A hybrid algorithm for constrained portfolio selection problems

Lwin, Khin T.; Qu, Rong

doi:10.1007/s10489-012-0411-7

Cited by 63 publications

(63 citation statements)

References 46 publications

(61 reference statements)

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“…The two objectives may conflict with each other, meaning that to improve one objective may weaken the other. There is no such a solution which is optimized in both objectives [33][34][35][36]. The optimal solution set for the bi-objective optimization problem is known as the Pareto-optimal set.…”

Section: Discussionmentioning

confidence: 99%

On minimizing coding operations in network coding based multicast: an evolutionary algorithm

Xing

Bai

et al. 2014

Appl Intell

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A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Abstract:In telecommunications networks, to enable a valid data transmission based on network coding, any intermediate node within a given network is allowed, if necessary, to perform coding operations. The more coding operations needed, the more coding resources consumed and thus the more computational overhead and transmission delay incurred. This paper investigates an efficient evolutionary algorithm to minimize the amount of coding operations required in network coding based multicast. Based on genetic algorithms, we adapt two extensions in the proposed evolutionary algorithm, namely a new crossover operator and a neighbourhood search operator, to effectively solve the highly complex problem being concerned.The new crossover is based on logic OR operations to each pair of selected parent individuals and the resulting offspring are more likely to become feasible. The aim of this operator is to intensify the search in regions with plenty of feasible individuals. The neighbourhood search consists of two moves which are based on greedy link removal and path reconstruction, respectively. Due to the specific problem feature, it is possible that each feasible individual corresponds to a number of, rather than a single, valid network coding based routing subgraphs. The neighbourhood search is applied to each feasible individual to find a fitter routing subgraph that consumes less coding resource. This operator not only improves solution quality but also accelerates the convergence.Experiments have been carried out on a number of fixed and randomly generated benchmark networks. The results demonstrate that with the two extensions, our evolutionary algorithm is effective and outperforms a number of state-of-the-art algorithms in terms of the ability of finding optimal solutions.

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Section: Discussionmentioning

confidence: 99%

On minimizing coding operations in network coding based multicast: an evolutionary algorithm

Xing

Bai

et al. 2014

Appl Intell

Self Cite

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“…Indeed, low and/or non-optimal performances on learning may arise with high-dimensional and complex data sets, such as those used in our investigation. A search algorithm such as SA may significantly reduce the number of variables to be considered by the RF model, thus improving the explanation success achieved (Seyedhosseini et al, 2016;Lwin & Qu, 2013;Wang et al, 2010). SA algorithm use is inspired by the controlled cooling processes of the metals.…”

Section: Rf and Sa Enhancementmentioning

confidence: 99%

The Determinants of Sovereign Bond Yields in the EMU: New Empirical Evidence

Altinbas¹,

Pacelli²,

Sica³

2018

IJEF

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This paper investigates the determinants of sovereign bond yields in the case of ten Economic and Monetary Union (EMU) countries (five core economies and five peripheral countries) for the period of [2001][2002][2003][2004][2005][2006][2007][2008][2009][2010][2011][2012][2013][2014][2015]. To this end, we carry out a two-step methodology based on (i) a principal component analysis of the countries' yields, which is aimed at splitting our sample into sub-periods, and (ii) a random forest model to investigate the determinants of bond yields in any identified sub-period enhanced with a variable selection process with simulated annealing. Our analysis indicates that macroeconomic fundamentals (especially the unemployment rate, the inflation rate and the government debt to the GDP change rate) are the main variables responsible for the sovereign bond yields in all the countries analyzed, both core and peripheral. In contrast, the bond yields do not seem to be intensively influenced by global indicators over the whole sampling period.

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“…However, they also propose a metaheuristic algorithm for solving larger instances, and employ an exact algorithm for checking the efficiency of the proposed metaheuristic. Lwin and Qu [2013] investigate an enriched version of the POSP including practical trading constraints such as the cardinality, floor, and ceiling constraints. For solving this problem, they derive a new hybrid algorithm that integrates a DE algorithm with another population based approach.…”

Section: Portfolio Optimization and Selection Problemmentioning

confidence: 99%

A Survey on Financial Applications of Metaheuristics

2017

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Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, etc. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and reallife combinatorial optimization problems that arise in a number of financial and banking activities. This paper reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. The paper also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.• Applied computing➝Operational research➝Decision analysis • Applied computing➝Law, social and behavioral sciences➝Economics.

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A hybrid algorithm for constrained portfolio selection problems

Cited by 63 publications

References 46 publications

On minimizing coding operations in network coding based multicast: an evolutionary algorithm

On minimizing coding operations in network coding based multicast: an evolutionary algorithm

The Determinants of Sovereign Bond Yields in the EMU: New Empirical Evidence

A Survey on Financial Applications of Metaheuristics

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