On the Relationship between IPO News Sentiment and IPO Oversubscription
Yu-Hsiang Huang
Meng-Feng Yen
Hsuan-Chu Lin
Ping-Hsun Huang
College of Management
Department of Accountancy
英文關鍵詞:Sentiment Analysis、Oversubscription、Deep Learning、IPO

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.

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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