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標題: | 精準醫療的新聞情緒指標之醫療產業投資組合績效 The performance of healthcare industry investment portfolios based on sentiment indicator in precision medicine news |
作者: | 游雅竹 Ya-Chu Yu |
指導教授: | 曹承礎 Seng-Cho Chou |
關鍵字: | 精準醫療,情緒分析,羅吉斯迴歸,股票選擇,醫療保健, Precision Medicine,Sentiment Analysis,Logistic Regression,Stock Selection,Healthcare, |
出版年 : | 2025 |
學位: | 碩士 |
摘要: | none Precision medicine and stock market volatility are interconnected, as advancements and the growth potential of precision medicine can influence the stock market performance of healthcare companies. The study is to examine movements in stock prices correlate with the investors'responses to precision medicine news through sentiment analysis to understand its impact on the financial market. On the other hand, if investors are not actively engaged with precision medicine news, the news may not entirely reflect their perspectives. The study uses Google Trends as a proxy for investor attention to establish a link between investors and precision medicine. The research utilizes simple regression analysis and threshold regression analysis to examine the impact of investor perceptions on the returns of U.S. healthcare stocks, connecting investor interest as criteria for stock selection. A logistic regression model is applied as a predictive framework, with technical indicators as common variables, to develop equal-weight daily trading strategies. Sentiment indicators, investor interest, and the volatility index (VIX) are included as additional variables for stock return prediction. The results show that excluding technical indicators nearly eliminates investment returns. This indicates that while sentiment indicators related to healthcare effectively capture trends in healthcare stocks, technical analysis is essential for leveraging significant uptrends and avoiding major downturns, ultimately driving profitability. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/97132 |
DOI: | 10.6342/NTU202500236 |
全文授權: | 未授權 |
電子全文公開日期: | N/A |
顯示於系所單位: | 管理學院企業管理專班(Global MBA) |
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