The Resource Online portfolio selection : principles and algorithms, Bin Li and Steven Chu Hong Hoi
Online portfolio selection : principles and algorithms, Bin Li and Steven Chu Hong Hoi
Resource Information
The item Online portfolio selection : principles and algorithms, Bin Li and Steven Chu Hong Hoi represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.This item is available to borrow from 1 library branch.
Resource Information
The item Online portfolio selection : principles and algorithms, Bin Li and Steven Chu Hong Hoi represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in European University Institute.
This item is available to borrow from 1 library branch.
- Summary
- With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: Introduce OLPS and formulate OLPS as a sequential decision task / Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning / Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques / Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art / Investigate possible future directions. Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment. --
- Language
- eng
- Label
- Online portfolio selection : principles and algorithms
- Title
- Online portfolio selection
- Title remainder
- principles and algorithms
- Statement of responsibility
- Bin Li and Steven Chu Hong Hoi
- Language
- eng
- Summary
- With the aim to sequentially determine optimal allocations across a set of assets, Online Portfolio Selection (OLPS) has significantly reshaped the financial investment landscape. Online Portfolio Selection: Principles and Algorithms supplies a comprehensive survey of existing OLPS principles and presents a collection of innovative strategies that leverage machine learning techniques for financial investment. The book presents four new algorithms based on machine learning techniques that were designed by the authors, as well as a new back-test system they developed for evaluating trading strategy effectiveness. The book uses simulations with real market data to illustrate the trading strategies in action and to provide readers with the confidence to deploy the strategies themselves. The book is presented in five sections that: Introduce OLPS and formulate OLPS as a sequential decision task / Present key OLPS principles, including benchmarks, follow the winner, follow the loser, pattern matching, and meta-learning / Detail four innovative OLPS algorithms based on cutting-edge machine learning techniques / Provide a toolbox for evaluating the OLPS algorithms and present empirical studies comparing the proposed algorithms with the state of the art / Investigate possible future directions. Complete with a back-test system that uses historical data to evaluate the performance of trading strategies, as well as MATLAB® code for the back-test systems, this book is an ideal resource for graduate students in finance, computer science, and statistics. It is also suitable for researchers and engineers interested in computational investment. --
- Assigning source
- Provided by Publisher
- Cataloging source
- CDX
- http://library.link/vocab/creatorName
- Li, Bin
- Illustrations
- illustrations
- Index
- index present
- Literary form
- non fiction
- Nature of contents
- bibliography
- http://library.link/vocab/relatedWorkOrContributorName
- Hoi, Steven C. H
- http://library.link/vocab/subjectName
-
- Portfolio management
- Investments
- Label
- Online portfolio selection : principles and algorithms, Bin Li and Steven Chu Hong Hoi
- Bibliography note
- Includes bibliographical references and index
- Carrier category
- volume
- Carrier category code
-
- nc
- Carrier MARC source
- rdacarrier.
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent.
- Control code
- FIEb17789692
- Dimensions
- 24 cm.
- Extent
- 232 pages
- Isbn
- 9781482249637
- Media category
- unmediated
- Media MARC source
- rdamedia.
- Media type code
-
- n
- Other physical details
- illustrations (black and white)
- System control number
- (OCoLC)1062282835
- Label
- Online portfolio selection : principles and algorithms, Bin Li and Steven Chu Hong Hoi
- Bibliography note
- Includes bibliographical references and index
- Carrier category
- volume
- Carrier category code
-
- nc
- Carrier MARC source
- rdacarrier.
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent.
- Control code
- FIEb17789692
- Dimensions
- 24 cm.
- Extent
- 232 pages
- Isbn
- 9781482249637
- Media category
- unmediated
- Media MARC source
- rdamedia.
- Media type code
-
- n
- Other physical details
- illustrations (black and white)
- System control number
- (OCoLC)1062282835
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.library.eui.eu/portal/Online-portfolio-selection--principles-and/3JUNAj_Wu-Y/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.library.eui.eu/portal/Online-portfolio-selection--principles-and/3JUNAj_Wu-Y/">Online portfolio selection : principles and algorithms, Bin Li and Steven Chu Hong Hoi</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.library.eui.eu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.library.eui.eu/">European University Institute</a></span></span></span></span></div>
