A genetic algorithm application in bankruptcy prediction modeling

By Kyung-shik Shin, Yong-Joo Lee. Expert systems with applications, 2002

417 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/14213e9bf68e6bf2706f43ce7d00842147de32c8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Dean A. Shepherd
2. Johan Wiklund
3. Hui Li
4. Jie Sun
5. Qinghua Huang
6. Kai-Yu He
7. Indranil Bose
8. Xi Chen

In Artificial Intelligence

1. Kyoung-jae Kim
2. Hui Li
3. Jie Sun
4. Qinghua Huang
5. Kai-Yu He
6. Dawei Du
7. D. Simon
8. Mehmet Ergezer

In Management Science and Operations Research

1. Kyoung-jae Kim
2. Dawei Du
3. D. Simon
4. Mehmet Ergezer
5. Indranil Bose
6. Xi Chen
7. Ruibin Geng
8. Hyunchul Ahn

Citations in the corpus

• The age-effect of financial indicators as buffers against the liability of newness ☆ (2010, background): “Complementing this work, scholars have developed an important group of models that attempts to predict bankruptcy and the financial distress that often precedes bankruptcy (Shin and Lee, 2002).”
• Soft computing techniques applied to finance (2008, neutral): “In this context, even though there is literature based on different techniques such as genetic algorithms [ 34 ] or rough sets [35]; artificial neural networks and neuro-fuzzy systems are especially widespread.”
• Oppositional biogeography-based optimization (2009, methodology): “In finance, EC has been employed for bankruptcy [84, 85, 86] and stock predictions [87, 88, 89].”
• Data Mining on Finance and Accounting: a Review of Current Research Trends (2004, background): “Shin and Lee (2002) proposed a model based on GAs.”
• Simultaneous optimization of artificial neural networks for financial forecasting (2012, methodology): “Thus, we use the same configuration settings for these parameters as prior studies did that proposed an optimization approach of ANN using GA [46, 47].”
• Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review (2007, background): “They concluded that the ICPBMSD or ICPB-GA might be promising when compared to traditional MSD or GA. Shin and Lee [102] also proposed a GA-based approach for bankruptcy prediction.”
• Prediction of financial distress: An empirical study of listed Chinese companies using data mining (2015, methodology): “, 1997; Li and Sun, 2009), genetic algorithms (Shin and Lee, 2002), Kohonen map (du Jardin, 2012), rough sets (McKee, 2000), simulation analysis (Cohen et al.”
• Discovering golden nuggets: data mining in financial application (2004, methodology): “In addition, numerous studies have tried to develop different bankruptcy prediction models by applying other data mining techniques including logistic regression analysis, genetic algorithms, decision trees, classification and regression trees (CART), and other statistical methods [29]–[31].”
• The discovery of experts' decision rules from qualitative bankruptcy data using genetic algorithms (2003, background): “Shin and Lee (2002) applied a GA in bankruptcy prediction to extract rules from financial ratios.”
• Soft computing system for bank performance prediction (2008, methodology): “Shin and Lee [47] applied SVM to the problem of corporate bankruptcy prediction and concluded that SVM outperformed the MLFF-BP in terms of accuracy and generalization, as the training dataset size got smaller.”
• Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches (2014, methodology): “Shin et al. [107] and Min and Lee [80] both used SVM to predict bankruptcy for South Korean companies, and drew the conclusion that this method outperformed MDA, Logit and NN. Hui and Sun [49] and Ding et al. [31] respectively adopted SVM model to do empirical study on FDP for Chinese listed compani”