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題名:應用公開資訊預測台灣公司違約機率
作者:彭俊能
作者(外文):Chun-Neng Peng
校院名稱:國立東華大學
系所名稱:企業管理學系
指導教授:林金龍
學位類別:博士
出版日期:2014
主題關鍵詞:新聞資訊違約機率離散時間危險模型經驗貝氏貝氏網絡News InformationDefault RatesDiscrete Time Hazard ModelEmpirical BayesBayesian Networks
原始連結:連回原系統網址new window
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企業違約的情況受到許多因素影響,這些因素可以大約分為內部的企業本身因素與外在的環境。企業本身的因素可用財報變數衡量,透過計量方法估計其對違約機率的影響。外在因素對違約率的影響則因包含量性與質性資料,很難準確估計。本文嘗試以代表質性的新聞資訊,利用統計方法中的經驗貝氏與貝氏網絡模型估計其對企業違約機率的影響。在實證模型驗證中發現,未包含新聞資訊估算之違約高點對應新聞事件發生時點有相當程度的落差,即新聞訊息對企業違約有一定的影響程度。將新聞量化之後的結合模型,預測企業違約的能力優於未包含新聞資訊之模型,因此適當適時的利用新聞資訊以增進預測企業違約的準確度確實有其必要性。
Corporate defaults are often affected by many factors that are roughly divided into the two types: internal factors and external factors. Internal factors can be measured precisely with firm-specific financial statistics while external factors contain qualitative data, like related news. There are considerable number of timely information from news which affects the default probability of corporates. Efficiently extracting the information contained in the news is the main focus of this study.
We proposes to use Empirical Bayes and Bayesian network to extract the information contained within news and then to estimate its impact on the default probability of corporates. Empirical analysis finds that the news information has a significant impact on the corporate default rate prediction. Adding the news variable does improve the forecast precision and prove its usefulness.
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