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系統識別號 U0026-2206202021180800
論文名稱(中文) IPO新聞情緒與IPO超額認購的關係
論文名稱(英文) On the Relationship between IPO News Sentiment and IPO Oversubscription
校院名稱 成功大學
系所名稱(中) 會計學系
系所名稱(英) Department of Accountancy
學年度 108
學期 2
出版年 109
研究生(中文) 黃昱翔
研究生(英文) Yu-Hsiang Huang
電子信箱 hyxjerry1128@gmail.com
學號 R16071179
學位類別 碩士
語文別 中文
論文頁數 44頁
口試委員 指導教授-顏盟峯
口試委員-林軒竹
口試委員-黃炳勳
中文關鍵字 情緒分析  超額認購  深度學習  IPO 
英文關鍵字 Sentiment Analysis  Oversubscription  Deep Learning  IPO 
學科別分類
中文摘要 隨著網路不斷地發展與進步,各種程式模型、演算法也迅速產生,近年來不管是大數據、人工智慧、機器學習、深度學習等都是相當熱門的議題。利用深度學習我們可以萃取我們所需的資料,並將這些文本資料變成分數或數據來進行研究探討。
本研究利用BERT深度學習模型將每一篇媒體報導的IPO新聞轉換成每一篇的IPO新聞情緒分數,並探討IPO新聞情緒與IPO超額認購的關係。研究結果發現當IPO新聞情緒越正面時,越有機會造成IPO超額認購,之後我們又將情緒向性結合語氣強弱度,研究結果發現兩者依舊產生正向顯著關係,最後我們在控制IPO新聞總情緒後,進一步探討正情緒及正情緒結合強弱語氣度對於IPO超額認購的關係,研究結果發現與IPO超額認購呈顯著正相關。
英文摘要 SUMMARY
With the continuous development and progress of the Internet, various programming models and algorithms have also rapidly emerged. In recent years, big data, artificial intelligence, machine learning, deep learning, etc. are quite popular topics. Using deep learning, we can extract the data we need and turn these text data into quantified scores or data for research.The research is to use a deep learning model to convert every piece of IPO news reported by the media into an IPO news sentiment polarity score (i.e. positive or negative sentiment), and to explore the relationship between IPO news sentiment and IPO oversubscription. The study found that the more positive the IPO news sentiment, the greater the chance of causing IPO oversubscription; the more negative the IPO news sentiment, the lower the chance of causing IPO oversubscription. Afterwards, we multiplied by the score of each sentence with the modal (the excitation level) of each sentence. Based on results, we found that the positive association between IPO oversubscription and sentiments extracted from relevant news remains statistically positive.

論文目次 第一章、緒論 1

第二章、文獻回顧與研究假說 3
第一節 IPO超額認購與IPO新聞情緒之情緒向性及結合語氣強弱度的關係 3
第二節 IPO超額認購分別與正負面新聞及結合語氣強弱度的關係 4
第三節 情緒分析 5
第四節 BERT模型演算法 7

第三章、樣本及研究方法 9
第一節 樣本與資料來源 9
第二節 新聞處理過程 10
第三節 變數衡量 11
第四節 研究模型設計 14

第四章、研究結果 20
第一節 敘述性統計 20
第二節 實證結果 28
第三節 額外測試 32

第五章、結論與建議 38
第一節 結論 38
第二節 貢獻 39
第三節 研究限制 39
第四節 研究建議 39

第六章、參考文獻 40
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