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Relevant bibliographies by topics / Portfolio selection optimization

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Academic literature on the topic 'Portfolio selection optimization'

Author: Grafiati

Published: 4 September 2021

Last updated: 1 February 2022

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Journal articles on the topic "Portfolio selection optimization"

1

Mercurio, Peter Joseph, Yuehua Wu, and Hong Xie. "Option Portfolio Selection with Generalized Entropic Portfolio Optimization." Entropy 22, no. 8 (2020): 805. http://dx.doi.org/10.3390/e22080805.

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In this third and final paper of our series on the topic of portfolio optimization, we introduce a further generalized portfolio selection method called generalized entropic portfolio optimization (GEPO). GEPO extends discrete entropic portfolio optimization (DEPO) to include intervals of continuous returns, with direct application to a wide range of option strategies. This lays the groundwork for an adaptable optimization framework that can accommodate a wealth of option portfolios, including popular strategies such as covered calls, married puts, credit spreads, straddles, strangles, butterfly spreads, and even iron condors. These option strategies exhibit mixed returns: a combination of discrete and continuous returns with performance best measured by portfolio growth rate, making entropic portfolio optimization an ideal method for option portfolio selection. GEPO provides the mathematical tools to select efficient option portfolios based on their growth rate and relative entropy. We provide an example of GEPO applied to real market option portfolio selection and demonstrate how GEPO outperforms traditional Kelly criterion strategies.

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Berger, Theo, and Christian Fieberg. "On portfolio optimization." Journal of Risk Finance 17, no. 3 (2016): 295–309. http://dx.doi.org/10.1108/jrf-09-2015-0094.

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Purpose The purpose of this paper is to show how investors can incorporate the multi-scale nature of asset and factor returns into their portfolio decisions and to evaluate the out-of-sample performance of such strategies. Design/methodology/approach The authors decompose daily return series of common risk factors and of all stocks listed in the Dow Jones Industrial Index (DJI) from 2000 to 2015 into different time scales to separate short-term noise from long-run trends. Then, the authors apply various (multi-scale) factor models to determine variance-covariance matrices which are used for minimum variance portfolio selection. Finally, the portfolios are evaluated by their out-of-sample performance. Findings The authors find that portfolios which are constructed on variance-covariance matrices stemming from multi-scale factor models outperform portfolio allocations which do not take the multi-scale nature of asset and factor returns into account. Practical implications The results of this paper provide evidence that accounting for the multi-scale nature of return distributions in portfolio decisions might be a promising approach from a portfolio performance perspective. Originality/value The authors demonstrate how investors can incorporate the multi-scale nature of returns into their portfolio decisions by applying wavelet filter techniques.

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Glensk, Barbara, and Reinhard Madlener. "Fuzzy Portfolio Optimization of Power Generation Assets." Energies 11, no. 11 (2018): 3043. http://dx.doi.org/10.3390/en11113043.

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Fuzzy theory is proposed as an alternative to the probabilistic approach for assessing portfolios of power plants, in order to capture the complex reality of decision-making processes. This paper presents different fuzzy portfolio selection models, where the rate of returns as well as the investor’s aspiration levels of portfolio return and risk are regarded as fuzzy variables. Furthermore, portfolio risk is defined as a downside risk, which is why a semi-mean-absolute deviation portfolio selection model is introduced. Finally, as an illustration, the models presented are applied to a selection of power generation mixes. The efficient portfolio results show that the fuzzy portfolio selection models with different definitions of membership functions as well as the semi-mean-absolute deviation model perform better than the standard mean-variance approach. Moreover, introducing membership functions for the description of investors’ aspiration levels for the expected return and risk shows how the knowledge of experts, and investors’ subjective opinions, can be better integrated in the decision-making process than with probabilistic approaches.

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Kim, Namhyoung, and Suvrit Sra. "Portfolio Optimization with Groupwise Selection." Industrial Engineering and Management Systems 13, no. 4 (2014): 442–48. http://dx.doi.org/10.7232/iems.2014.13.4.442.

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Nystrup, Peter, Erik Lindström, and Henrik Madsen. "Hyperparameter Optimization for Portfolio Selection." Journal of Financial Data Science 2, no. 3 (2020): 40–54. http://dx.doi.org/10.3905/jfds.2020.1.035.

