Bankruptcy prediction for Japanese firms: using Multiple Criteria Linear Programming data mining approach

<h1 style="margin: 0in 0.5in 0pt; text-align: justify; page-break-after: auto; mso-pagination: none;">Our study evaluates a multiple criteria linear programming (MCLP) and other data mining approaches to predict auditor changes using a portfolio of financial statement measures to capture financial distress. The results of the MCLP approach and the other data mining approaches show that these methods perform reasonably well to predict auditor changes using financial distress variables. Overall accuracy rates are more than 60 percent, and true positive rates exceed 80 percent. Our study is designed to establish a starting point for auditor-change prediction using financial distress variables. Further research should incorporate additional explanatory variables and a longer study period to improve prediction rates.</h1>

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Bankruptcy Prediction Studies Across Countries Using Multiple Criteria Linear Programming (MCLP) and Other Data Mining Approaches

10.1007/978-3-030-73443-5_76-1 ◽

2021 ◽

pp. 1-14

Author(s):

Wikil Kwak ◽

Yong Shi

Keyword(s):

Data Mining ◽

Linear Programming ◽

Bankruptcy Prediction ◽

Multiple Criteria

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Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch004 ◽

2008 ◽

pp. 26-49 ◽

Cited By ~ 1

Author(s):

Yong Shi ◽

Yi Peng ◽

Gang Kou ◽

Zhengxin Chen

Keyword(s):

Data Mining ◽

Linear Programming ◽

Credit Card ◽

Programming Model ◽

Real Life ◽

Multiple Criteria ◽

Linear Programming Model ◽

Multiple Criteria Optimization ◽

Research Challenges ◽

Challenges And Opportunities

This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and opportunities for the data mining community. To achieve these goals, this chapter first introduces the basic notions and mathematical formulations for multiple criteria optimization-based classification models, including the multiple criteria linear programming model, multiple criteria quadratic programming model, and multiple criteria fuzzy linear programming model. Then it presents the real-life applications of these models in credit card scoring management, HIV-1 associated dementia (HAD) neuronal dam-age and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.

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Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications

Data Mining Applications for Empowering Knowledge Societies ◽

10.4018/978-1-59904-657-0.ch001 ◽

2009 ◽

pp. 1-25

Author(s):

Yong Shi ◽

Yi Peng ◽

Gang Kou ◽

Zhengxin Chen

Keyword(s):

Data Mining ◽

Linear Programming ◽

Programming Model ◽

Neuronal Damage ◽

Real Life ◽

Multiple Criteria ◽

Linear Programming Model ◽

Multiple Criteria Optimization ◽

Research Challenges ◽

Challenges And Opportunities

This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria programming (MCP) to solve data mining problems, and outlines some research challenges and opportunities for the data mining community. To achieve these goals, this chapter first introduces the basic notions and mathematical formulations for multiple criteria optimization- based classification models, including the multiple criteria linear programming model, multiple criteria quadratic programming model, and multiple criteria fuzzy linear programming model. Then it presents the real-life applications of these models in credit card scoring management, HIV-1 associated dementia (HAD) neuronal damage and dropout, and network intrusion detection. Finally, the chapter discusses research challenges and opportunities.

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Predicting Material Weaknesses In Internal Control Systems After The Sarbanes-Oxley Act Using Multiple Criteria Linear Programming And Other Data Mining Approaches

Journal of Applied Business Research (JABR) ◽

10.19030/jabr.v25i6.999 ◽

2011 ◽

Vol 25 (6) ◽

Author(s):

Wikil Kwak ◽

Susan Eldridge ◽

Yong Shi ◽

Gang Kou

Keyword(s):

Data Mining ◽

Linear Programming ◽

Internal Control ◽

Foreign Currency ◽

Predictive Ability ◽

Cash Flows ◽

Multiple Criteria ◽

Font Size ◽

Sarbanes Oxley ◽

Material Weaknesses

<h1 style="TEXT-JUSTIFY: inter-ideograph; TEXT-ALIGN: justify; MARGIN: 0in 0.5in 0pt">Our study proposes a multiple criteria linear programming (MCLP) and other data mining methods to predict material weaknesses in a firm’s internal control system after the Sarbanes-Oxley Act (SOX) using 2003-2004 U.S. data.  The results of the MCLP and other data mining approaches in our prediction study show that the MCLP method performs better overall than the other data mining approaches using financial and other data from the Form 10-K report.  Consistent with prior research, firms that disclosed material weaknesses in their SOX Section 302 disclosures were more complex (based on the existence of foreign currency translations), more often used Big 4 auditors, and had lower operating cash flows-to-total assets ratios than the non-material weakness control firms.  Because of mixed results on several profitability measures and marginal predictive ability for the MCLP and other methods used, more research is needed to identify firm characteristics that help investors, auditors, and others predict material weaknesses.</h1>

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Data Classification via a New Data Mining Approach: Multiple Criteria Programming with Multiple Kernels

2009 Asia-Pacific Conference on Information Processing ◽

10.1109/apcip.2009.86 ◽

2009 ◽

Author(s):

Wenzhou Wang ◽

Liwei Wei

Keyword(s):

Data Mining ◽

Data Classification ◽

Multiple Criteria ◽

Data Mining Approach ◽

Multiple Criteria Programming

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A Robust Data-Mining Approach to Bankruptcy Prediction

Journal of Forecasting ◽

10.1002/for.1232 ◽

2011 ◽

Vol 31 (6) ◽

pp. 504-523 ◽

Cited By ~ 17

Author(s):

Mehdi Divsalar ◽

Habib Roodsaz ◽

Farshad Vahdatinia ◽

Ghassem Norouzzadeh ◽

Amir Hossein Behrooz

Keyword(s):

Data Mining ◽

Bankruptcy Prediction ◽

Data Mining Approach

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DATA MINING VIA MULTIPLE CRITERIA LINEAR PROGRAMMING: APPLICATIONS IN CREDIT CARD PORTFOLIO MANAGEMENT

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622002000038 ◽

2002 ◽

Vol 01 (01) ◽

pp. 131-151 ◽

Cited By ~ 91

Author(s):

YONG SHI ◽

YI PENG ◽

WEIXUAN XU ◽

XIAOWO TANG

Keyword(s):

Data Mining ◽

Linear Programming ◽

Portfolio Management ◽

Credit Card ◽

Real Life ◽

Multiple Criteria ◽

Business Decisions ◽

Business World ◽

Linear Discriminant ◽

Technology Tool

Data mining becomes a cutting-edge information technology tool in today's competitive business world. It helps the company discover previously unknown, valid, and actionable information from various and large databases for crucial business decisions. This paper provides a promising approach of data mining to classify the credit cardholders' behavior through multiple criteria linear programming. After reviewing the history of linear discriminant analyses, we will describe first a model for classifying two-group (e.g. bad or good) credit cardholder behaviors, and then a three-group (e.g. bad, normal, or good) credit model. Besides the discussion of the modeling structure, we will utilize the well-known commercial software package SAS to implement this technology by using a real-life credit card data warehouse. A number of potential business and financial applications will be finally summarized.

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