Portfolio Optimization for Defence Applications

Harrison, Kyle Robert; Elsayed, Saber M.; Garanovich, Ivan L.; Weir, Terence; Galister, Michael; Boswell, Sharon G.; Taylor, Richard; Sarker, Ruhul

doi:10.1109/access.2020.2983141

Cited by 21 publications

(8 citation statements)

References 92 publications

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“…In Brazil, large ecosystems and SoS were found in the defense industry [35], as military systems usually have hundreds of suppliers and several innovative complementors. Furthermore, cases in the defense domain are also worthy of study as they pose particular challenges not seen in other sectors [36,37]. Accordingly, the author selected three ecosystems from the Brazilian armored vehicle sector to support the multiple case study.…”

Section: Case Selectionmentioning

confidence: 99%

Dynamics of Innovation Ecosystems: Orchestrating Actors and Interactions in Emerging Economies

Bernat

2024

Business, Management and Economics

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Innovation ecosystem (IE) literature explores the interdependencies between partners that jointly innovate and create value. IEs comprise various actors such as focal firms, suppliers, complementary innovators, and customers. This study elaborates on actors’ interactions that promote the emergence and evolution of IEs in emerging economies. System of Systems (SoS) literature—which classifies the types of authority between the system and its components into virtual, collaborative, acknowledged, and directed—is applied to propose a conceptual framework for analyzing IEs. Following a multiple case study, three ecosystems were selected from the Brazilian armored vehicle sector and analyzed according to the proposed framework. The results revealed that the organizational environment impacts IEs by promoting their emergence and evolution or even leading to their death. The interaction between ecosystem actors can also contribute to the success or failure of IEs. Managing to reach the optimal type of authority can be a valuable tool for orchestrating actors and their interactions in IEs.

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Section: Case Selectionmentioning

confidence: 99%

Dynamics of Innovation Ecosystems: Orchestrating Actors and Interactions in Emerging Economies

Bernat

2024

Business, Management and Economics

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“…The approach presented in this study is an application of non-linear cardinalityconstraints optimization, where the main goal is to strike a balance between maximizing returns and minimizing risks [13]. While some studies opted a penalty on the l 1 norm of the weight vector or its alternatives, others explicitly enforce cardinality constraints [12].…”

Section: Introductionmentioning

confidence: 99%

“…Noteworthy, the formulation of the regularized problem exhibited better mathematical properties than the corresponding cardinality-constrained problems. This is because it can minimize risks while maximizing expected returns in the context of portfolio optimization [13]. The formulation of the regularized problem has also been shown to be well-established fact because the approximation of risk plays a pivotal role in the presence of uncertain inputs, such as expected returns, making risk quantification paramount.…”

Section: Introductionmentioning

confidence: 99%

Sequential Quadratic Programming for Nonlinear Problems with Cardinality Constraints

Almani

2023

Preprint

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Nonlinear optimization problems with cardinality constraints and risk measurements, particularly in the fields such as finance and risk management, have posed significant challenges. Portfolio optimization under these constraints requires effective algorithms and methodologies to improve performance. Thus, this study aimed to investigate in nonlinear optimization problems with cardinality constraints and risk measurements in fields like finance and risk management. Indeed, by using various algorithms and methodologies, this study aimed to improve the performance of portfolio optimization under these constraints. To measurement risk in portfolio programing, deemed as an application in nonlinear problems with cardinality constraints, VaR (value at risk), CVaR (conditional value at risk), RVaR (robust value at risk), and RCVaR (robust conditional value at risk) were used. Regularization techniques played a crucial role in optimizing non-smooth problems, addressing challenges arising from the non-smoothness of the constraints, thereby enhancing algorithm performance. By using some regularization algorithms, such as Scholtes, Kanzow-Schwartz, and Bordakov methods, an improvement in performance was obtained. Notably, sequential quadratic programming (SQP), which is an iterative optimization technique, was used to solve nonlinear programming problems by approximating the objective and constraints using quadratic functions. After a comparison of algorithms' performance was explored, the experiments indicated a significant enhancement in the performance of the SQP algorithm by using the Scholtes regularization technique. Thus, these findings show that SQP can be a promise method to solving this classification of cardinality-constraints nonlinear problems. The study introduces innovative optimization solutions to improve algorithmic performance in complex problems, contributing to advancements in finance and risk management approaches.

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“…Portfolio optimization was the process by which the optimal portfolio (distribution of assets) was selected according to some objective measure, with the caveat that the associated risks must also be minimized [12]. Portfolio optimization was concerned with maximizing the expected return from a series of investments and minimizing the associated risks, such as stock market volatility [13]. Instead, one must consider how assets affect the risk and return of the entire portfolio.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Stock Portfolios Using Goal Programming Based on the Kalman-Filter Method

Fauziyah

Purnaningrum

2021

J. Mat. Mantik

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Long-term stock investment development is carried out by means of portfolio optimization. Selection of stocks for portfolios is not only based on high-value stock prices but also takes into account their fluctuations. Estimation of future stock price fluctuations has an indirect impact on future portfolio formation. This research has implemented the Kalman filter method to obtain the best estimation results from various stock prices with a high degree of accuracy. The results are then used to form a stock portfolio on the basis of Goal Programming. This study has compared the optimization results with the real value of stock prices. The results obtained, Kalman filter-based Goal Programming is more effective for predicting future portfolios compared to the Goal Programming method with a return difference of Rp. 178,039,848. This suggests that optimization with the Kalman Filter-based Objective Programming can be used as a tool to determine future stock portfolios.

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Portfolio Optimization for Defence Applications

Cited by 21 publications

References 92 publications

Dynamics of Innovation Ecosystems: Orchestrating Actors and Interactions in Emerging Economies

Dynamics of Innovation Ecosystems: Orchestrating Actors and Interactions in Emerging Economies

Sequential Quadratic Programming for Nonlinear Problems with Cardinality Constraints

Optimization of Stock Portfolios Using Goal Programming Based on the Kalman-Filter Method

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