对经济数据进行全面的 MCDM 评估:最大归一化、CODAS 和模糊方法的成功分析

IF 6.9 1区 经济学 Q1 BUSINESS, FINANCE Financial Innovation Pub Date : 2024-03-13 DOI:10.1186/s40854-023-00588-x
Mahmut Baydaş, Mustafa Yılmaz, Željko Jović, Željko Stević, Sevilay Ece Gümüş Özuyar, Abdullah Özçil
{"title":"对经济数据进行全面的 MCDM 评估:最大归一化、CODAS 和模糊方法的成功分析","authors":"Mahmut Baydaş, Mustafa Yılmaz, Željko Jović, Željko Stević, Sevilay Ece Gümüş Özuyar, Abdullah Özçil","doi":"10.1186/s40854-023-00588-x","DOIUrl":null,"url":null,"abstract":"The approach of evaluating the final scores of multi-criteria decision-making (MCDM) methods according to the strength of association with real-life rankings is interesting for comparing MCDM methods. This approach has recently been applied mostly to financial data. In these studies, where it is emphasized that some methods show more stable success, it would be useful to see the results that will emerge by testing the approach on different data structures more comprehensively. Moreover, not only the final MCDM results but also the performance of normalization techniques and data types (fuzzy or crisp), which are components of MCDM, can be compared using the same approach. These components also have the potential to affect MCDM results directly. In this direction, in our study, the economic performances of G-20 (Group of 20) countries, which have different data structures, were calculated over ten different periodic decision matrices. Ten different crisp-based MCDM methods (COPRAS, CODAS, MOORA, TOPSIS, MABAC, VIKOR (S, R, Q), FUCA, and ELECTRE III) with different capabilities were used to better visualize the big picture. The relationships between two different real-life reference anchors and MCDM methods were used as a basis for comparison. The CODAS method develops a high correlation with both anchors in most periods. The most appropriate normalization technique for CODAS was identified using these two anchors. Interestingly, the maximum normalization technique was the most successful among the alternatives (max, min–max, vector, sum, and alternative ranking-based). Moreover, we compared the two main data types by comparing the correlation results of crisp-based and fuzzy-based CODAS. The results were very consistent, and the “Maximum normalization-based fuzzy integrated CODAS procedure” was proposed to decision-makers to measure the economic performance of the countries.","PeriodicalId":37175,"journal":{"name":"Financial Innovation","volume":"22 1","pages":""},"PeriodicalIF":6.9000,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A comprehensive MCDM assessment for economic data: success analysis of maximum normalization, CODAS, and fuzzy approaches\",\"authors\":\"Mahmut Baydaş, Mustafa Yılmaz, Željko Jović, Željko Stević, Sevilay Ece Gümüş Özuyar, Abdullah Özçil\",\"doi\":\"10.1186/s40854-023-00588-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The approach of evaluating the final scores of multi-criteria decision-making (MCDM) methods according to the strength of association with real-life rankings is interesting for comparing MCDM methods. This approach has recently been applied mostly to financial data. In these studies, where it is emphasized that some methods show more stable success, it would be useful to see the results that will emerge by testing the approach on different data structures more comprehensively. Moreover, not only the final MCDM results but also the performance of normalization techniques and data types (fuzzy or crisp), which are components of MCDM, can be compared using the same approach. These components also have the potential to affect MCDM results directly. In this direction, in our study, the economic performances of G-20 (Group of 20) countries, which have different data structures, were calculated over ten different periodic decision matrices. Ten different crisp-based MCDM methods (COPRAS, CODAS, MOORA, TOPSIS, MABAC, VIKOR (S, R, Q), FUCA, and ELECTRE III) with different capabilities were used to better visualize the big picture. The relationships between two different real-life reference anchors and MCDM methods were used as a basis for comparison. The CODAS method develops a high correlation with both anchors in most periods. The most appropriate normalization technique for CODAS was identified using these two anchors. Interestingly, the maximum normalization technique was the most successful among the alternatives (max, min–max, vector, sum, and alternative ranking-based). Moreover, we compared the two main data types by comparing the correlation results of crisp-based and fuzzy-based CODAS. The results were very consistent, and the “Maximum normalization-based fuzzy integrated CODAS procedure” was proposed to decision-makers to measure the economic performance of the countries.\",\"PeriodicalId\":37175,\"journal\":{\"name\":\"Financial Innovation\",\"volume\":\"22 1\",\"pages\":\"\"},\"PeriodicalIF\":6.9000,\"publicationDate\":\"2024-03-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Financial Innovation\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1186/s40854-023-00588-x\",\"RegionNum\":1,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BUSINESS, FINANCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Financial Innovation","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1186/s40854-023-00588-x","RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0

