| Summary: | 博士 === 國立中央大學 === 營建管理研究所 === 107 === Financial crisis has raised concerns for years and its effect on companies influence economies globally. The ability to accurately identify the features responsible for business failure is an important issue in financial decision-making. The study made use of 3190 effective financial reports from 55 construction companies over a decade while applying 25 ratios. All the ratios involved each play a crucial role. We proposed a PSO(SIP) algorithm to visualize high-dimensional data as a two dimensional scatter plot. The projected scatter plot allows a straightforward analysis of the inherent structure of clusters within the analyzed data points. It will also assist traditional methods in analyzing ratios by providing visualized images for decision makers to make correct decisions for future problems. In addition, the visualized clusters will provide a better understanding of the relationships among ratios and enhance the study of the correlation between them. Our goal is to determine the factors responsible for distress in the Failed category and factors responsible for growth in the non-Failed category. To achieve our goal, the algorithm is combined with PCA to determine the weights of the features and then adjust and find association rules within the ratios. This method provides better reliability in the identification of the principal features in bankruptcy analysis.
Based on the 25 ratios used, the PSOISST model yields an average accuracy rate of 90%. Applying weights, adjusting and then mining association rules, the model identified return-on-assets, revenue growth rate, earning-per-share, profit margin, operating profit, fixed assets turnover ratio and dependence-on-borrowing as the most important contributors to growth in the non-failed construction companies. On the other hand, for the companies that have failed, the model output eight ratios namely; earnings per share, return on assets, after-tax rate of return, inventory turnover ratio, debt to assets ratio, dependence on borrowing, profit margin, operating profit. Two ratios, dependence-on-borrowing and debt-to-assets-ratio have been identified as very crucial contributors to failure.
Corporate financial distress is a major concern to business sectors worldwide; therefore, combining AI with statistical techniques improves results in mitigating bankruptcy.
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