334,52 €
371,69 €
-10% with code: EXTRA
Machine Learning for Asset Management
Machine Learning for Asset Management
334,52
371,69 €
  • We will send in 10–14 business days.
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 wi…
  • Publisher:
  • ISBN-10: 1786305445
  • ISBN-13: 9781786305442
  • Format: 15.5 x 23.6 x 2.8 cm, hardcover
  • Language: English
  • SAVE -10% with code: EXTRA

Machine Learning for Asset Management (e-book) (used book) | bookbook.eu

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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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  • Author: Emmanuel Jurczenko
  • Publisher:
  • ISBN-10: 1786305445
  • ISBN-13: 9781786305442
  • Format: 15.5 x 23.6 x 2.8 cm, hardcover
  • Language: English English

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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