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Bjerring, Thomas Trier, Omri Ross, and Alex Weissensteiner. "Feature selection for portfolio optimization." Annals of Operations Research 256, no. 1 (2016): 21–40. http://dx.doi.org/10.1007/s10479-016-2155-y.

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Jena, R. K. "Extended Mean - Variance Portfolio Optimization Model: A Comparative Study Among Swarm Intelligence Algorithms." International Journal of Accounting and Financial Reporting 9, no. 2 (2019): 184. http://dx.doi.org/10.5296/ijafr.v9i2.14601.

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Portfolio optimization is one of the important issues in the effective management of investment. There is plenty of research in the literature addressing these issues. Markowitz’s primary portfolio selection model is a more suitable method to solve the model for obtaining fairly optimum portfolios. But, the problem of portfolio optimization is multi-objective in nature that aims at simultaneously maximizing the expected return of the portfolio and minimizing portfolio risk. The computational complexity increases with an increase in the total number of available assets. Therefore heuristic methods are more suitable for portfolio optimization in compare to deterministic methods. This research compares three well-known swarm intelligence algorithms (e.g. Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC)) for portfolio optimization. The Sharpe ratio was used as one of the important criteria for this comparison. PSO outperformed other algorithms in portfolio optimization experiments. The results were also showed that the portfolios which were made of monthly data had performed better than the yearly data.

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Mercurio, Peter Joseph, Yuehua Wu, and Hong Xie. "Portfolio Optimization for Binary Options Based on Relative Entropy." Entropy 22, no. 7 (2020): 752. http://dx.doi.org/10.3390/e22070752.

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The portfolio optimization problem generally refers to creating an investment portfolio or asset allocation that achieves an optimal balance of expected risk and return. These portfolio returns are traditionally assumed to be continuous random variables. In An Entropy-Based Approach to Portfolio Optimization, we introduced a novel non-parametric optimization method based on Shannon entropy, called return-entropy portfolio optimization (REPO), which offers a simple and fast optimization algorithm for assets with continuous returns. Here, in this paper, we would like to extend the REPO approach to the optimization problem for assets with discrete distributed returns, such as those from a Bernoulli distribution like binary options. Under a discrete probability distribution, portfolios of binary options can be viewed as repeated short-term investments with an optimal buy/sell strategy or general betting strategy. Upon the outcome of each contract, the portfolio incurs a profit (success) or loss (failure). This is similar to a series of gambling wagers. Portfolio selection under this setting can be formulated as a new optimization problem called discrete entropic portfolio optimization (DEPO). DEPO creates optimal portfolios for discrete return assets based on expected growth rate and relative entropy. We show how a portfolio of binary options provides an ideal general setting for this kind of portfolio selection. As an example we apply DEPO to a portfolio of short-term foreign exchange currency pair binary options from the NADEX exchange platform and show how it outperforms leading Kelly criterion strategies. We also provide an additional example of a gambling application using a portfolio of sports bets over the course of an NFL season and present the advantages of DEPO over competing Kelly criterion strategies.

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Li, Longqing. "Simulation-Based Optimal Portfolio Selection Strategy—Evidence from Asian Markets." Applied Economics and Finance 5, no. 5 (2018): 1. http://dx.doi.org/10.11114/aef.v5i4.3376.

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Recently portfolio optimization has become widely popular in risk management, and the common practice is to use mean-variance or Value-at-Risk (VaR), despite the VaR being incoherent risk measure because of the lack of subadditivity. This has led to the emergence of the conditional value-at-risk (CVaR) approach, consequently, a gradual development of mean-CVaR portfolio optimization. To seek an optimal portfolio selection strategy and increase the robustness of the result, the paper studies the performance of portfolio optimization in Asian markets using a Monte-Carlo simulation tool, creates a variety of randomly selected portfolios that consists of Asian ADRs listed in NYSE from 2011 to 2016, and applies both optimization frameworks with different skewed fat-tailed distributions, including the Generalized Hyperbolic (GH) and skewed-T distribution. The main result shows that the Generalized Hyperbolic distribution produces the lowest risk under a given rate of return, while the skewed-T distribution creates a diversification allocation outcome similar to that of historical simulation.

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Li, Longqing. "Simulation-Based Optimal Portfolio Selection Strategy—Evidence from Asian Markets." Applied Economics and Finance 5, no. 5 (2018): 1. http://dx.doi.org/10.11114/aef.v5i5.3376.

