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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/83846

    Title: 危機預測之探討–以財務與資訊優勢者異常事件為例;The Research of Crisis Forecasting–Take Financial and Information Superior Events for Examples
    Authors: 謝佳芳;Hsieh, Chia-Fang
    Contributors: 會計研究所
    Keywords: 公司治理;危機預測;機器學習;資訊優勢者異常事件;Corporate Governance;Crisis Forecasting;Machine Learning;Information Superior Abnormal Events
    Date: 2020-07-20
    Issue Date: 2020-09-02 17:13:36 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 資本市場瞬息萬變,企業為了永續經營,除了要維持彈性以因應變動,如何避免危機也是一個重要的課題,本研究以Altman (1968)中的Z-Score建立財務變數模型,再建立一個整合財務與資訊優勢者異常事件變數的模型,並將上述變數丟入羅吉斯迴歸、隨機森林與決策樹等三種不同的模型進行比較分析。
    ;Crisis Forecasting have always been a discussion thourgh out the history. People have been searching for better variables to predict crisis events in a more precise way. In this study, we find out that there is a group of people can retrieve information before the capital market. That means they can do some strategies before outsiders, that’s the definition of information superior. This study shows that the higher the extent of shares as collateral by the board of directors and the lower the extent of shares holded by the managers or the board of directors, the possibilities of crisis go higher. In addition, the empirical results show that the abnormal downsize of stock prices appears before two years of crisis events as the decline of the extent of shares holded by the managers and the maneger turnover. That means the change of managers and the decline percentage of shares holding result in higher agency problems, which are signals of crisis events.
    As a results, the supplement of events in information superiors make the accuracy, AUC and F1 Score in Logistic Regression, Random Forest and Decision Tree gets higher than pure Z-Score model. The variables we use have even greater ability to Crisis forecasting than pure Z-Score model in two or three years before crisis events. In the combined evaluation between models, Random Forest have better accuracy than Logistic Regression, with Decision Tree the lowest one.
    Appears in Collections:[會計研究所 ] 博碩士論文

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