Predicting corporate investment/non-investment grade by using interval-valued fuzzy rule-based systems-A cross-region analysis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F18%3A39913370" target="_blank" >RIV/00216275:25410/18:39913370 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1568494617306427" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494617306427</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2017.10.037" target="_blank" >10.1016/j.asoc.2017.10.037</a>

Result language

angličtina

Original language name

Predicting corporate investment/non-investment grade by using interval-valued fuzzy rule-based systems-A cross-region analysis

Original language description

Systems for predicting corporate rating have attracted considerable interest in soft computing research due to the requirements for both accuracy and interpretability. In addition, the high uncertainty associated primarily with linguistic uncertainties and disagreement among experts is another challenging problem. To overcome these problems, this study proposes a hybrid evolutionary interval-valued fuzzy rule-based system, namely IVTURS, combined with evolutionary feature selection component. This model is used to predict the investment/non-investment grades of companies from four regions, namely Emerging countries, the EU, the United States, and other developed countries. To evaluate prediction performance, a yield measure is used that combines the return and default rates of companies. Here, we show that using interval-valued fuzzy sets leads to higher accuracy, particularly with the growing granularity at the fuzzy partition level. The proposed prediction model is then compared with several state-of-the-art evolutionary fuzzy rule-based systems. The obtained results show that the proposed model is especially suitable for high-dimensional problems, without facing rule base interpretability issues. This finding indicates that the model is preferable for investors oriented toward developed markets such as the EU and the United States.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA13-10331S" target="_blank" >GA13-10331S: The role of text information in corporate financial distress prediction models – country-specific and industry-specific approaches</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Applied Soft Computing

ISSN

1568-4946

e-ISSN

—

Volume of the periodical

62

Issue of the periodical within the volume

January

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

13

Pages from-to

73-85

UT code for WoS article

000418333500006

EID of the result in the Scopus database

2-s2.0-85032451278