Online portfolio selection: principles and algorithms
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 pr...
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton, FL
CRC Press
[2016]
|
| Schlagwörter: | |
| Links: | https://learning.oreilly.com/library/view/-/9781482249644/?ar |
| Zusammenfassung: | 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. Readers are encouraged to visit the authors' website for updates: http://olps.stevenhoi.org. |
| Beschreibung: | Includes bibliographical references. - Online resource; title from digital title page (viewed on November 25, 2015) |
| Umfang: | 1 Online-Ressource |
| ISBN: | 9781482249644 1482249642 1138894109 9781138894105 9781351229180 1351229184 |
Internformat
MARC
| LEADER | 00000cam a22000002 4500 | ||
|---|---|---|---|
| 001 | ZDB-30-ORH-04759974X | ||
| 003 | DE-627-1 | ||
| 005 | 20240228120002.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 191023s2016 xx |||||o 00| ||eng c | ||
| 020 | |a 9781482249644 |c electronic bk. |9 978-1-4822-4964-4 | ||
| 020 | |a 1482249642 |c electronic bk. |9 1-4822-4964-2 | ||
| 020 | |a 1138894109 |9 1-138-89410-9 | ||
| 020 | |a 9781138894105 |9 978-1-138-89410-5 | ||
| 020 | |a 9781351229180 |c electronic bk. |9 978-1-351-22918-0 | ||
| 020 | |a 1351229184 |c electronic bk. |9 1-351-22918-4 | ||
| 035 | |a (DE-627-1)04759974X | ||
| 035 | |a (DE-599)KEP04759974X | ||
| 035 | |a (ORHE)9781482249644 | ||
| 035 | |a (DE-627-1)04759974X | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rda | ||
| 041 | |a eng | ||
| 072 | 7 | |a BUS |2 bisacsh | |
| 082 | 0 | |a 332.6 |2 23 | |
| 100 | 1 | |a Li, Bin |e VerfasserIn |4 aut | |
| 245 | 1 | 0 | |a Online portfolio selection |b principles and algorithms |c Bin Li and Steven C.H. Hoi |
| 264 | 1 | |a Boca Raton, FL |b CRC Press |c [2016] | |
| 264 | 4 | |c ©20 | |
| 300 | |a 1 Online-Ressource | ||
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Includes bibliographical references. - Online resource; title from digital title page (viewed on November 25, 2015) | ||
| 520 | |a 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. Readers are encouraged to visit the authors' website for updates: http://olps.stevenhoi.org. | ||
| 630 | 2 | 0 | |a MATLAB |
| 650 | 0 | |a Portfolio management | |
| 650 | 0 | |a Investments | |
| 650 | 4 | |a MATLAB | |
| 650 | 4 | |a Gestion de portefeuille | |
| 650 | 4 | |a Investissements | |
| 650 | 4 | |a portfolios (financial records) | |
| 650 | 4 | |a BUSINESS & ECONOMICS ; Finance | |
| 650 | 4 | |a Investments | |
| 650 | 4 | |a Portfolio management | |
| 700 | 1 | |a Hoi, Steven C. H. |e VerfasserIn |4 aut | |
| 776 | 1 | |z 9781482249637 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |z 9781482249637 |
| 966 | 4 | 0 | |l DE-91 |p ZDB-30-ORH |q TUM_PDA_ORH |u https://learning.oreilly.com/library/view/-/9781482249644/?ar |m X:ORHE |x Aggregator |z lizenzpflichtig |3 Volltext |
| 912 | |a ZDB-30-ORH | ||
| 912 | |a ZDB-30-ORH | ||
| 951 | |a BO | ||
| 912 | |a ZDB-30-ORH | ||
| 049 | |a DE-91 | ||
Datensatz im Suchindex
| DE-BY-TUM_katkey | ZDB-30-ORH-04759974X |
|---|---|
| _version_ | 1821494875097399296 |
| adam_text | |
| any_adam_object | |
| author | Li, Bin Hoi, Steven C. H. |
| author_facet | Li, Bin Hoi, Steven C. H. |
| author_role | aut aut |
| author_sort | Li, Bin |
| author_variant | b l bl s c h h sch schh |
| building | Verbundindex |
| bvnumber | localTUM |
| collection | ZDB-30-ORH |
| ctrlnum | (DE-627-1)04759974X (DE-599)KEP04759974X (ORHE)9781482249644 |
| dewey-full | 332.6 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 332 - Financial economics |
| dewey-raw | 332.6 |
| dewey-search | 332.6 |
| dewey-sort | 3332.6 |
| dewey-tens | 330 - Economics |
| discipline | Wirtschaftswissenschaften |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03720cam a22005772 4500</leader><controlfield tag="001">ZDB-30-ORH-04759974X</controlfield><controlfield tag="003">DE-627-1</controlfield><controlfield tag="005">20240228120002.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">191023s2016 xx |||||o 00| ||eng c</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781482249644</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">978-1-4822-4964-4</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1482249642</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">1-4822-4964-2</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1138894109</subfield><subfield code="9">1-138-89410-9</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781138894105</subfield><subfield code="9">978-1-138-89410-5</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781351229180</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">978-1-351-22918-0</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1351229184</subfield><subfield code="c">electronic bk.