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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

<a href="/cs/project/GA13-10331S" target="_blank" >GA13-10331S: Úloha textové informace v modelech predikce finanční tísně podniků - přístupy specifické podle států a průmyslových odvětví</a><br>

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název periodika

Applied Soft Computing

ISSN

1568-4946

e-ISSN

—

Svazek periodika

62

Číslo periodika v rámci svazku

January

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

73-85

Kód UT WoS článku

000418333500006

EID výsledku v databázi Scopus

2-s2.0-85032451278