El artículo que has seleccionado ha finalizado, pero hemos encontrado algo similar.

Imagen 1 de 1

Foto genérica

Aprendizaje automático en precios de activos, tapa dura de Nagel, Stefan, totalmente nuevo, gratuito... - Imagen 1 de 1

Princeton Lectures in Finance Ser.: Machine Learning in Asset Pricing by Stefan Nagel (2021, Hardcover)

Sé el primero enescribir una opinión.

Great Book Prices Store (312909)
96,6% de votos positivos

Precio:

USD64,05

Aproximadamente57,54 EUR

+ USD21,50 de envío

Entrega prevista: lun. 21 oct. - lun. 4 nov.

Devoluciones:

30 días para devoluciones. El comprador paga el envío de la devolución.

Estado:

Nuevo

A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources.

Ver todos los detalles

Acerca de este artículo

Product Identifiers

Publisher
Princeton University Press
ISBN-10
0691218706
ISBN-13
9780691218700
eBay Product ID (ePID)
22050071606

Product Key Features

Number of Pages
160 Pages
Publication Name
Machine Learning in Asset Pricing
Language
English
Publication Year
2021
Subject
Finance / Financial Engineering, Finance / General
Type
Textbook
Author
Stefan Nagel
Subject Area
Business & Economics
Series
Princeton Lectures in Finance Ser.
Format
Hardcover

Dimensions

Item Height
0.8 in
Item Weight
14.5 Oz
Item Length
9.5 in
Item Width
6.5 in

Additional Product Features

Intended Audience
College Audience
LCCN
2022-275700
Reviews
"The book shows the advances Machine Learning offers for academic research. The book certainly makes a difference in the exploding literature on Machine Learning and I highly recommend it to all academics in finance." ---Thorsten Hens, Journal of Economics
Dewey Edition
23
Series Volume Number
1
Illustrated
Yes
Dewey Decimal
332.6
Synopsis
A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation., A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricingInvestors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machin, A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricingInvestors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. In this book, Stefan Nagel examines the promises and challenges of ML applications in asset pricing.Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, Nagel then discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets.Machine Learning in Asset Pricing presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.
LC Classification Number
HG4636

Comprar por categoría

Imagen 1 de 1

Princeton Lectures in Finance Ser.: Machine Learning in Asset Pricing by Stefan Nagel (2021, Hardcover)

Todos los anuncios de este producto

artículo 1 Machine Learning in Asset Pricing, Hardcover by Nagel, Stefan, Like New Used,...

artículo 2 Machine Learning in Asset Pricing, Hardcover by Nagel, Stefan, Brand New, Fre...

artículo 3 Machine Learning in Asset Pricing by Stefan Nagel: New

artículo 4 Machine Learning in Asset Pricing (Princeton Lectures in Finance, 1) by Nagel

artículo 5 Machine Learning in Asset Pricing by Stefan Nagel (English) Hardcover Book

Acerca de este artículo

Product Identifiers

Product Key Features

Dimensions

Additional Product Features

También te pueden interesar

Libros antiguos y de colección stefan zweig

Didácticos infantiles, lectura

Libros infantiles y juveniles, lectura

Didácticos infantiles, lectura, en inglés

Libros infantiles y juveniles, lectura, en inglés

Libros infantiles y juveniles didácticos e interactivos, lectura