Article2011

Predicting Business Failure Using an RSF‐based Case‐Based Reasoning Ensemble Forecasting Method

43 citations0/0 files storedJournal of Forecasting

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Financial Distress and Bankruptcy Prediction Forecasting Techniques and Applications AI-based Problem Solving and Planning

Abstract

ABSTRACT Case‐based reasoning (CBR) is considered a vital methodology in the current business forecasting area because of its simplicity, competitive performance with modern methods, and ease of pattern maintenance. Business failure prediction (BFP) is an effective tool that helps business people and entrepreneurs make more precise decisions in the current crisis. Using CBR as a basis for BFP can improve the tool's utility because CBR has the potential advantage in making predictions as well as suggestions compared with other methods. Recent studies indicate that an ensemble of various techniques has the possibility of improving the performance of predictive model. This research focuses on an early investigation on predicting business failure using a CBR ensemble (CBRE) forecasting method constructed from the use of random similarity functions (RSF), dubbed RSF‐based CBRE. Four issues are discussed: (i) the reasons for the use of RSF as the basis in the CBRE forecasting method for BFP; (ii) the means to construct the RSF‐based CBRE forecasting method for BFP; (iii) the empirical test on sensitivity of the RSF‐based CBRE to the number of member CBR predictors; and (iv) performance assessment of the ensemble forecasting method. Results of the RSF‐based CBRE forecasting method were statistically validated by comparing them with those of multivariate discriminant analysis, logistic regression, single CBR, and a linear support vector machine. The results from Chinese hotel BFP indicate that the RSF‐based CBRE forecasting method could significantly improve CBR's upper limit of predictive capability. Copyright © 2011 John Wiley & Sons, Ltd.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1966Financial Ratios As Predictors of Failure 2005Corporate Financial Distress and Bankruptcy 1998How effective are neural networks at forecasting and prediction? A review and evaluation 2002A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction 1997Bankruptcy prediction using case-based reasoning, neural networks, and discriminant analysis 2008Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach 1996Integration of case-based forecasting, neural network, and discriminant analysis for bankruptcy prediction

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