Bankruptcy prediction using neural networks
Decision Support Systems - Special issue on neural networks for decision support
Hybrid neural network models for bankruptcy predictions
Decision Support Systems
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Decision Support Systems - Special issue: Data mining for financial decision making
Discovering Statistics Using SPSS
Discovering Statistics Using SPSS
Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks
Decision Support Systems
Using neural networks and data mining techniques for the financial distress prediction model
Expert Systems with Applications: An International Journal
Decision Tree Method in Financial Analysis of Listed Logistics Companies
ICICTA '10 Proceedings of the 2010 International Conference on Intelligent Computation Technology and Automation - Volume 01
An investigation of data and text mining methods for real world deception detection
Expert Systems with Applications: An International Journal
An elusive antecedent of superior firm performance: The knowledge management factor
Decision Support Systems
Comparative analysis of data mining methods for bankruptcy prediction
Decision Support Systems
IT assets, organization capital and market power: Contributions to business value
Decision Support Systems
An analytic approach to better understanding and management of coronary surgeries
Decision Support Systems
| Hi-index | 12.05 |
Determining the firm performance using a set of financial measures/ratios has been an interesting and challenging problem for many researchers and practitioners. Identification of factors (i.e., financial measures/ratios) that can accurately predict the firm performance is of great interest to any decision maker. In this study, we employed a two-step analysis methodology: first, using exploratory factor analysis (EFA) we identified (and validated) underlying dimensions of the financial ratios, followed by using predictive modeling methods to discover the potential relationships between the firm performance and financial ratios. Four popular decision tree algorithms (CHAID, C5.0, QUEST and C&RT) were used to investigate the impact of financial ratios on firm performance. After developing prediction models, information fusion-based sensitivity analyses were performed to measure the relative importance of independent variables. The results showed the CHAID and C5.0 decision tree algorithms produced the best prediction accuracy. Sensitivity analysis results indicated that Earnings Before Tax-to-Equity Ratio and Net Profit Margin are the two most important variables.