Description: Online Portfolio Selection : Principles and Algorithms, Hardcover by Li, Bin; Hoi, Steven C. H., ISBN 1482249634, ISBN-13 9781482249637, Brand New, Free shipping in the US This self-contained text on online portfolio selection (OLPS) is intended for graduate students in finance, computer science, and statistics, as well as researchers and engineers interested in computational investment. Coverage encompasses fundamentals of OLPS for financial investments and machine learning techniques for financial investment. Principles discussed include follow the winner, follow the loser, pattern matching, and meta-learning. Examples with simulated data are used demonstrate trading strategies. The text provides four original algorithms and a backtest system for determining how well trading strategies work, as well as empirical results on evaluation of cumulative wealth, risk and risk-adjusted return, and parameter sensitivity. Numerous b&w charts and diagrams are included. About 60 pages of appendices offer code and instructions for OLPS, proofs and derivations, supplementary data, and portfolio statistics. A companion web site is available. Annotation ©2016 Ringgold, Inc., Portland, OR ()
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Book Title: Online Portfolio Selection : Principles and Algorithms
Number of Pages: 212 Pages
Publication Name: Online Portfolio Selection : Principles and Algorithms
Language: English
Publisher: CRC Press LLC
Publication Year: 2015
Item Height: 0.7 in
Subject: Machine Theory, Investments & Securities / Portfolio Management, Probability & Statistics / General, Finance / General
Type: Textbook
Item Weight: 16.8 Oz
Subject Area: Mathematics, Computers, Business & Economics
Item Length: 9.5 in
Author: Steven Chu Hong Hoi, Bin Li
Item Width: 6.4 in
Format: Hardcover
