國立虎尾科技大學 |

Lagrangian relaxation approaches to cardinality constrained portfolio selection.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Lagrangian relaxation approaches to cardinality constrained portfolio selection./
Author:	Wu, Dexiang.
Description:	1 online resource (188 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-01(E), Section: B.
Contained By:	Dissertation Abstracts International78-01B(E).
Subject:	Operations research. -
Online resource:	click for full text (PQDT)
ISBN:	9781339963051

Lagrangian relaxation approaches to cardinality constrained portfolio selection.
Wu, Dexiang.

Lagrangian relaxation approaches to cardinality constrained portfolio selection. - 1 online resource (188 pages)

Source: Dissertation Abstracts International, Volume: 78-01(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Portfolio selection with cardinality constraint is a process that creates a strict subset of assets from a large selection pool. The advantage of cardinality constraint is that fewer assets can reduce transaction costs and complexity of asset management. Also, this type of constraint can be used to mimic a benchmark portfolio (index) such as S&P 500. In this dissertation we study two different cardinality constrained portfolio selection problems, known as Index Tracking and Financial Planning.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781339963051Subjects--Topical Terms:

573517
Operations research.
Index Terms--Genre/Form:

554714
Electronic books.

Lagrangian relaxation approaches to cardinality constrained portfolio selection.
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245 1 0 $a Lagrangian relaxation approaches to cardinality constrained portfolio selection.
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500 $a Source: Dissertation Abstracts International, Volume: 78-01(E), Section: B.
500 $a Adviser: Roy H. Kwon.
502 $a Thesis (Ph.D.) $c University of Toronto (Canada) $d 2016.
504 $a Includes bibliographical references
520 $a Portfolio selection with cardinality constraint is a process that creates a strict subset of assets from a large selection pool. The advantage of cardinality constraint is that fewer assets can reduce transaction costs and complexity of asset management. Also, this type of constraint can be used to mimic a benchmark portfolio (index) such as S&P 500. In this dissertation we study two different cardinality constrained portfolio selection problems, known as Index Tracking and Financial Planning.
520 $a Index Tracking is a typical application of the cardinality constrained portfolio selection process and has attracted much attention from portfolio managers. However, replicating unpredictable market indices using limited available resource requires advanced modelling and optimization techniques in practice. This thesis aims to qualitatively investigate and analyze different types of index tracking problems and the associated optimal strategies.
520 $a Firstly, we construct the tracking portfolio via a constrained clustering approach which considers various practical aspects such as transaction costs, turnover, and sector limits constraints. We show that the portfolio allocation can diversify between different sectors and reduce the portfolio risk fairly well. Next we address a cardinality constrained Financial Planning problem through Stochastic Mixed Integer Programming and extend the network flow structured framework to index tracking problem. Finally, we incorporate the cardinality restriction to a classical mean-variance based tracking model and build the robust counterpart via Robust Optimization.
520 $a All developed models demand problem solvability due to the rapid increase in the number of variables and constraints for tracking real indices such as S&P 500. We design three dual decomposition algorithms, which allow different specific heuristics to be embedded, to quickly obtain high quality solutions for associated models. For example, Tabu Search was applied to solve the scenario sub-problems to speed up the Progressive Hedging algorithm for cardinality constrained financial planning problems. Our designed models are general enough to extend to many other management applications, and our accompanied decomposition algorithms are efficient enough to handle the cardinality constraint in these problems. The generated portfolios illustrate the effectiveness of our selection technologies and designed algorithms in terms of different performance metrics with respect to the market.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Operations research. $3 573517
650 4 $a Mathematics. $3 527692
650 4 $a Computer science. $3 573171
650 4 $a Finance. $3 559073
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710 2 $a University of Toronto (Canada). $b Mechanical and Industrial Engineering. $3 845502
773 0 $t Dissertation Abstracts International $g 78-01B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10140910 $z click for full text (PQDT)