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Recently portfolio optimization has become widely popular in risk management, and the common practice is to use mean-variance or Value-at-Risk (VaR), despite the VaR being incoherent risk measure because of the lack of subadditivity. This has led to the emergence of the conditional value-at-risk (CVaR) approach, consequently, a gradual development of mean-CVaR portfolio optimization. To seek an optimal portfolio selection strategy and increase the robustness of the result, the paper studies the performance of portfolio optimization in Asian markets using a Monte-Carlo simulation tool, creates a variety of randomly selected portfolios that consists of Asian ADRs listed in NYSE from 2011 to 2016, and applies both optimization frameworks with different skewed fat-tailed distributions, including the Generalized Hyperbolic (GH) and skewed-T distribution. The main result shows that the Generalized Hyperbolic distribution produces the lowest risk under a given rate of return, while the skewed-T distribution creates a diversification allocation outcome similar to that of historical simulation.

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Dissertations / Theses on the topic "Portfolio selection optimization"

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Puhle, Michael. "Bond portfolio optimization." Berlin Heidelberg Springer, 2007. http://d-nb.info/985928115/04.

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Müller, Stephan. "Constrained portfolio optimization /." [S.l.] : [s.n.], 2005. http://aleph.unisg.ch/hsgscan/hm00133325.pdf.

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SCHLITTLER, JOAO GABRIEL FELIZARDO S. "PORTFOLIO SELECTION VIA DATA-DRIVEN DISTRIBUTIONALLY ROBUST OPTIMIZATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2018. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=36002@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>Otimização de portfólio tradicionalmente assume ter conhecimento da distribuição de probabilidade dos retornos ou pelo menos algum dos seus momentos. No entanto, é sabido que a distribuição de probabilidade dos retornos muda com frequência ao longo do tempo, tornando difícil a utilização prática de modelos puramente estatísticos, que confiam indubitavelmente em uma distribuição estimada. Em contrapartida, otimização robusta considera um completo desconhecimento da distribuição dos retornos, e por isto, buscam uma solução ótima para todas as realizações possíveis dentro de um conjunto de incerteza dos retornos. Mais recentemente na literatura, técnicas de distributionally robust optimization permitem lidar com a ambiguidade com relação à distribuição dos retornos. No entanto essas técnicas dependem da construção do conjunto de ambiguidade, ou seja, distribuições de probabilidade a serem consideradas. Neste trabalho, propomos a construção de conjuntos de ambiguidade poliédricos baseado somente em uma amostra de retornos. Nestes conjuntos, as relações entre variáveis são determinadas pelos dados de maneira não paramétrica, sendo assim livre de possíveis erros de especificação de um modelo estocástico. Propomos um algoritmo para construção do conjunto e, dado o conjunto, uma reformulação computacionalmente tratável do problema de otimização de portfólio. Experimentos numéricos mostram que uma melhor performance do modelo em comparação com benchmarks selecionados.<br>Portfolio optimization traditionally assumes knowledge of the probability distribution of returns or at least some of its moments. However is well known that the probability distribution of returns changes over time, making difficult the use of purely statistic models which undoubtedly rely on an estimated distribution. On the other hand robust optimization consider a total lack of knowledge about the distribution of returns and therefore it seeks an optimal solution for all the possible realizations wuthin a set of uncertainties of the returns. More recently the literature shows that distributionally robust optimization techniques allow us to deal with ambiguity regarding the distribution of returns. However these methods depend on the construction of the set of ambiguity, that is, all distribution of probability to be considered. This work proposes the construction of polyhedral ambiguity sets based only on a sample of returns. In those sets, the relations between variables are determined by the data in a non-parametric way, being thus free of possible specification errors of a stochastic model. We propose an algorithm for constructing the ambiguity set, and then a computationally treatable reformulation of the portfolio optimization problem. Numerical experiments show that a better performance of the model compared to selected benchmarks.

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Potaptchik, Marina. "Portfolio Selection Under Nonsmooth Convex Transaction Costs." Thesis, University of Waterloo, 2006. http://hdl.handle.net/10012/2940.

