Natural Computing in Computational Finance : Volume 4

Anthony Brabazon

ISBN 10: 3662519984 / ISBN 13: 9783662519981

Published by Springer, Berlin, Springer, 2016

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 # 9783662519981

Bibliographic Details

• Title: Natural Computing in Computational Finance : Volume 4
• Publisher: Springer, Berlin, Springer
• Publication Date: 2016
• Language: English
• ISBN 10: 3662519984
• ISBN 13: 9783662519981
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 11D millimeters 235 millimeters width by 155 millimeters height by 11 millimeters depth
• Weight: 332 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.