Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
The nature of statistical learning theory
The nature of statistical learning theory
Self organizing neural networks for financial diagnosis
Decision Support Systems
Hybrid neural network models for bankruptcy predictions
Decision Support Systems
Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Foundations of Soft Case-Based Reasoning
Foundations of Soft Case-Based Reasoning
Expert Systems with Applications: An International Journal
Mining competent case bases for case-based reasoning
Artificial Intelligence
Short communication: Data mining method for listed companies' financial distress prediction
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Financial distress early warning based on group decision making
Computers and Operations Research
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
Financial distress prediction based on similarity weighted voting CBR
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
An application of support vector machine to companies' financial distress prediction
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Majority voting combination of multiple case-based reasoning for financial distress prediction
Expert Systems with Applications: An International Journal
Financial distress prediction based on serial combination of multiple classifiers
Expert Systems with Applications: An International Journal
Loss and gain functions for CBR retrieval
Information Sciences: an International Journal
Predicting business failure using multiple case-based reasoning combined with support vector machine
Expert Systems with Applications: An International Journal
Business failure prediction using hybrid2 case-based reasoning (H2CBR)
Computers and Operations Research
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Predicting business failure using forward ranking-order case-based reasoning
Expert Systems with Applications: An International Journal
Introducing attribute risk for retrieval in case-based reasoning
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example
Knowledge-Based Systems
A multi-agent system for web-based risk management in small and medium business
Expert Systems with Applications: An International Journal
Bankruptcy prediction models based on multinorm analysis: An alternative to accounting ratios
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Financial ratio selection for business crisis prediction
Expert Systems with Applications: An International Journal
Improving user experience with case-based reasoning systems using text mining and Web 2.0
Expert Systems with Applications: An International Journal
International Journal of Intelligent Systems in Accounting and Finance Management
Financial Distress Prediction of Chinese-Listed Companies Based on PCA and WNNs
International Journal of Advanced Pervasive and Ubiquitous Computing
Novel feature selection methods to financial distress prediction
Expert Systems with Applications: An International Journal
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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 1 year prior to distress, if irrelevant information among features has been handled effectively.