scholarly journals A new VIKOR-based in-sample-out-of-sample classifier with application in bankruptcy prediction

2019 ◽  
Vol 296 (1-2) ◽  
pp. 495-512 ◽  
Author(s):  
Jamal Ouenniche ◽  
Kais Bouslah ◽  
Blanca Perez-Gladish ◽  
Bing Xu
Keyword(s):  
Predictive Analytics ◽  
Bankruptcy Prediction ◽  
Business Analytics ◽  
Discrete Variables ◽  
Classification Problems ◽  
Financial Industry ◽  
Class Prediction ◽  
Risk Class ◽  
Out Of Sample ◽  
Continuous And Discrete Variables

AbstractNowadays, business analytics has become a common buzzword in a range of industries, as companies are increasingly aware of the importance of high quality predictions to guide their pro-active planning exercises. The financial industry is amongst those industries where predictive analytics techniques are widely used to predict both continuous and discrete variables. Conceptually, the prediction of discrete variables comes down to addressing sorting problems, classification problems, or clustering problems. The focus of this paper is on classification problems as they are the most relevant in risk-class prediction in the financial industry. The contribution of this paper lies in proposing a new classifier that performs both in-sample and out-of-sample predictions, where in-sample predictions are devised with a new VIKOR-based classifier and out-of-sample predictions are devised with a CBR-based classifier trained on the risk class predictions provided by the proposed VIKOR-based classifier. The performance of this new non-parametric classification framework is tested on a dataset of firms in predicting bankruptcy. Our findings conclude that the proposed new classifier can deliver a very high predictive performance, which makes it a real contender in industry applications in finance and investment.

scholarly journals A new EDAS-based in-sample-out-of-sample classifier for risk-class prediction

2019 ◽  
Vol 57 (2) ◽  
pp. 314-323 ◽  
Author(s):  
Jamal Ouenniche ◽  
Oscar Javier Uvalle Perez ◽  
Aziz Ettouhami
Keyword(s):  
Predictive Analytics ◽  
Predictive Performance ◽  
Data Set ◽  
Content Type ◽  
Risk Class ◽  
Design Elements ◽  
Classification Framework ◽  
Out Of Sample ◽  
Parametric Classifier ◽  
Non Parametric

PurposeNowadays, the field of data analytics is witnessing an unprecedented interest from a variety of stakeholders. The purpose of this paper is to contribute to the subfield of predictive analytics by proposing a new non-parametric classifier.Design/methodology/approachThe proposed new non-parametric classifier performs both in-sample and out-of-sample predictions, where in-sample predictions are devised with a new Evaluation Based on Distance from Average Solution (EDAS)-based classifier, and out-of-sample predictions are devised with a CBR-based classifier trained on the class predictions provided by the proposed EDAS-based classifier.FindingsThe performance of the proposed new non-parametric classification framework is tested on a data set of UK firms in predicting bankruptcy. Numerical results demonstrate an outstanding predictive performance, which is robust to the implementation decisions’ choices.Practical implicationsThe exceptional predictive performance of the proposed new non-parametric classifier makes it a real contender in actual applications in areas such as finance and investment, internet security, fraud and medical diagnosis, where the accuracy of the risk-class predictions has serious consequences for the relevant stakeholders.Originality/valueOver and above the design elements of the new integrated in-sample-out-of-sample classification framework and its non-parametric nature, it delivers an outstanding predictive performance for a bankruptcy prediction application.

Taguchi-Particle Swarm Optimization for Numerical Optimization

2012 ◽  
pp. 44-59
Author(s):  
T. O. Ting ◽  
H. C. Ting ◽  
T. S. Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Taguchi Method ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Computational Cost ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Discrete Variables ◽  
Swarm Optimization ◽  
Continuous And Discrete Variables

In this work, a hybrid Taguchi-Particle Swarm Optimization (TPSO) is proposed to solve global numerical optimization problems with continuous and discrete variables. This hybrid algorithm combines the well-known Particle Swarm Optimization Algorithm with the established Taguchi method, which has been an important tool for robust design. This paper presents the improvements obtained despite the simplicity of the hybridization process. The Taguchi method is run only once in every PSO iteration and therefore does not give significant impact in terms of computational cost. The method creates a more diversified population, which also contributes to the success of avoiding premature convergence. The proposed method is effectively applied to solve 13 benchmark problems. This study’s results show drastic improvements in comparison with the standard PSO algorithm involving continuous and discrete variables on high dimensional benchmark functions.

Business Analytics and Big Data

2015 ◽  
pp. 1-11 ◽  
Author(s):  
Dennis T. Kennedy ◽  
Dennis M. Crossen ◽  
Kathryn A. Szabat
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Quantitative Methods ◽  
Business Processes ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Database Systems ◽  
Three Dimensions ◽  
Business Analytics ◽  
The People

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.

Book-Tax Differences as an Indicator of Financial Distress

2013 ◽  
Vol 27 (3) ◽  
pp. 469-489 ◽  
Author(s):  
Tracy J. Noga ◽  
Anne L. Schnader
Keyword(s):  
Financial Distress ◽  
Hazard Model ◽  
Time Frame ◽  
Bankruptcy Prediction ◽  
Related Information ◽  
Ex Ante ◽  
Out Of Sample ◽  
Sample Testing

SYNOPSIS: We contend that tax-related information, which has not yet been considered by extant research, can significantly improve bankruptcy prediction. We investigate the association between abnormal changes in book-tax differences (BTDs) and bankruptcy using a hazard model and out-of-sample testing as in Shumway (2001). We find that information regarding abnormal changes in BTDs significantly increases our ability to ex ante identify firms that have an increased likelihood of going bankrupt in the coming five-year period. The information provided by BTDs significantly adds information to traditional models for predicting bankruptcy, such as that proposed by Ohlson (1980), and also expands the prediction window beyond the traditional two-year time frame.

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