Forecasting with neural networks
Information and Management
Communications of the ACM
Neural network applications in finance: a review and analysis of literature (1990-1996)
Information and Management
A bibliography of neural network business applications research: 1994–1998
Computers and Operations Research - Neural networks in business
Journal of Systems and Software
Selecting Bankruptcy Predictors Using a Support Vector Machine Approach
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6 - Volume 6
Applying rough sets to market timing decisions
Decision Support Systems - Special issue: Data mining for financial decision making
The language of quarterly reports as an indicator of change in the company's financial status
Information and Management
Sorting through the dot bomb rubble: how did the high-profile e-tailers fail?
International Journal of Information Management: The Journal for Information Professionals
Failure prediction of dotcom companies using hybrid intelligent techniques
Expert Systems with Applications: An International Journal
Business failure prediction model based on grey prediction and rough set theory
WSEAS Transactions on Information Science and Applications
Failure prediction of dotcom companies using neural network-genetic programming hybrids
Information Sciences: an International Journal
Exploring the ncRNA-ncRNA patterns based on bridging rules
Journal of Biomedical Informatics
Dynamic financial distress prediction using instance selection for the disposal of concept drift
Expert Systems with Applications: An International Journal
Principal component case-based reasoning ensemble for business failure prediction
Information and Management
Expert Systems with Applications: An International Journal
International Journal of Intelligent Systems in Accounting and Finance Management
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We conducted an empirical investigation of dot-coms from a financial perspective. Data from the financial statements of 240 such businesses was used to compute financial ratios and the rough sets technique was used to evaluate whether the financial ratios could predict financial health of them based on available data. The most predictive financial ratios were identified and interesting rules concerning the financial ratios and financial health of dot-coms were discovered. It was shown that rough sets performed a satisfactory job of predicting financial health and were more suitable for detecting unhealthy dot-coms than healthy ones.