Bankruptcy Prediction Using Memetic Algorithm with Fuzzy Approach: Empirical Evidence from Iran

Autor:	Gholamreza Karami, Seyed Mostafa Seyed Hosseini, Seyed Mojtaba Seyed Hosseini, Navid Attaran
Rok vydání:	2012
Předmět:	Set (abstract data type) Order (exchange) Computer science Stock exchange Accounting information system Bankruptcy prediction Memetic algorithm Operations management Data mining computer.software_genre Fuzzy logic computer Type I and type II errors
Zdroj:	International Journal of Economics and Finance. 4
ISSN:	1916-9728 1916-971X
DOI:	10.5539/ijef.v4n5p116
Popis:	Several corporate failures have recently occurred, many have suffered from serious losses and most notably public confidence has deteriorated. In order to facilitate investor’s decision making regarding potential investment opportunities, this paper seeks to demonstrate that it is model specific constraints that limits the usefulness of accounting information, not the nature of variables per se. Thus we develop a Hybrid model which is an adaptive Memetic Algorithm combined with fuzzy approach that generates and optimizes a set of if-then rules for bankruptcy prediction. Data are derived from Tehran Stock Exchange (TSE) data bank, adopting 18 variables all of which are accounting ones, between 2001 and 2009. Four out of five models used in this survey have either accomplished high degree of accuracy or low level of type I error; however experimental results show that in terms of both average accuracy in prediction and occurrence of type I and II errors, fuzzy memetic performs better than GA, MLP, C4.5 and LDA in comparison.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ed2a6774612ab153301fd23cf0e57727 https://doi.org/10.5539/ijef.v4n5p116 Zobrazit plný text záznamu