Construction financial crisis warning model using data mining
Financial crisis of construction companies not only influences the company itself, but also the shareholders, debtors, administration authorities, suppliers, employees, banks and the entire society. This paper employs artificial neural network of data mining and decision tree algorithm to build financial crisis warning model. The research results show that, forecasting performance of artificial neural network is better than that of decision tree model, hence, “financial statement average warning model established through artificial neural network based on the average revenue of the past three years before financial crisis has better forecasting performance than the “annual report forecast model. Factor analysis is employed to select common factor in 1 year before financial crisis, and the critical variables of financial crisis are found to be: debt-to-equity ratio, quick ratio, borrowing dependence, inventory turnover ratio, and earnings per share. According to the decision tree rule, variables differentiahle to financial crisis warning are debt-to-equity ratio, earnings per share, and borrowing dependence.
Data mining real estate Financial crisis warning
Pan Nai Hsin
Department of construction engineering,National Yunlin University of Science and Technology
国际会议
南京
英文
1139-1143
2009-10-29（万方平台首次上网日期，不代表论文的发表时间）