INCORPORATING ARTIFICIAL NEURAL NETWORKS AND EVOLUTION STRATEGIES ON FINANCIAL DISTRESS RULES EXTRACTION
學年 101
學期 2
發表日期 2013-07-07
作品名稱 INCORPORATING ARTIFICIAL NEURAL NETWORKS AND EVOLUTION STRATEGIES ON FINANCIAL DISTRESS RULES EXTRACTION
作品名稱（其他語言）
著者 Chang, Ying-Hua; Meng, Jui-Hsien
作品所屬單位 淡江大學資訊管理學系
出版者
會議名稱 2013 International Conference on Business and Information
會議地點 Bali, Indonesia
摘要 Business environments have been changed for years and more complicated than before. There are more impacts and many difficulties to companies because of financial distress. In order to reduce the impact, it is important to find out the causes of financial distress, and give alert to companies before it get distressed. There were studies that use basic financial analysis, or single data-mining method exploring causes of financial distress. This study based on dynamic financial states, and finds the characteristics and rules of financial distress. This study combines Back-propagation Artificial Neural Networks (BPN) and Evolution Strategies (ES), extracts rules of financial distress, and incorporates with the results of Markov process analysis, in order to develop an optimized financial-distress alert model. This study utilizes supervised learning networks to evaluate the risk of financial distress of the companies, and find out the rules of financial distress by adding the prior evaluated results to evolution strategies.
關鍵字 Financial distress;Financial alert;Back-propagation Artificial neural network
語言 en
收錄於
會議性質
校內研討會地點
研討會時間 20130707~20130709
通訊作者
國別 IDN
公開徵稿
出版型式
出處 Proceedings of International Conference on Business and Information
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