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論文名稱:以機器學習方式辨認財務危機公司 -納入重大訊息之考量
論文名稱(外文):Identifying Financial Distress Companies by Machine Learning: Under the Considerations of Material Information
指導教授(外文):KAO, LI-HAN
外文關鍵詞:Financial DistressMaterial InformationMachine LearningText Mining
  • 被引用被引用:1
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The financial distress prediction models developed by the previous s were generally divided into two categories, one is to use financial indicators to identify the signs of company bankruptcy from the financial side of the company, and the other is to add non-financial information. Such non-financial factors including corporate governance-related indicators are usually combined with financial aspects to enhance the accuracy of the prediction of company financial distress. As a result of recent internet development and the generation of huge digital textual information, the textual information has also increased its importance and impact on determining financial crises. Under the circumstances, this study focuses on incorporating material information retrieved from the Public Information Observation Post System (PIO) into the financial distress prediction model to provide a multi-orientation evaluation for different stakeholders. The empirical results show that companies are more likely to face financial crises when they release major messages containing specific keywords and more negative words. Besides, the neutral terms in major messages can be used as a reference to exclude messages that are not related to financial crises to narrow the scope of messages that may generate financial crises. Finally, four machine learning methods are used to train and validate the accuracy of the financial crisis prediction model after incorporating keywords in the critical messages. The accuracy rates of the four methods were 73.35% for Random Forest, 65.96% for Logistic Regression, 63.72% for Support Vector Machine, and 55.15% for K-Nearest Neighbor algorithm. The final results showed that Random Forest was the best prediction model for financial distress.
摘要 I
Abstract II
目錄 III
表目錄 V
圖目錄 VI
第一章 緒論 1
第一節 研究背景及動機 1
第二節 論文架構 4
第二章 文獻回顧 5
第一節 財務危機 5
一、財務危機定義 5
二、財務危機相關研究 7
第二節 資訊揭露與相關管道 9
一、資訊揭露與透明度 9
二、公開資訊觀測站之發展沿革與相關法令 10
三、國外資訊揭露方式與規範 13
第三節 資料探勘與機器學習 17
一、資料探勘定義與應用 17
二、機器學習定義、分析模式及相關文獻探討 17
三、文字探勘定義及相關文獻探討 18
第三章 研究方法與設計 20
第一節 研究樣本與流程設計 20
第二節 文字探勘方法介紹 21
第三節 變數定義及迴歸驗證模型 21
第四節 機器學習方法介紹 23
一、羅吉斯迴歸(Logistic Regression) 23
二、隨機森林(Random Forest) 24
三、支援向量機(Support Vector Machine, SVM) 24
四、K-近鄰演算法(K-Nearest Neighbor, K-NN) 25
第四章 實證結果 26
第一節 敘述性統計 26
第二節 羅吉斯迴歸 31
一、模型選定 31
二、假說1實證結果與分析 33
三、假說2、假說3實證結果與分析 36
四、小結 40
第三節 以機器學習方式建立預測模型 40
第五章 結果與建議 43
第一節 結論 43
第二節 研究貢獻 44
第三節 研究限制 44
第四節 未來研究建議 45
參考文獻 46

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