide audience. Hence, they should be of interest  to academics, students and practitioners in the fields of computational finance and  economics.  

written so that they are accessible to a wide audience. Hence, they should be of interest  to academics, students and practitioners in the fields of computational finance and  economics.  

From the Back Cover

This book follows on from Natural Computing in Computational Finance Volumes I, II and III. As in the previous volumes of this series, the book consists of a series of chapters each of

which was selected following a rigorous, peer-reviewed, selection process. The chapters illustrate the application of a range of cutting-edge natural computing and agent-based methodologies in computational finance and economics.

The applications explored include option model calibration, financial trend reversal detection, enhanced indexation, algorithmic trading, corporate payout determination and agent-based modeling of liquidity costs, and trade strategy adaptation. While describing cutting edge applications, the chapters are

written so that they are accessible to a wide audience. Hence, they should be of interest to academics, students and practitioners in the fields of computational finance and economics.