Abstract
The primary objective of any classification model is to categorize observations into two or more distinct groups to predict outcomes—such as determining whether a company is likely to remain solvent or face bankruptcy (default vs. non-default). The study focuses on predicting the future performance and behavioral patterns of companies by utilizing both parametric and non-parametric statistical methods. Parametric methods rely on specific assumptions regarding the distribution of data to estimate parameters for problem-solving. In contrast, non-parametric approaches, such as tree classification (Decision Trees), offer flexibility by not assuming a specific data distribution. By comparing these methodologies, the research aims to enhance the accuracy of financial forecasting, providing stakeholders with reliable tools to anticipate and mitigate the risks of corporate bankruptcy through data-driven insights.
DOI
10.33095/jeas.v14i49.1373
Subject Area
Statistical
First Page
295
Last Page
315
Rights
http://creativecommons.org/licenses/by-nc-nd/4.0
Recommended Citation
Redha, S. M., & Diab, W. S. (2008). Using Statistical Methods and Decision Tree Classification to Classify and Predict Corporate Financial Bankruptcy. Journal of Economics and Administrative Sciences, 14(49), 295-315. https://doi.org/10.33095/jeas.v14i49.1373
