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研究生:林家呈
研究生(外文):LIN,JIA-CHENG
論文名稱:一個結合文字探勘與一類別分類器之過度自信執行長預測模型
論文名稱(外文):Building Overconfident CEO Prediction Model by Combining Text Mining and One Class Classifier
指導教授:許育峯
指導教授(外文):HSU,YU-FENG
口試委員:林維暘吳徐哲
口試委員(外文):LIN,WEI-YANGWU,SYU-JHE
口試日期:2023-12-19
學位類別:碩士
校院名稱:國立中正大學
系所名稱:會計與資訊科技研究所
學門:商業及管理學門
學類:會計學類
論文種類:學術論文
論文出版年:2023
畢業學年度:112
語文別:中文
論文頁數:170
中文關鍵詞:文字探勘過度自信過度自信執行長預測機器學習
外文關鍵詞:Text MiningOverconfidenceOverconfident CEO PredictionsMachine Learning
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透過過去研究結果得知,過度自信執行長會對於公司帶來一些好處,但也帶來許多負面影響。而對於多數的投資人而言,因為沒有公司內部的訊息,而且也不容易接近公司的執行長,因此很難識別一家公司是否為過度自信執行長所領導,遑論避免由過度自信執行長所引起的問題。因此為了協助公司外部的投資人判斷一家公司是否為過度自信執行長所領導,本研究針對美國上市公司的文件資料進行研究,結合文字探勘技術與一類別分類器建立過度自信執行長的預測分類模型,協助投資者評估風險,避免錯誤投資決策。

Based on past research findings, it is known that an overly confident CEO can bring some benefits to a company, but it also brings many negative impacts. For most investors, it is difficult to identify whether a company is led by an overly confident CEO because they lack internal company information and it's challenging to access the CEO. As a result, it is challenging to avoid the problems caused by an overly confident CEO. In order to assist external investors in determining whether a company is led by an overly confident CEO, this study conducts research on publicly traded companies in the United States using document analysis. It combines text mining techniques and a classification
model to establish a predictive classification model for identifying CEOs with excessive confidence, aiding investors in assessing risks and avoiding erroneous investment decisions.

目錄 i
圖目錄 iii
表目錄 iv
第一章、緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 研究架構 3
第二章、文獻探討 5
2.1 過度自信執行長相關文獻 5
2.2 過度自信衡量方法 8
第三章、研究方法 10
3.1 資料來源 12
3.2 資料前處理 15
3.3 文字探勘方法 15
3.3.1 情緒擷取分析 16
3.3.2 名詞短語 16
3.3.3 專有名詞 17
3.3.4 命名實體辨識 17
3.3.5 隱含狄利克雷分佈 18
3.3.6 詞頻-逆向文件頻率 19
3.4 機器學習演算法 20
3.4.1 隔離森林 23
3.4.2 支援向量機 24
3.4.3 支援向量回歸 25
3.4.4 一類別支援向量機 26
3.4.5 區域離群值因子 27
3.4.6 隨機森林 28
3.4.7 多層感知器 29
3.4.8 長短期記憶模型 31
3.5 模型衡量標準 34
第四章、研究結果與分析 34
4.1 一類別分類器與機器學習演算法之比較(過採樣前) 37
4.2 一類別分類器與機器學習演算法之比較(過採樣後) 90
4.3 情緒水準預測結果 157
第五章、結論與建議 160
5.1 研究結論 160
5.2 研究限制 162
5.3 建議 162
參考文獻 163


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