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Springer Machine Learning in Finance: Theory to Practice Book

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Description

Machine Learning in Finance: From Theory to Practice by Springer provides a unified treatment of machine learning methods applied to quantitative finance. This text connects machine learning with financial econometrics and discrete time stochastic control to help readers understand how theory and hypothesis tests inform algorithm selection for financial data modeling. As the finance industry shifts toward larger datasets and increased computational resources, machine learning has become an essential skillset. This book is specifically written for advanced graduate students looking to master the intersection of statistical disciplines and financial decision making. It bridges the gap between theoretical frameworks and practical application in modern quantitative finance.

Key Features

This Springer publication introduces machine learning methods specifically designed for applications in the finance industry.

The text provides a unified treatment of machine learning alongside financial econometrics and discrete time stochastic control.

Readers learn how theory and hypothesis tests inform the choice of specific algorithms for financial data modeling.

This book is written for advanced graduate students focusing on quantitative finance and decision making.

It addresses the growing need for machine learning skills driven by larger datasets and increased computational resources.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
02 July 2021
Listed Since
05 June 2021

Barcode

No barcode data available

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