</subfield><subfield code="9">1-351-22918-4</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)04759974X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KEP04759974X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ORHE)9781482249644</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627-1)04759974X</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">BUS</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">332.6</subfield><subfield code="2">23</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Li, Bin</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Online portfolio selection</subfield><subfield code="b">principles and algorithms</subfield><subfield code="c">Bin Li and Steven C.H. Hoi</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boca Raton, FL</subfield><subfield code="b">CRC Press</subfield><subfield code="c">[2016]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©20</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references. - Online resource; title from digital title page (viewed on November 25, 2015)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. Readers are encouraged to visit the authors' website for updates: http://olps.stevenhoi.org.</subfield></datafield><datafield tag="630" ind1="2" ind2="0"><subfield code="a">MATLAB</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Portfolio management</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Investments</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">MATLAB</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Gestion de portefeuille</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Investissements</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">portfolios (financial records)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">BUSINESS & ECONOMICS ; Finance</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Investments</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Portfolio management</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hoi, Steven C. H.</subfield><subfield code="e">VerfasserIn</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="1" ind2=" "><subfield code="z">9781482249637</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="z">9781482249637</subfield></datafield><datafield tag="966" ind1="4" ind2="0"><subfield code="l">DE-91</subfield><subfield code="p">ZDB-30-ORH</subfield><subfield code="q">TUM_PDA_ORH</subfield><subfield code="u">https://learning.oreilly.com/library/view/-/9781482249644/?ar</subfield><subfield code="m">X:ORHE</subfield><subfield code="x">Aggregator</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">BO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-ORH</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91</subfield></datafield></record></collection> |
| id | ZDB-30-ORH-04759974X |
| illustrated | Not Illustrated |
| indexdate | 2025-01-17T11:21:19Z |
| institution | BVB |
| isbn | 9781482249644 1482249642 1138894109 9781138894105 9781351229180 1351229184 |
| language | English |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource |
| psigel | ZDB-30-ORH TUM_PDA_ORH ZDB-30-ORH |
| publishDate | 2016 |
| publishDateSearch | 2016 |
| publishDateSort | 2016 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Li, Bin VerfasserIn aut Online portfolio selection principles and algorithms Bin Li and Steven C.H. Hoi Boca Raton, FL CRC Press [2016] ©20 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Includes bibliographical references. - Online resource; title from digital title page (viewed on November 25, 2015) 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. Readers are encouraged to visit the authors' website for updates: http://olps.stevenhoi.org. MATLAB Portfolio management Investments Gestion de portefeuille Investissements portfolios (financial records) BUSINESS & ECONOMICS ; Finance Hoi, Steven C. H. VerfasserIn aut 9781482249637 Erscheint auch als Druck-Ausgabe 9781482249637 |
| spellingShingle | Li, Bin Hoi, Steven C. H. Online portfolio selection principles and algorithms MATLAB Portfolio management Investments Gestion de portefeuille Investissements portfolios (financial records) BUSINESS & ECONOMICS ; Finance |
| title | Online portfolio selection principles and algorithms |
| title_auth | Online portfolio selection principles and algorithms |
| title_exact_search | Online portfolio selection principles and algorithms |
| title_full | Online portfolio selection principles and algorithms Bin Li and Steven C.H. Hoi |
| title_fullStr | Online portfolio selection principles and algorithms Bin Li and Steven C.H. Hoi |
| title_full_unstemmed | Online portfolio selection principles and algorithms Bin Li and Steven C.H. Hoi |
| title_short | Online portfolio selection |
| title_sort | online portfolio selection principles and algorithms |
| title_sub | principles and algorithms |
| topic | MATLAB Portfolio management Investments Gestion de portefeuille Investissements portfolios (financial records) BUSINESS & ECONOMICS ; Finance |
| topic_facet | MATLAB Portfolio management Investments Gestion de portefeuille Investissements portfolios (financial records) BUSINESS & ECONOMICS ; Finance |
| work_keys_str_mv | AT libin onlineportfolioselectionprinciplesandalgorithms AT hoistevench onlineportfolioselectionprinciplesandalgorithms |