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

Gholamreza Karami, Seyed Mostafa Seyed Hosseini, Navid Attaran, Seyed Mojtaba Seyed Hosseini

Abstract


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.


Full Text: PDF DOI: 10.5539/ijef.v4n5p116

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This work is licensed under a Creative Commons Attribution 3.0 License.

International Journal of Economics and Finance  ISSN  1916-971X (Print) ISSN  1916-9728 (Online)

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