The Goal Programming Model: Theory and Applications

Aouni, Belaı̈d; Colapinto, Cinzia; Torre, Davide La

doi:10.1002/9781118462706.ch16

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Comparatively, a significant analysis was presented by Aouni ( 2009 ), where the author linked portfolio optimization with multiattribute portfolio selection. In his further research (Aouni, 2010 ; Aouni et al, 2008 ), the author gave more examples of how goal programming can be used in portfolio selection. A comprehensive review of MCDM techniques was presented in the study Mardani et al ( 2015 ), where a list of publications (more than 460) with different applications in many fields of science, engineering and management was provided.…”

Section: Artificial Intelligence Approaches For Signal Generationmentioning

confidence: 99%

Enhancing portfolio management using artificial intelligence: literature review

Sutiene,

Schwendner,

Sipos

et al. 2024

Front. Artif. Intell.

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Building an investment portfolio is a problem that numerous researchers have addressed for many years. The key goal has always been to balance risk and reward by optimally allocating assets such as stocks, bonds, and cash. In general, the portfolio management process is based on three steps: planning, execution, and feedback, each of which has its objectives and methods to be employed. Starting from Markowitz's mean-variance portfolio theory, different frameworks have been widely accepted, which considerably renewed how asset allocation is being solved. Recent advances in artificial intelligence provide methodological and technological capabilities to solve highly complex problems, and investment portfolio is no exception. For this reason, the paper reviews the current state-of-the-art approaches by answering the core question of how artificial intelligence is transforming portfolio management steps. Moreover, as the use of artificial intelligence in finance is challenged by transparency, fairness and explainability requirements, the case study of post-hoc explanations for asset allocation is demonstrated. Finally, we discuss recent regulatory developments in the European investment business and highlight specific aspects of this business where explainable artificial intelligence could advance transparency of the investment process.

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Section: Artificial Intelligence Approaches For Signal Generationmentioning

confidence: 99%

Enhancing portfolio management using artificial intelligence: literature review

Sutiene,

Schwendner,

Sipos

et al. 2024

Front. Artif. Intell.

View full text Add to dashboard Cite

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“…Let us notice that the GP model ( 12) admits a solution because of the continuity of f i and F and the compactness of D. Some applications of this model can also be found in [4,5,6,7,8].…”

Section: Risk Management Through a Gp Model With Satisfaction Functionmentioning

confidence: 99%

A Goal Programming Model with Satisfaction Function for Risk Management and Optimal Portfolio Diversification

Maggis

Torre

2012

INFOR: Information Systems and Operational Research

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We extend the classical risk minimization model with scalar risk measures to the general case of set-valued risk measures. The problem we obtain is a set-valued optimization model and we propose a goal programming-based approach with satisfaction function to obtain a solution which represents the best compromise between goals and the achievement levels. Numerical examples are provided to illustrate how the method works in practical situations.

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The Goal Programming Model: Theory and Applications

Cited by 2 publications

References 17 publications

Enhancing portfolio management using artificial intelligence: literature review

Enhancing portfolio management using artificial intelligence: literature review

A Goal Programming Model with Satisfaction Function for Risk Management and Optimal Portfolio Diversification

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