The discovery of experts' decision rules from qualitative bankruptcy data using genetic algorithms

Kim, Myoung-Jong; Han, Ingoo

doi:10.1016/s0957-4174(03)00102-7

Cited by 148 publications

(68 citation statements)

References 24 publications

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“…In this paper we shall focus on the first category of models to illustrate how outof-sample evaluation of companies could be performed. The most popular static bankruptcy prediction models are based on statistical methodologies (e.g., Altman 1968Altman , 1983Taffler 1984), stochastic methodologies (e.g., Theodossiou 1991;Ohlson 1980;Zmijewski 1984), and artificial intelligence methodologies (e.g., Kim and Han 2003;Li and Sun 2011;Zhang et al 1999;Shin et al 2005). DEA methodologies are increasingly gaining popularity in bankruptcy prediction (e.g., Cielen et al 2004;Paradi et al 2004;Premachandra et al 2011;Shetty et al 2012).…”

Section: Introductionmentioning

confidence: 99%

An out-of-sample evaluation framework for DEA with application in bankruptcy prediction

Ouenniche

Tone

2017

Ann Oper Res

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Nowadays, data envelopment analysis (DEA) is a well-established non-parametric methodology for performance evaluation and benchmarking. DEA has witnessed a widespread use in many application areas since the publication of the seminal paper by Charnes, Cooper and Rhodes in 1978. However, to the best of our knowledge, no published work formally addressed out-of-sample evaluation in DEA. In this paper, we fill this gap by proposing a framework for the out-of-sample evaluation of decision making units. We tested the performance of the proposed framework in risk assessment and bankruptcy prediction of companies listed on the London Stock Exchange. Numerical results demonstrate that the proposed out-of-sample evaluation framework for DEA is capable of delivering an outstanding performance and thus opens a new avenue for research and applications in risk modelling and analysis using DEA as a non-parametric frontier-based classifier and makes DEA a real contender in industry applications in banking and investment.

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Section: Introductionmentioning

confidence: 99%

An out-of-sample evaluation framework for DEA with application in bankruptcy prediction

Ouenniche

Tone

2017

Ann Oper Res

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“…Along similar lines, Shin and Lee [2002] propose the use of GAs to predict the failure of corporations by using past financial data. In a similar fashion, in Kim and Han [2003], bankruptcy decision rules are determined with the use of GAs and the input of experts' qualitative opinions. They conclude that GAs can outperform alternative metaheuristic solvers (e.g.…”

Section: Bankruptcy Prediction and Financial Distress Predictionmentioning

confidence: 99%

A Survey on Financial Applications of Metaheuristics

2017

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Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, etc. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and reallife combinatorial optimization problems that arise in a number of financial and banking activities. This paper reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. The paper also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.• Applied computing➝Operational research➝Decision analysis • Applied computing➝Law, social and behavioral sciences➝Economics.

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“…They have been used in a number of applications in engineering and social science. Recently, they applied for optimization of the parameters of support vector machine for predicting bankruptcy [75], and hybrid with neural net work for detecting credit card fraud with high accuracy [31], and have been used along with Artificial Immune System for reducing a number of false alarm in credit card fraud detection. GA has been used in credit card fraud detection for minimizing the wrongly classified number of transactions [17].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Analysis on Credit Card Fraud Detection Techniques: Based on Certain Design Criteria

Zareapoor¹,

R²,

Alam³

2012

IJCA

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Financial fraud is increasing significantly with the development of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. The companies and financial institution loose huge amounts due to fraud and fraudsters continuously try to find new rules and tactics to commit illegal actions. Thus, fraud detection systems have become essential for all credit card issuing banks to minimize their losses. The most commonly used fraud detection methods are Neural Network (NN), rule-induction techniques, fuzzy system, decision trees, Support Vector Machines (SVM), Artificial Immune System (AIS), genetic algorithms, K-Nearest Neighbor algorithms. These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. This paper presents a survey of various techniques used in credit card fraud detection and evaluates each methodology based on certain design criteria.

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The discovery of experts' decision rules from qualitative bankruptcy data using genetic algorithms

Cited by 148 publications

References 24 publications

An out-of-sample evaluation framework for DEA with application in bankruptcy prediction

An out-of-sample evaluation framework for DEA with application in bankruptcy prediction

A Survey on Financial Applications of Metaheuristics

Analysis on Credit Card Fraud Detection Techniques: Based on Certain Design Criteria

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