Machine Learning For Financial Engineering
Author:   Laszlo Gyorfi (Budapest Univ Of Technology & Economics, Hungary) ,  Gyorgy Ottucsak (Budapest Univ Of Technology & Economics, Hungary) ,  Harro Walk (Univ Stuttgart, Germany)
Publisher:   Imperial College Press
Volume:   8
ISBN:  

9781848168138


Pages:   260
Publication Date:   16 March 2012
Format:   Hardback
Availability:   In Print   Availability explained
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Machine Learning For Financial Engineering


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Overview

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.

Full Product Details

Author:   Laszlo Gyorfi (Budapest Univ Of Technology & Economics, Hungary) ,  Gyorgy Ottucsak (Budapest Univ Of Technology & Economics, Hungary) ,  Harro Walk (Univ Stuttgart, Germany)
Publisher:   Imperial College Press
Imprint:   Imperial College Press
Volume:   8
Dimensions:   Width: 15.20cm , Height: 2.00cm , Length: 22.90cm
Weight:   0.499kg
ISBN:  

9781848168138


ISBN 10:   1848168136
Pages:   260
Publication Date:   16 March 2012
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

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).

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