摘要

根据与实际排名的关联程度来评估多标准决策(MCDM)方法的最终得分,这种方法对于比较多标准决策方法很有意义。这种方法最近主要应用于金融数据。在这些研究中,人们强调某些方法显示出更稳定的成功,因此,通过在不同的数据结构上更全面地测试这种方法而得出的结果将是有益的。此外,使用相同的方法不仅可以比较 MCDM 的最终结果,还可以比较作为 MCDM 组成部分的规范化技术和数据类型(模糊或清晰)的性能。这些组成部分也有可能直接影响 MCDM 的结果。为此,在我们的研究中,对具有不同数据结构的 G-20(20 国集团)国家的经济表现,通过十个不同的周期性决策矩阵进行了计算。我们使用了十种不同能力的基于脆性的 MCDM 方法(COPRAS、CODAS、MOORA、TOPSIS、MABAC、VIKOR (S,R,Q)、FUCA 和 ELECTRE III),以更好地直观了解全局。两种不同的现实参考锚和 MCDM 方法之间的关系被用作比较的基础。CODAS 方法在大多数时期与这两个锚点都有很高的相关性。通过这两个锚点,确定了 CODAS 最合适的归一化技术。有趣的是,在所有备选方案(最大值、最小值、矢量、总和以及基于排序的备选方案)中,最大值归一化技术是最成功的。此外,我们还通过比较基于简明的 CODAS 和基于模糊的 CODAS 的相关结果,对两种主要数据类型进行了比较。结果非常一致,我们建议决策者使用 "基于最大归一化的模糊综合 CODAS 程序 "来衡量各国的经济绩效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A comprehensive MCDM assessment for economic data: success analysis of maximum normalization, CODAS, and fuzzy approaches
The approach of evaluating the final scores of multi-criteria decision-making (MCDM) methods according to the strength of association with real-life rankings is interesting for comparing MCDM methods. This approach has recently been applied mostly to financial data. In these studies, where it is emphasized that some methods show more stable success, it would be useful to see the results that will emerge by testing the approach on different data structures more comprehensively. Moreover, not only the final MCDM results but also the performance of normalization techniques and data types (fuzzy or crisp), which are components of MCDM, can be compared using the same approach. These components also have the potential to affect MCDM results directly. In this direction, in our study, the economic performances of G-20 (Group of 20) countries, which have different data structures, were calculated over ten different periodic decision matrices. Ten different crisp-based MCDM methods (COPRAS, CODAS, MOORA, TOPSIS, MABAC, VIKOR (S, R, Q), FUCA, and ELECTRE III) with different capabilities were used to better visualize the big picture. The relationships between two different real-life reference anchors and MCDM methods were used as a basis for comparison. The CODAS method develops a high correlation with both anchors in most periods. The most appropriate normalization technique for CODAS was identified using these two anchors. Interestingly, the maximum normalization technique was the most successful among the alternatives (max, min–max, vector, sum, and alternative ranking-based). Moreover, we compared the two main data types by comparing the correlation results of crisp-based and fuzzy-based CODAS. The results were very consistent, and the “Maximum normalization-based fuzzy integrated CODAS procedure” was proposed to decision-makers to measure the economic performance of the countries.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Financial Innovation
Financial Innovation Economics, Econometrics and Finance-Finance
CiteScore
11.40
自引率
11.90%
发文量
95
审稿时长
5 weeks
期刊介绍: Financial Innovation (FIN), a Springer OA journal sponsored by Southwestern University of Finance and Economics, serves as a global academic platform for sharing research findings in all aspects of financial innovation during the electronic business era. It facilitates interactions among researchers, policymakers, and practitioners, focusing on new financial instruments, technologies, markets, and institutions. Emphasizing emerging financial products enabled by disruptive technologies, FIN publishes high-quality academic and practical papers. The journal is peer-reviewed, indexed in SSCI, Scopus, Google Scholar, CNKI, CQVIP, and more.
期刊最新文献
Machine learning in business and finance: a literature review and research opportunities Predictive crypto-asset automated market maker architecture for decentralized finance using deep reinforcement learning Cryptocurrencies under climate shocks: a dynamic network analysis of extreme risk spillovers Exploring the coherency and predictability between the stocks of artificial intelligence and energy corporations From CFOs to crypto: exploratory study unraveling factors in corporate adoption
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1