Financial distress prediction based on similarity weighted voting CBR

Authors:
Jie Sun;Xiao-Feng Hui
Affiliations:
School of Management, Harbin Institute of Technology, Harbin, HeiLongJiang Province, China;School of Management, Harbin Institute of Technology, Harbin, HeiLongJiang Province, China
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

Citing 4
Cited 22

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
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters

Expert Systems with Applications: An International Journal
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

Listed companies' financial distress prediction based on weighted majority voting combination of multiple classifiers

Expert Systems with Applications: An International Journal
Financial distress prediction based on OR-CBR in the principle of k-nearest neighbors

Expert Systems with Applications: An International Journal
Financial distress early warning based on group decision making

Computers and Operations Research
Ranking-order case-based reasoning for financial distress prediction

Knowledge-Based Systems
Gaussian case-based reasoning for business failure prediction with empirical data in China

Information Sciences: an International Journal
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
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
An effort prediction framework for software defect correction

Information and Software Technology
On sensitivity of case-based reasoning to optimal feature subsets in business failure prediction

Expert Systems with Applications: An International Journal
Predicting business failure using classification and regression tree: An empirical comparison with popular classical statistical methods and top classification mining methods

Expert Systems with Applications: An International Journal
On performance of case-based reasoning in Chinese business failure prediction from sensitivity, specificity, positive and negative values

Applied Soft Computing
Predicting business failure using forward ranking-order case-based reasoning

Expert Systems with Applications: An International Journal
Aggregating multiple classification results using Choquet integral for financial distress early warning

Expert Systems with Applications: An International Journal
Case-based reasoning ensemble and business application: A computational approach from multiple case representations driven by randomness

Expert Systems with Applications: An International Journal
Hybrid intelligent parameter estimation based on grey case-based reasoning for laminar cooling process

Engineering Applications of Artificial Intelligence
MCELCCh-FDP: Financial distress prediction with classifier ensembles based on firm life cycle and Choquet integral

Expert Systems with Applications: An International Journal
Financial distress prediction using support vector machines: Ensemble vs. individual

Applied Soft Computing
Financial ratio selection for business crisis prediction

Expert Systems with Applications: An International Journal
Integration Of Random Sample Selection, Support Vector Machines And Ensembles For Financial Risk Forecasting With An Empirical Analysis On The Necessity Of Feature Selection

International Journal of Intelligent Systems in Accounting and Finance Management
Predicting financial distress and corporate failure: A review from the state-of-the-art definitions, modeling, sampling, and featuring approaches

Knowledge-Based Systems

Quantified Score

Hi-index	0.02

Visualization

Abstract

Financial distress prediction is an important research topic in both academic and practical world. This paper proposed a financial distress prediction model based on similarity weighted voting case-based reasoning (CBR), which consists of case representation, similar case retrieval and combination of target class. An empirical study was designed and carried out by using Chinese listed companies’ three-year data before special treatment (ST) and adopting leave-one-out and grid-search technique to find the model’s good parameters. The experiment result of this model was compared with multi discriminant analysis (MDA), Logit, neural networks (NNs) and support vector machine (SVM), and it was concluded that similarity weighted voting CBR model has very good predictive ability for enterprises which will probably run into financial distress in less than two years, and it is more suitable for short-term financial distress prediction.