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Institutional Repository [SANDBOX]
Technical University of Crete
Technical University of Crete
EN
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| URI: | http://purl.tuc.gr/dl/dias/E189E995-1DF3-4D60-BD2A-4432139FC3D4 | ||
| Year | 2001 | ||
| Type of Item | Peer-Reviewed Journal Publication | ||
| License |
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| Bibliographic Citation | M. Doumpos, S.H. Zanakis , C. Zopounidis ,"Multicriteria preference disaggregation for classification problems with an application to global investing risk," Decision Sc., vol. 32, no 2, pp. 333-385,2001.doi:10.1111/j.1540-5915.2001.tb00963.x https://doi.org/10.1111/j.1540-5915.2001.tb00963.x | ||
| Appears in Collections |
Mathematical programming and multicriteria approaches to classification and discrimination are reviewed, with an emphasis on preference disaggregation. The latter include the UTADIS family and a new method, Multigroup Hierarchical DIScrimination (MHDIS). They are used to assess investing risk in 51 countries that have stock exchanges, according to 27 criteria. These criteria include quantitative and qualitative measures of market risk (volatility and currency fluctuations); range of investment opportunities; quantity and quality on market information; investor protection (security regulations treatment of minority shareholders); and administrative “headaches” (custody, settlement, and taxes). The model parameters are determined so that the results best match the risk level assigned to those countries by experienced international investment managers commissioned by The Wall Street Journal. Among the six evaluation models developed, one (MHDIS) classifies correctly all countries into the appropriate groups. Thus, this model is able to reproduce consistently the evaluation of the expert investment analysts. The most significant criteria and their weights for assessing global risk investing are also presented, along with their marginal utilities, leading to identifiers of risk groups and global utilities portraying the strength of each country's risk classification. The same method, MHDIS, outperformed the other five methods in a 10-fold validation experiment.
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