國立虎尾科技大學 |

Optimizing optimization = the next generation of optimization applications and theory /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Optimizing optimization/ [edited by] Stephen Satchell.
Reminder of title:	the next generation of optimization applications and theory /
other author:	Satchell, S.
Published:	Amsterdam ;Academic Press, : c2010.,
Description:	1 online resource (xvi, 306 p.) :ill. :
Subject:	Portfolio management - Data processing. -
Online resource:	http://www.sciencedirect.com/science/book/9780123749529
ISBN:	9780123749529

Optimizing optimization = the next generation of optimization applications and theory /
Optimizing optimizationthe next generation of optimization applications and theory /[electronic resource] :[edited by] Stephen Satchell. - Amsterdam ;Academic Press,c2010. - 1 online resource (xvi, 306 p.) :ill. - Quantitative finance series. - Quantitative finance series..

Includes bibliographical references and index.

Optimizing Optimization -- Stephen Satchell -- Section 1: Practitioners and Products -- Chapter 1: Robust Portfolio Optimization Using Second Order Cone Programming -- Fiona Kolbert and Laurence Wormald -- Chapter 2: Novel Approaches to Portfolio Construction: Multiple Risk Models and Multi-Solution Generation -- Sebastian Ceria, Francis Margot, Anthony Renshaw, and Anureet Saxena -- Chapter 3: Bitter Lessons Learned from Practical Optimization or A Holding Hand Through the Dark Valley of Infeasibility -- Daryl Roxburgh, Katja Scherer, and Tim Matthews -- Chapter 4: The Windham Portfolio Advisor -- Mark Kritzman -- Section 2: Theory -- Chapter 5: Modeling, Estimation, and Optimization of Equity Portfolios with Heavy-tailed Distributions -- Amira Biglova, Sergio Ortobelli, Svetlozar Rachev, and Frank J. Fabozzi -- Chapter 6: Staying Ahead on Downside Risk -- Giuliano De Rossi -- Chapter 7: Optimization and Portfolio Selection -- Hal Forsey and Frank Sortino -- Chapter 8: Computing Optimal Mean/Downside Risk Frontiers: the Role of Ellipticity -- A.D. Hall and Stephen Satchell -- Chapter 9: Portfolio Optimization with 'Threshold Accepting': A Practical Guide -- Manfred Gilli and Enrico Schumann -- Chapter 10: Some Properties Averaging Simulated Optimization Methods -- J. Knight and Stephen Satchell -- Chapter 11: Heuristic Portfolio Optimization: Bayesian Updating with the Johnson Family of Distributions -- Richard Louth -- Chapter 12: More Than You Ever Wanted to Know about Conditional Value at Risk-Optimization -- Bernd Scherer.

Editor Stephen Satchell brings us a book that truly lives up to its title: optimizing optimization by taking the lessons learned about the failures of portfolio optimization from the credit crisis and collecting them into one book, providing a variety of perspectives from the leaders in both industry and academia on how to solve these problems both in theory and in practice. Industry leaders are invited to present chapters that explain how their new breed of optimization software addresses the faults of previous versions. Software vendors present their best of breed optimization software, demonstrating how it addresses the faults of the credit crisis. Cutting-edge academic articles complement the commercial applications to provide a well-rounded insight into the current landscape of portfolio optimization. Optimization is the holy grail of portfolio management, creating a portfolio in which return is highest in light of the risk the client is willing to take. Portfolio optimization has been done by computer modeling for over a decade, and several leading software companies make a great deal of money by selling optimizers to investment houses and hedge funds. Hedge funds in particular were enamored of heavily computational optimizing software, and many have been burned when this software did not perform as, er, expected during the market meltdown. The software providers are currently reworking their software to address any shortcomings that became apparent during the meltdown, and are eager for a forum to address their market and have the space to describe in detail how their new breed of software can manage not only the meltdown problems but also perform faster and better than ever before-that is, optimizing the optimizers!! In addition, there is a strong line of serious well respected research on portfolio optimization coming from the academic side of the finance world. Many different academic approaches have appeared toward optimization: some favor stochastic methods, others numerical methods, others heuristic methods. All focus on the same issues of optimizing performance at risk levels. This book will provide the forum that the software vendors are looking for to showcase their new breed of software. It will also provide a forum for the academics to showcase their latest research. It will be a must-read book for portfolio managers who need to know whether their current optimization software provider is up to snuff compared to the competition, whether they need to move to a competitor product, whether they need to be more aware of the cutting-edge academic research as well. Presents a unique "confrontation" between software engineers and academics Highlights a global view of common optimization issues Emphasizes the research and market challenges of optimization software while avoiding sales pitches Accentuates real applications, not laboratory results.

ISBN: 9780123749529

Standard No.: 9786612666087

Source: 176936:173366Elsevier Science & Technologyhttp://www.sciencedirect.comSubjects--Topical Terms:

672012
Portfolio management
--Data processing.Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: HG4529.5 / .O68 2010

