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Synopsis
This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management.
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About the Author
Emmanuel JURCZENKO is Director of Graduate Studies and Professor of Finance at Glion Institute of Higher Education, Switzerland. Prior to this, he spent 13 years as Associate Professor of Finance at ESCP-Europe and worked for ABN-AMRO as Head of Quantitative Analysts where he was in charge of quantitative fund selection. His research focuses on portfolio construction in particular on risk budgeting, factor investing and machine learning estimation techniques.
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Machine Learning for Asset Management : New Developments and Financial Applications
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Machine Learning for Asset Management
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Machine Learning for Asset Management (Hardcover)
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Hardcover. Condition: new. Hardcover. This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9781786305442
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Machine Learning for Asset Management (Hardcover)
Published by
ISTE Ltd and John Wiley & Sons Inc, London, 2020
ISBN 10: 1786305445
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Hardcover. Condition: new. Hardcover. This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781786305442
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Machine Learning for Asset Management (Hardcover)
Published by
ISTE Ltd and John Wiley & Sons Inc, London, 2020
ISBN 10: 1786305445
ISBN 13: 9781786305442
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Hardcover. Condition: new. Hardcover. This new edited volume consists of a collection of original articles written by leading financial economists and industry experts in the area of machine learning for asset management. The chapters introduce the reader to some of the latest research developments in the area of equity, multi-asset and factor investing. Each chapter deals with new methods for return and risk forecasting, stock selection, portfolio construction, performance attribution and transaction costs modeling. This volume will be of great help to portfolio managers, asset owners and consultants, as well as academics and students who want to improve their knowledge of machine learning in asset management. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781786305442
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