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We consider a portfolio selection problem in the presence of transaction costs. Transaction costs on each asset are assumed to be a convex function of the amount sold or bought. This function can be nondifferentiable in a finite number of points. The objective function of this problem is a sum of a convex twice differentiable function and a separable convex nondifferentiable function. We first consider the problem in the presence of linear constraints and later generalize the results to the case when the constraints are given by the convex piece-wise linear functions. <br /><br /> Due to the special structure, this problem can be replaced by an equivalent differentiable problem in a higher dimension. It's main drawback is efficiency since the higher dimensional problem is computationally expensive to solve. <br /><br /> We propose several alternative ways to solve this problem which do not require introducing new variables or constraints. We derive the optimality conditions for this problem using subdifferentials. First, we generalize an active set method to this class of problems. We solve the problem by considering a sequence of equality constrained subproblems, each subproblem having a twice differentiable objective function. Information gathered at each step is used to construct the subproblem for the next step. We also show how the nonsmoothness can be handled efficiently by using spline approximations. The problem is then solved using a primal-dual interior-point method. <br /><br /> If a higher accuracy is needed, we do a crossover to an active set method. Our numerical tests show that we can solve large scale problems efficiently and accurately.

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Ferreira, Pedro Miguel Barreirão. "Diversification and portfolio selection methods." Master's thesis, Instituto Superior de Economia e Gestão, 2010. http://hdl.handle.net/10400.5/2227.

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Mestrado em Finanças<br>This paper studies several portfolio selection methods in order to achieve higher returns and lower risk than the market. The main objective of this paper is to conclude if it is possible to achieve higher returns and lower risk than the market using only daily close stocks price data. It is important however, to know how the number of assets affects the risk of portfolio (benefits of diversification). Therefore, in the early stage, the impact of the introduction of stocks in the portfolio in terms of risk will be analyzed in order to choose a minimum number of stocks to maximize the benefits of diversification. Several techniques of portfolio selection (optimal portfolio, minimum variance and equal weights) are tested in order to achieve higher returns and lower risk levels than the sectors indexes. The benefits of diversification can be achieved with few stocks. This is the first conclusion of this paper that allows a reduction of the cost of transactions in the techniques used. Some of the portfolio selection methods in this paper achieved quite good results, revealed better performance than the index markets over the ten year period. However the best technique isn't equal to all sectors, there are slight differences between the best techniques among sectors.<br>Este trabalho estuda diversos métodos de selecção de carteiras de forma a obter maiores retornos e menor risco que o mercado. O principal objectivo é obter maiores rendibilidades e menores níveis de risco que o mercado usando apenas os preços das acções. Contudo, é importante saber como o número de activos afecta o risco de uma carteira (benefícios da diversificação). Portanto, numa primeira fase, será analisado o impacto da introdução de activos numa carteira em termos de risco, para escolher um número mínimo de acções para constituir uma carteira maximizando o benefício da diversificação. Diversas técnicas de selecção de carteiras (carteira óptima, variância mínima e pesos iguais) são testadas de forma a obter maiores retornos e menores nível de risco que o índice sectorial. Os benefícios da diversificação podem ser atingidos com poucas acções. Esta foi a primeira conclusão, que permitiu a redução dos custos de transacção nas técnicas utilizadas. Alguns métodos de selecção de carteiras estudados obtiveram bons resultados, revelando melhor performance que o índice de mercado ao longo dos dez anos. Contudo, a melhor técnica não é igual para todos os sectores, existem ligeiras diferenças entre as melhores técnicas entre os sectores.

