Machine Learning For Financial Engineering
Published on 16. March 2012
Book
Hardback
260 pages
978-1-84816-813-8 (ISBN)
Description
This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment.The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and engineering.
More details
Series
Language
English
Place of publication
London
United Kingdom
Target group
College/higher education
Professional and scholarly
Researchers, academics and graduate students in artificial intelligence/machine learning, and mathematical finance/quantitative finance.
Dimensions
Height: 235 mm
Width: 157 mm
Thickness: 19 mm
Weight
535 gr
ISBN-13
978-1-84816-813-8 (9781848168138)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Content
On the History of the Growth Optimal Portfolio (M M Christensen); Empirical Log-Optimal Portfolio Selections: A Survey (L Gyorfi et al.); Log-Optimal Portfolio Selection with Proportional Transaction Costs (L Gyorfi & H Walk); Log-Optimal Portfolio with Short Selling and Leverage (M Horvath & A Urban); Nonparametric Sequential Prediction of Stationary Time Series (L Gyorfi & G Ottuscak); Empirical Pricing American Put Options (L Gyorfi & A Telcs).