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Online Portfolio Selection : Principles and Algorithms (Hardcover) (Bin Li & Steven C. H. Hoi)

Online Portfolio Selection : Principles and Algorithms (Hardcover) (Bin Li & Steven C. H. Hoi) - image 1 of 1

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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 (protoview.com)

This book investigates online portfolio selection (OLPS), which aims to efficiently allocate capital among a set of assets. The principles of OLPS mainly include follow the winner, follow the loser, and pattern matching based algorithms. Based on various existing algorithms, the authors designed four algorithms to advance the state of the art, which led to some very powerful tools for portfolio selection. Simulations using real market data are used and show that these strategies are effective and provide strong confidence for potential deployment.

Number of Pages: 210
Genre: Business + Money Management, Computers + Internet, Mathematics
Sub-Genre: Machine Theory, Finance, Probability + Statistics / General
Format: Hardcover
Publisher: Taylor & Francis
Author: Bin Li & Steven C. H. Hoi
Language: English
Street Date: November 5, 2015
TCIN: 23999758
UPC: 9781482249637
Item Number (DPCI): 247-50-8110
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