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Bierkamp, Nils. "Simulative portfolio optimization under distributions of hyperbolic type : methods and empirical investigation /." Aachen : Shaker, 2006. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=014986541&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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CORREA, MARLON HENRIQUE ZAVAGLI. "STOCHASTIC OPTIMIZATION MODEL FOR PORTFOLIO SELECTION OF BRAZILIAN FIXED-INCOME SECURITIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25294@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>A seleção de um portfolio de renda fixa é um problema comumente enfrentado pelos agentes do mercado financeiro. A alocação ótima destes ativos melhora o nível de rentabilidade e lucratividade da instituição. Um dos trade-offs rotineiramente encontrado pelos gestores destas carteiras é decidir entre a compra de títulos pré-fixados e pós-fixados de curto prazo ou longo prazo, sendo que estes últimos no geral rendem mais devido ao prêmio de risco. Tais títulos, apesar de terem a sua rentabilidade já definida no momento da compra, podem ser vendidos a qualquer momento e sua nova rentabilidade estará sujeitas às marcações a mercado. O retorno da carteira composta por estes títulos é portanto uma variável aleatória que torna necessário o controle dos riscos de perda deste portfolio. O presente estudo teve por objetivo desenvolver um modelo de otimização da rentabilidade de uma carteira composta somente por títulos prefixados do tesouro nacional, com restrições ao nível de risco expresso através do Conditional Value at Risk. Após tal, foram realizados backtests para medir o desempenho do modelo e comparar a sua rentabilidade com o índice CDI. Os testes mostraram que o modelo apresenta resultados bons em rentabilidade e resultados satisfatórios em termos de controle de risco.<br>Fixed-income portfolio selection is a common problem faced by financial market agents. The optimal allocation of these assets improves the profitability of institutions. A trade-off routinely found by the managers of these portfolios is deciding between buying floating rate securities or short-term or long-term fixed-rate securities, while the latter generally has a higher yield due to risk premium. Despite fixed rate securities have their return already set at the moment of purchase, they can be sold at any time and the new return will be subject to the current market prices. Since the return of a portfolio holding these securities is a random variable, we argue for the importance of a risk assessment and control a fixed income security portfolio. This study aimmed to develop an optimization model of return with a portfolio composed only on fixed and floating rate bonds from Brazil s sovereign treasury, using risk restrictions expressed on the Conditional Value at Risk measure. After that, backtestswere performed to measure model efficiency and compare its return to the Brazilian s Interbank rate. The tests have shown good results in profitability and risk control.

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Bade, Alexander. "Bayesian portfolio optimization from a static and dynamic perspective /." Münster : Verl.-Haus Monsenstein und Vannerdat, 2009. http://d-nb.info/996985085/04.

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Ramilton, Alan. "Should you optimize your portfolio? : On portfolio optimization: The optimized strategy versus the naïve and market strategy on the Swedish stock market." Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-218024.

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In this paper, I evaluate the out-of-sample performance of the portfolio optimizer relative to the naïve and market strategy on the Swedish stock market from January 1998 to December 2012. Recent studies suggest that simpler strategies, such as the naïve strategy, outperforms optimized strategies and that they should be implemented in the absence of better estimation models. Of the 12 strategies I evaluate, 11 of them significantly outperform both benchmark strategies in terms of Sharpe ratio. I find that the no-short-sales constrained minimum-variance strategy is preferred over the mean-variance strategy, and that the historical sample estimator creates better minimum-variance portfolios than the single-factor model and the three-factor model. My results suggest that there are considerable gains to optimization in terms of risk reduction and return in the context of portfolio selection.

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Murphy, Jonathan Rodgers. "A robust multi-objective statistical improvement approach to electric power portfolio selection." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45946.

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Motivated by an electric power portfolio selection problem, a sampling method is developed for simulation-based robust design that builds on existing multi-objective statistical improvement methods. It uses a Bayesian surrogate model regressed on both design and noise variables, and makes use of methods for estimating epistemic model uncertainty in environmental uncertainty metrics. Regions of the design space are sequentially sampled in a manner that balances exploration of unknown designs and exploitation of designs thought to be Pareto optimal, while regions of the noise space are sampled to improve knowledge of the environmental uncertainty. A scalable test problem is used to compare the method with design of experiments (DoE) and crossed array methods, and the method is found to be more efficient for restrictive sample budgets. Experiments with the same test problem are used to study the sensitivity of the methods to numbers of design and noise variables. Lastly, the method is demonstrated on an electric power portfolio simulation code.

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Books on the topic "Portfolio selection optimization"

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Boyle, Phelim P. Optimal portfolio selection with transaction costs. University of Toronto, Dept. of Statistics, 1994.

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Bond Portfolio Optimization. Springer, 2008.

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Travers, Frank J. Investment Manager Analysis: A Comprehensive Guide to Portfolio Selection, Monitoring and Optimization. Wiley & Sons, Incorporated, John, 2011.

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Travers, Frank J. Investment Manager Analysis: A Comprehensive Guide to Portfolio Selection, Monitoring and Optimization. Wiley & Sons, Incorporated, John, 2004.

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Investment Manager Analysis: A Comprehensive Guide to Portfolio Selection, Monitoring and Optimization (Wiley Finance). Wiley, 2004.

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Book chapters on the topic "Portfolio selection optimization"

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Pardalos, P. M. "Optimization Techniques for Portfolio Selection." In New Operational Approaches for Financial Modelling. Physica-Verlag HD, 1997. http://dx.doi.org/10.1007/978-3-642-59270-6_2.

