Ranking-order case-based reasoning for financial distress prediction

This paper addresses a new method of financial distress prediction using case-based reasoning (CBR) with financial ratios derived from financial statements. The aim of this work presented here is threefold. First, we make a brief review on financial distress prediction from the view of categories of the earliest applied models, models that generate If-Then rules, the most widely applied models historically, the most hotly researched models recently, and the most potential models. On the other hand, we make use of ranking-order information of distance between target case and each historical case on each feature to generate similarities between pairwise cases. The similarity between two cases on each feature is calculated by corresponding ranking-order information of distance in the first place, followed by a weighted integration to generate the final similarity between two cases. The CBR system that employs the new similarity measure model in the frame of k-nearest neighbor (k-NN) is named as ranking-order case-based reasoning (ROCBR). At the same time, we introduce ROCBR in financial distress prediction, and analyze the obtained results of financial distress prediction of Chinese listed companies, comparing them with those provided by the other three well-known CBR models with Euclidean distance, Manhuttan distance, and inductive approach as its heart of retrieval. The three compared CBR models are called as ECBR, MCBR, and ICBR, respectively. The two famous statistical models of logistic regression (Logit) and multi-variant discriminate analysis (MDA) are also employed for a comparison. The financial distress dataset used in the experiments come from Shanghai Stock Exchange and Shenzhen Stock Exchange. Empirical results indicate that ROCBR outperforms ECBR, MCBR, ICBR, MDA, and Logit significantly in financial distress prediction of Chinese listed companies I year prior to distress, if irrelevant information among features has been handled effectively. (C) 2008 Elsevier B.V. All rights reserved.