Article2009

Forecasting business failure in China using case‐based reasoning with hybrid case respresentation

31 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 a very effective and easily understandable method for solving real‐world problems. Business failure prediction (BFP) is a forecasting tool that helps people make more precise decisions. CBR‐based BFP is a hot topic in today's global financial crisis. Case representation is critical when forecasting business failure with CBR. This research describes a pioneer investigation on hybrid case representation by employing principal component analysis (PCA), a feature extraction method, along with stepwise multivariate discriminant analysis (MDA), a feature selection approach. In this process, sample cases are represented with all available financial ratios, i.e., features. Next, the stepwise MDA is used to select optimal features to produce a reduced‐case representation. Finally, PCA is employed to extract the final information representing the sample cases. All data signified by hybrid case representation are recorded in a case library, and the k‐nearest‐neighbor algorithm is used to make the forecasting. Thus we constructed a hybrid CBR (HCBR) by integrating hybrid case representation into the forecasting tool. We empirically tested the performance of HCBR with data collected for short‐term BFP of Chinese listed companies. Empirical results indicated that HCBR can produce more promising prediction performance than MDA, logistic regression, classical CBR, and support vector machine. Copyright © 2009 John Wiley & Sons, Ltd.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 2005Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters 1997Credit risk measurement: Developments over the last 20 years 2007Using neural network ensembles for bankruptcy prediction and credit scoring 1999Business failure prediction using rough sets 1998How effective are neural networks at forecasting and prediction? A review and evaluation 2005Hybrid genetic algorithms and support vector machines for bankruptcy prediction

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2021Explanatory Factors of Business Failure: Literature Review and Global Trends 2011Predicting business failure using support vector machines with straightforward wrapper: A re-sampling study 2010Predicting business failure using forward ranking-order case-based reasoning

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