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Kibzun, Andrey, and Riho Lepp. "Discrete Approximation in Quantile Problem of Portfolio Selection." In Applied Optimization. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-6594-6_6.

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Majumder, Saibal, Samarjit Kar, and Tandra Pal. "Mean-Entropy Model of Uncertain Portfolio Selection Problem." In Multi-Objective Optimization. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1471-1_2.

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Vercher, Enriqueta, and José D. Bermúdez. "Fuzzy Portfolio Selection Models: A Numerical Study." In Springer Optimization and Its Applications. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4614-3773-4_10.

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Hasuike, Takashi, and Hiroaki Ishii. "Mathematical Approaches for Fuzzy Portfolio Selection Problems with Normal Mixture Distributions." In Fuzzy Optimization. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13935-2_19.

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Coelho, Ricardo. "On Fuzzy Convex Optimization to Portfolio Selection Problem." In Soft Computing Based Optimization and Decision Models. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64286-4_8.

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Calvo, Clara, Carlos Ivorra, and Vicente Liern. "Fuzzy Portfolio Selection Models for Dealing with Investor’s Preferences." In Soft Computing Based Optimization and Decision Models. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64286-4_7.

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Hochreiter, Ronald. "An Evolutionary Optimization Approach to Risk Parity Portfolio Selection." In Applications of Evolutionary Computation. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16549-3_23.

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Lai, Kin Keung, Lean Yu, Shouyang Wang, and Chengxiong Zhou. "A Double-Stage Genetic Optimization Algorithm for Portfolio Selection." In Neural Information Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893295_102.

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Niu, Ben, Ying Bi, and Ting Xie. "Structure-Redesign-Based Bacterial Foraging Optimization for Portfolio Selection." In Intelligent Computing in Bioinformatics. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09330-7_49.

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Conference papers on the topic "Portfolio selection optimization"

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Barros, Marcio de Oliveira, Hélio Rodrigues Costa, Fábio Vitorino Figueiredo, and Ana Regina Cavalcanti da Rocha. "Multiobjective optimization for project portfolio selection." In the fourteenth international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2330784.2331037.

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Loukeris, N., S. Bekiros, and I. Elefhteriadis. "The Intelligent Portfolio Selection Optimization System, (IPSOS)." In 2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA). IEEE, 2016. http://dx.doi.org/10.1109/iisa.2016.7785340.

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Chu-Xin, JIN, CHEN Wan-Yi, and Yu Shu-Jing. "Robust Portfolio Selection Based on Optimization Methods." In 2018 37th Chinese Control Conference (CCC). IEEE, 2018. http://dx.doi.org/10.23919/chicc.2018.8483072.

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Das, Puja, and Arindam Banerjee. "Meta optimization and its application to portfolio selection." In the 17th ACM SIGKDD international conference. ACM Press, 2011. http://dx.doi.org/10.1145/2020408.2020588.

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Zhao, Ziping, and Daniel P. Palomar. "Large-Scale Regularized Portfolio Selection Via Convex Optimization." In 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2019. http://dx.doi.org/10.1109/globalsip45357.2019.8969214.

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Xu, Fasheng, Wei Chen, and Ling Yang. "Improved Particle Swarm Optimization for Realistic Portfolio Selection." In Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2007). IEEE, 2007. http://dx.doi.org/10.1109/snpd.2007.375.

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Chen, Wei, Run-tong Zhang, Yong-ming Cai, and Fa-sheng Xu. "Particle Swarm Optimization for Constrained Portfolio Selection Problems." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258773.

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Lin, Yifan, Enlu Zhou, and Aly Megahed. "A Nested Simulation Optimization Approach for Portfolio Selection." In 2020 Winter Simulation Conference (WSC). IEEE, 2020. http://dx.doi.org/10.1109/wsc48552.2020.9384023.

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Tang, Wenguang, and Fenxia Zhao. "Multi-objective Programming Model for Asset Portfolio Selection." In 2011 Fourth International Joint Conference on Computational Sciences and Optimization (CSO). IEEE, 2011. http://dx.doi.org/10.1109/cso.2011.171.

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Davoudpour, Hamid, and Maryam Ashrafi. "Developing a framework for energy technology portfolio selection." In PROCEEDINGS OF THE SIXTH GLOBAL CONFERENCE ON POWER CONTROL AND OPTIMIZATION. AIP, 2012. http://dx.doi.org/10.1063/1.4769015.

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