Dewey Class. No.: 332.602853

Optimizing optimization = the next generation of optimization applications and theory /
LDR:06302cam 2200481Ia 4500 001 691327
003 OCoLC
005 20111122091029.0
006 m d
007 cr cn|||||||||
008 120103s2010 enka ob 001 0 eng d
019 $a 430842943 $a 643532545 $a 647911402 $a 663077442 $a 694147676
020 $a 9780123749529
020 $a 0123749522
020 $a 9780080959207 (electronic bk.)
020 $a 0080959202 (electronic bk.)
024 8 $a 9786612666087
029 1 $a HEBIS $b 22136725X
029 1 $a AU@ $b 000044902487
029 1 $a CDX $b 14358550
029 1 $a AU@ $b 000045964187
029 1 $a NZ1 $b 13642092
035 $a (OCoLC)528610139 $z (OCoLC)430842943 $z (OCoLC)643532545 $z (OCoLC)647911402 $z (OCoLC)663077442 $z (OCoLC)694147676
035 $a ocn528610139
037 $a 176936:173366 $b Elsevier Science & Technology $n http://www.sciencedirect.com
040 $a OPELS $b eng $c OPELS $d BTCTA $d BWX $d YDXCP $d CDX $d OCLCQ $d N$T $d E7B $d MERUC $d OCLCQ
049 $a TEFA
050 4 $a HG4529.5 $b .O68 2010
072 7 $a BUS $x 036000 $2 bisacsh
082 0 4 $a 332.602853 $2 22
245 0 0 $a Optimizing optimization $h [electronic resource] : $b the next generation of optimization applications and theory / $c [edited by] Stephen Satchell.
260 $a Amsterdam ; $a Boston : $b Academic Press, $c c2010.
300 $a 1 online resource (xvi, 306 p.) : $b ill.
490 1 $a Quantitative finance series
504 $a Includes bibliographical references and index.
505 0 $a Optimizing Optimization -- Stephen Satchell -- Section 1: Practitioners and Products -- Chapter 1: Robust Portfolio Optimization Using Second Order Cone Programming -- Fiona Kolbert and Laurence Wormald -- Chapter 2: Novel Approaches to Portfolio Construction: Multiple Risk Models and Multi-Solution Generation -- Sebastian Ceria, Francis Margot, Anthony Renshaw, and Anureet Saxena -- Chapter 3: Bitter Lessons Learned from Practical Optimization or A Holding Hand Through the Dark Valley of Infeasibility -- Daryl Roxburgh, Katja Scherer, and Tim Matthews -- Chapter 4: The Windham Portfolio Advisor -- Mark Kritzman -- Section 2: Theory -- Chapter 5: Modeling, Estimation, and Optimization of Equity Portfolios with Heavy-tailed Distributions -- Amira Biglova, Sergio Ortobelli, Svetlozar Rachev, and Frank J. Fabozzi -- Chapter 6: Staying Ahead on Downside Risk -- Giuliano De Rossi -- Chapter 7: Optimization and Portfolio Selection -- Hal Forsey and Frank Sortino -- Chapter 8: Computing Optimal Mean/Downside Risk Frontiers: the Role of Ellipticity -- A.D. Hall and Stephen Satchell -- Chapter 9: Portfolio Optimization with 'Threshold Accepting': A Practical Guide -- Manfred Gilli and Enrico Schumann -- Chapter 10: Some Properties Averaging Simulated Optimization Methods -- J. Knight and Stephen Satchell -- Chapter 11: Heuristic Portfolio Optimization: Bayesian Updating with the Johnson Family of Distributions -- Richard Louth -- Chapter 12: More Than You Ever Wanted to Know about Conditional Value at Risk-Optimization -- Bernd Scherer.
520 $a Editor Stephen Satchell brings us a book that truly lives up to its title: optimizing optimization by taking the lessons learned about the failures of portfolio optimization from the credit crisis and collecting them into one book, providing a variety of perspectives from the leaders in both industry and academia on how to solve these problems both in theory and in practice. Industry leaders are invited to present chapters that explain how their new breed of optimization software addresses the faults of previous versions. Software vendors present their best of breed optimization software, demonstrating how it addresses the faults of the credit crisis. Cutting-edge academic articles complement the commercial applications to provide a well-rounded insight into the current landscape of portfolio optimization. Optimization is the holy grail of portfolio management, creating a portfolio in which return is highest in light of the risk the client is willing to take. Portfolio optimization has been done by computer modeling for over a decade, and several leading software companies make a great deal of money by selling optimizers to investment houses and hedge funds. Hedge funds in particular were enamored of heavily computational optimizing software, and many have been burned when this software did not perform as, er, expected during the market meltdown. The software providers are currently reworking their software to address any shortcomings that became apparent during the meltdown, and are eager for a forum to address their market and have the space to describe in detail how their new breed of software can manage not only the meltdown problems but also perform faster and better than ever before-that is, optimizing the optimizers!! In addition, there is a strong line of serious well respected research on portfolio optimization coming from the academic side of the finance world. Many different academic approaches have appeared toward optimization: some favor stochastic methods, others numerical methods, others heuristic methods. All focus on the same issues of optimizing performance at risk levels. This book will provide the forum that the software vendors are looking for to showcase their new breed of software. It will also provide a forum for the academics to showcase their latest research. It will be a must-read book for portfolio managers who need to know whether their current optimization software provider is up to snuff compared to the competition, whether they need to move to a competitor product, whether they need to be more aware of the cutting-edge academic research as well. Presents a unique "confrontation" between software engineers and academics Highlights a global view of common optimization issues Emphasizes the research and market challenges of optimization software while avoiding sales pitches Accentuates real applications, not laboratory results.
588 $a Description based on print version record.
650 0 $a Portfolio management $x Data processing. $3 672012
650 0 $a Portfolio management $x Mathematical models. $3 557706
650 7 $a BUSINESS & ECONOMICS $x Investments & Securities $x General. $2 bisacsh $3 810833
655 4 $a Electronic books. $2 local $3 554714
700 1 $a Satchell, S. $q (Stephen) $3 812268
830 0 $a Quantitative finance series. $3 811405
856 4 0 $3 ScienceDirect $u http://www.sciencedirect.com/science/book/9780123749529
938 $a Baker and Taylor $b BTCP $n BK0008531681
938 $a YBP Library Services $b YANK $n 3095931
938 $a Coutts Information Services $b COUT $n 14358550 $c 141.95 USD
938 $a ebrary $b EBRY $n ebr10379017
994 $a C0 $b TEF