Process-oriented social responsibility indicator for mutual funds: a multi-criteria decision analysis approach

Q4 Business, Management and Accounting
T. Verheyden, L. Moor
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引用次数: 8

Abstract

In recent years, socially responsible investing (SRI) has grown from a niche to a mainstream investment strategy. Consequently, the supply of SRI mutual funds expanded and a discussion on the performance of SRI funds developed. The traditional approach to measuring mutual fund performance does not take into account possible heterogeneity with respect to social responsibility and focuses on a dichotomous SRI versus non-SRI typology. We consider multi-criteria decision analysis (MCDA) to build an indicator to capture the multi-faceted nature of SRI. Considering extensive robustness analysis and additional criteria, we find the PROMETHEE methodology to be most recommended for both academic and professional applications. The obtained scores and rankings prove to be highly robust and are in line with generally accepted intuition in the industry. The proposed PROMETHEE-based indicator is also easy to understand and implement, which makes it highly suited for implementation in mutual fund performance research.
面向过程的共同基金社会责任指标:一种多准则决策分析方法
近年来,社会责任投资(SRI)已从一个小众投资策略发展成为主流投资策略。因此,SRI共同基金的供应扩大了,对SRI基金绩效的讨论也开始了。衡量共同基金业绩的传统方法没有考虑到社会责任方面可能存在的异质性,而是侧重于SRI与非SRI类型的二分法。我们考虑多准则决策分析(MCDA)来建立一个指标来捕捉社会责任投资的多面性。考虑到广泛的稳健性分析和其他标准,我们发现PROMETHEE方法最适合学术和专业应用。所获得的分数和排名被证明是高度稳健的,符合业界普遍接受的直觉。所提出的基于promehee的指标也易于理解和实施,这使得它非常适合在共同基金绩效研究中实施。
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来源期刊
International Journal of Multicriteria Decision Making
International Journal of Multicriteria Decision Making Business, Management and Accounting-Strategy and Management
CiteScore
0.70
自引率
0.00%
发文量
9
期刊介绍: IJMCDM is a scholarly journal that publishes high quality research contributing to the theory and practice of decision making in ill-structured problems involving multiple criteria, goals and objectives. The journal publishes papers concerning all aspects of multicriteria decision making (MCDM), including theoretical studies, empirical investigations, comparisons and real-world applications. Papers exploring the connections with other disciplines in operations research and management science are particularly welcome. Topics covered include: -Artificial intelligence, evolutionary computation, soft computing in MCDM -Conjoint/performance measurement -Decision making under uncertainty -Disaggregation analysis, preference learning/elicitation -Group decision making, multicriteria games -Multi-attribute utility/value theory -Multi-criteria decision support systems and knowledge-based systems -Multi-objective mathematical programming -Outranking relations theory -Preference modelling -Problem structuring with multiple criteria -Risk analysis/modelling, sensitivity/robustness analysis -Social choice models -Theoretical foundations of MCDM, rough set theory -Innovative applied research in relevant fields
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