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Machine Learning for Financial Engineering Cover Image

E-book

Machine Learning for Financial Engineering

Gyorfi, Laszlo. (Author). Ottucsak, Gyorgy. (Added Author). Walk, Harro. (Added Author).

Summary: 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 eng.

Record details

ISBN: 1848168136
ISBN: 9781848168138
ISBN: 1848168144 (electronic bk.)
ISBN: 9781848168145 (electronic bk.)
Physical Description: 1 online resource (261 p.)
remote
electronic resource
Publisher: Singapore : World Scientific, 2012.

Content descriptions

General Note:	CatBulkString:jan.10.13 Multi-User. CatMonthString:jan.13 5.4. Universally Consistent Predictions: Unbounded Y.
Formatted Contents Note:	5. Nonparametric Sequential Prediction of Stationary Time Series L. Gyorfi and Gy. Ottucsak5.1. Introduction; 5.2. Nonparametric Regression Estimation; 5.2.1. The Regression Problem; 5.2.2. Regression Function Estimation and L2 Error; 5.2.3. Partitioning Estimate; 5.2.4. Kernel Estimate; 5.2.5. Nearest-Neighbor Estimate; 5.2.6. Empirical Error Minimization; 5.3. Universally Consistent Predictions: Bounded Y; 5.3.1. Partition-Based Prediction Strategies; 5.3.2. Kernel-Based Prediction Strategies; 5.3.3. Nearest-Neighbor-Based Prediction Strategy; 5.3.4. Generalized Linear Estimates.
Terms Governing Use and Reproduction Note:	Access restricted by subscription. Access requires VIU IP addresses and is restricted to VIU students, faculty and staff.
Source of Description Note:	Description based on print version record.