System No. | U0002-1612202218355100 |
---|---|
DOI | 10.6846/TKU.2023.00085 |
Title (in Chinese) | 散戶關注度對臺灣加權股價指數報酬與波動度之影響—以Google搜尋量建構臺灣恐慌指數 |
Title (in English) | The Effect of Retail Investors' Attention on Taiwan Market Index Return and Volatility: Constructing Taiwan Fear Index by Google Search Volume |
Other Title | |
Institution | 淡江大學 |
Department (in Chinese) | 財務金融學系碩士班 |
Department (in English) | Department of Banking and Finance |
Other Division | |
Other Division Name | |
Other Department/Institution | |
Academic Year | 111 |
Semester | 1 |
PublicationYear | 112 |
Author's name (in Chinese) | 王麒程 |
Author's name(in English) | Chi-Cheng Wang |
Student ID | 610530197 |
Degree | 碩士 |
Language | Traditional Chinese |
Other Language | |
Date of Oral Defense | 2022-12-11 |
Pagination | 53page |
Committee Member |
advisor
-
HSUAN-LING CHANG(157500@mail.tku.edu.tw)
co-chair - 鄭宏文 co-chair - 趙慶祥 co-chair - 張瑄凌 |
Keyword (inChinese) |
Google Trends 臺指VIX 投資人關注度 財務行為 |
Keyword (in English) |
Google Trends VIX Investor Attention Behavioral Finance |
Other Keywords | |
Subject | |
Abstract (in Chinese) |
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。 |
Abstract (in English) |
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. |
Other Abstract | |
Table of Content (with Page Number) |
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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