Construct an Early Warning System of Financial Distress in the Information and Communication Industry

碩士 === 國立中正大學 === 會計與資訊科技研究所 === 98 === Financial tsunami raged round the world in 2008. Many firms have closed down in Taiwan. According to statistics, 11 firms withdraw from the stock exchange market in 2008. Eleven firms which have three for the information and communication industry. Therefore,...

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Bibliographic Details
Main Authors: Hung-Ju Lin, 林宏儒
Other Authors: Hsu-Che WU
Format: Others
Language:zh-TW
Published: 2010
Online Access:http://ndltd.ncl.edu.tw/handle/37424450821203952139
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Summary:碩士 === 國立中正大學 === 會計與資訊科技研究所 === 98 === Financial tsunami raged round the world in 2008. Many firms have closed down in Taiwan. According to statistics, 11 firms withdraw from the stock exchange market in 2008. Eleven firms which have three for the information and communication industry. Therefore, this study will establish industry-specific model for financial distress prediction. The early warning model is also a reference to understand the company''s financial status for managers, creditors and investors. The study use logistic regression analysis to construct an early warning system of financial distress. First, select the appropriate variables through the statistical test. Next, adding variables to established model by forward stepwise variable selection method. ECOIR models are the best warning of this study model, its use of variables including current ratio, earnings per share, cash flow ratio and debt ratio, business groups sales ratio, the change of accountants, and interest rate. The results show that both non-financial and economic indicators predicted the financial distress is indeed helpful. ECOIR model is been verified by samples from 2008 to 2009 .ECOIR shows the probability of correct classification was 93.33% in the distress year, and 80.00% next year. In addition, we found that distress firms have the following characteristics: high rates of replacement of accountants, earnings per share of low, cash flow ratio is low, and business groups sales ratio is low. Finally, the follow-up studies can use different statistical methods, information technology to construct financial distress prediction model.