行為財務學 (Behavioral Finance) 近年來在財務學領域愈來愈受到重視，心理學領域普遍認為情緒對人類的決策判斷具有重要的影響，臺灣金融市場的投資人結構由於以散戶為主，因此散戶的情緒對金融市場及其投資行為各方面的影響又更顯重要。
    根據市場研究機構 “NetMarketShare” 所公布的資料顯示，自2014年以來Google Chrome成為臺灣用戶市占率最高的瀏覽器，統計至2021年為止市占率已超過60%，故本研究使用Google Trends提供的搜尋量指數 (SVI) 作為散戶關注度的代理變數。
    本研究遵循Kostopoulos, Meyer and Uhr (2020) 所使用之方法，選取臺灣不確定經濟指標 (EPU) 關鍵字的週SVI數據，範圍設定為2007年1月1日至2021年12月31日，此期間涵蓋2008年金融海嘯、2018~2020年新冠疫情等臺股重大事件，將其以均等權重建構成臺灣恐慌指數 (TFEARS)，用於檢測散戶的關注度對於臺指波動率指數及加權股價指數的影響。
    實證結果表明TFEARS與加權股價指數之對數報酬率呈現負向相關、與臺指波動率指數之變動量無顯著關係，因關鍵字「動盪」、「金融危機」會增加波動率，「景氣循環」則使得波動率下降，其表現與投資人情緒理論一致。

Behavioral finance has received more and more attention in the field of finance in recent years. It is generally believed in the field of psychology that emotions have an important impact when human making decision and judgment. Because the investor structure of Taiwan's financial market is dominated by retail investors, the emotions of retail investors have a significant impact on financial markets. The influence of various aspects of the market and its investment behavior is even more important.
According to the data released by the market research organization "NetMarket-Share", Google Chrome has become the browser with the highest market share in Taiwan since 2014, and the market share has exceeded 60% by 2021. Therefore, this research use the search volume index provide by Google Trends and used it as a proxy variable for retail investors' attention.
This study follows the method used by Kostopoulos, Meyer and Uhr (2020), selecting weekly SVI data of Taiwan’s uncertain economic indicators keywords, and the date is set from January 1, 2007 to December 31, 2021, which cover financial crisis in 2008.The significant events in Taiwan stock market such as the financial crisis in 2008 and COVID-19 in 2018 to 2020, were reconstructed with equal weights to form the Taiwan Fears Index (TFEARS), which was used to detect the impact of retail investors' attention on the Taiwan Options Volatility Index (VIXTWN) and the TWSE Capitalization Weighted Stock Index (TAIEX).
The empirical results show that TFEARS has a negative correlation with the logarithmic return of the TAIEX, and has no significant relationship with delta of the VIXTWN, because the keywords "turbulence" and "financial crisis" will increase the volatility, and "business cycle" will reduce the volatility. Its performance is consistent with investor sentiment theory.

第一章	緒論                	         1
　第一節  研究背景與動機	                 1
　第二節  研究目的	                 5
　第三節  研究架構與流程	                 7
第二章	文獻探討 	                 8
　第一節  波動率指數	                 8
　第二節  散戶關注度	                10
　第三節  Google搜尋量指數	        16
　第四節  FEARS指數建構	                19
第三章	研究方法	                        22
　第一節  研究樣本及資料來源	        22
　第二節  研究方法	                23
　第三節  模型建立        	        25
第四章  實證結果與分析	                27
  第一節  敘述統計量分析	                27
  第二節  相關係數、共線性分析	        30
  第三節  TFEARS對TAIEX之實證結果	        31
  第四節  TFEARS對VIXTWN之實證結果	35
第五章  結論與建議	                44
　第一節  結論	                        44
　第二節  建議	                        45
參考文獻	                                46
附錄                         	        51

表目錄
表3.3.1   假說檢定 (Hypothesis Testing)	                        26
表4.1.1   SVI、TAIEX、VIXTWN 敘述統計量	                        27
表4.1.2   δSVI、TFEARS敘述統計量	                                29
表4.2.1   TAIEX、VIXTWN、SVI 之相關係數及共線性分析	        30
表4.3.1   SVI對加權股價指數 (TAIEX) 之變異數分析	                31
表4.3.2   SVI對加權股價指數 (TAIEX) 之回歸分析	                32
表4.3.3   δSVI對加權股價指數報酬率 ln(TAIEX) 之變異數分析	        33
表4.3.4   δSVI對加權股價指數報酬率 ln(TAIEX) 之回歸分析	        33
表4.3.5   TFEARS對加權股價指數報酬率 ln(TAIEX) 之變異數分析	34
表4.3.6   TFEARS對加權股價指數報酬率 ln(TAIEX) 之回歸分析	        34
表4.4.1   SVI對臺指波動率指數 (VIXTWN) 之變異數分析	        35
表4.4.2   SVI對臺指波動率指數 (VIXTWN) 之回歸分析	                36
表4.4.3   δSVI對臺指波動率指數報酬率 ln(VIXTWN) 之變異數分析	37
表4.4.4   δSVI對臺指波動率指數報酬率 ln(VIXTWN) 之回歸分析	        37
表4.4.5   TFEARS對臺指波動率指數報酬率 ln(VIXTWN) 之變異數分析	38
表4.4.6   TFEARS對臺指波動率指數報酬率 ln(VIXTWN) 之回歸分析	38
表4.4.7   δSVI對臺指波動率指數變動量 δVIXTWN 之變異數分析	        39
表4.4.8   δSVI對臺指波動率指數變動量 δVIXTWN 之回歸分析	        39
表4.4.9   TFEARS對臺指波動率指數變動量 δVIXTWN 之變異數分析	40
表4.4.10  TFEARS對臺指波動率指數變動量 δVIXTWN 之回歸分析	        40
表4.4.11  TFEARS+ 對臺指波動率指數變動量 δVIXTWN 之變異數分析	41
表4.4.12  TFEARS+ 對臺指波動率指數變動量 δVIXTWN 之回歸分析	41
表4.4.13  TFEARS- 對臺指波動率指數變動量 δVIXTWN 之變異數分析	42
表4.4.14  TFEARS- 對臺指波動率指數變動量 δVIXTWN 之回歸分析	42

圖目錄
圖1.3.1   研究架構流程圖                              	         7
圖2.3.1   “NetMarketShare” 全球桌上型電腦及筆記型電腦瀏覽器排名	16
圖3.1.1   Google Trends SVI 數據生成示意圖	                23
圖4.1.1   SVI不安	                                        28
圖4.1.2   SVI金融危機	                                        28
圖4.3.1   TFEARS 對 ln(TAIEX) 之回歸模型	                        34
圖4.4.1   TFEARS 對 ln(VIXTWN) 之回歸模型	                38
圖4.4.2   TFEARS+ 對 δVIXTWN 之回歸模型	                        41
圖4.4.3   TFEARS- 對 δVIXTWN 之回歸模型	                        42
圖4.4.4   TFEARS	                                        43
圖4.4.5   TFEARS+	                                        43
圖4.4.6   TFEARS-	                                        43
附錄1     SVI不穩定	                                        51
附錄2     SVI動盪	                                        51
附錄3     SVI未明	                                        51
附錄4     SVI不安	                                        52
附錄5     SVI金融危機	                                        52
附錄6     SVI景氣循環	                                        52
附錄7     TFEARS	                                        53
附錄8     TFEARS+	                                        53
附錄9     TFEARS-	                                        53

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