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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/110802


    Title: Google Trends關鍵字搜尋與台灣上市金控公司股價之探討
    A study on Google Trends keyword search and share price of financial holding companies in Taiwan
    Authors: 彭怡娟
    Peng, Yi Chuan
    Contributors: 陳威光
    李桐豪
    Chen, Wei Kuang
    Lee, Tung Hao
    彭怡娟
    Peng, Yi Chuan
    Keywords: Google Trends
    新聞
    網路關注
    台灣上市金控公司
    股價報酬
    Google Trends
    News
    Online attention
    Taiwanese listed financial holding companies
    Stock return
    Date: 2017
    Issue Date: 2017-07-11 11:30:47 (UTC+8)
    Abstract: 2015~2016年間台灣金融業發生許多重大新聞事件，隨著資訊科技普及，網路搜尋已成為大眾獲取資訊的重要管道。本文利用Google Trends關鍵字搜尋指數作為網路關注度的代理變數，進行與台灣上市金控公司股價報酬相關之研究。
    本文使用三種研究方法進行探討，首先利用圖表式比對法，初步觀察異常搜尋指數與異常報酬出現時間之關聯性，結果並未發現搜尋指數與台灣上市金控股價報酬間有明顯且一致的關係；接著套用向量自我迴歸模型進行分析，然而14家台灣上市金控公司中，僅從兆豐金數據可發現前一期搜尋指數的異常變動量增加1%將使下一期異常報酬率下降約2.67%；最後參考相關文獻使用Fama Macbeth兩階段迴歸模型，結果發現平均而言搜尋指數的異常變動量每上升一個標準差會顯著影響兩週後股價的異常報酬率下降約0.17%，SVI對於股價報酬影響為負向符合本文研究動機與背景，且有相關文獻指出投資人對於壞消息的反應較慢，因此使股價報酬有延後反應的現象，但無法解釋兩週的反應時間，因此對於這樣的研究結果持保留的態度。
    總結三種研究方法所得結果，本文認為網路關注度對於目前台灣上市金控公司股價的影響仍然有限。
    It’s unquiet for Taiwanese Financial industry between 2015 and 2016. There has been a lot of major news. With the popularity of information technology, Internet search has become an important channel for public access to information. Therefore, we use Search Volume Index (SVI) as a proxy for public online attention and conducts research related to the stock returns of listed financial holding companies in Taiwan.
    In this paper, three kinds of research methods are used. The first way is chart comparison method for preliminary data analysis. The results couldn’t show a clear and consistent relationship between SVI and stock returns. The second method is vector self-regression model. However, only Mega financial holding company’s result indicates abnormal search volume index(ASVI) increase 1% will decrease next week abnormal return by 2.67%. At last, we use Fama Macbeth two-stage regression model and find that on average 1 standard deviation increased in ASVI will decrease abnormal return by 0.17% after two weeks. The negative impact of SVI on the stock returns of financial holding companies is in line with the research motivation and background, and some relevant literatures prove that investors’ response to the bad news is slow, which leads to the delayed response of stock returns. However, the two weeks of reaction time for stock returns is unknown.
    In conclusion, this paper finds out that the impact of public online attention on share price of listed financial holding companies in Taiwan is still limited currently.
    Reference: 一、中文文獻
    1. 王雪伶(2014)。Google trends 與電影票房關係之研究。未出版之(博)碩士論文，銘傳大學，傳播管理研究所，臺北市。
    2. 呂家萱(2014)。新聞頻率、散戶投資人情緒與股價共動性。未出版之(博)碩士論文，國立臺灣大學，財務金融學研究所，台北市。
    3. 李冠毅(2011)。利用 Google Trends 分析關鍵字廣告對於產業績效相關性研究。未出版之(博)碩士論文，樹德科技大學，資訊管理研究所，高雄市。
    4. 李駿廷(2015)。從 Google Trends Interest 看投資者關注與市場效率之關聯: 以美國存託憑證為例。未出版之(博)碩士論文，國立中正大學，財務金融研究所，嘉義縣。
    5. 林建良(2011)。新聞媒體的資訊內涵與橫斷面股價報酬。未出版之(博)碩士論文，國立高雄大學，金融管理研究所，高雄市。
    6. 林鼎壹(2014)。用谷歌搜尋趨勢預測公司營收：台灣的實證研究。未出版之(博)碩士論文，國立東華大學，財務金融研究所，花蓮縣。
    7. 張琬喻、楊弘章、陳佳吟(2014)。新聞媒體報導對公司財務績效與股價之影響。證券市場發展季刊, 26(1), 113-146.
    8. 陳有佾(2010)。有限注意與新聞資訊對基金投資人交易決策之影響。未出版之(博)碩士論文，國立聯合大學，管理學研究所，苗栗縣。
    9. 陳怡靜(2014)。谷歌搜尋引擎與台灣股市交易活動之關聯性。未出版之(博)碩士論文，國立高雄第一科技大學，金融學研究所，高雄市。
    10. 陳蕙妤(2012)。網路搜尋量是否可以增進股票市場波動率的預測? 國際實證。未出版之(博)碩士論文，國立臺灣大學，財務金融研究所，台北市。
    11. 黃浚紘(2013)。Google 是否能預測台灣股票報酬率?。未出版之(博)碩士論文，國立臺灣大學，財務金融研究所，台北市。
    12. 謝宗憲(2014)。亞馬遜公司股價與Google Trends 關鍵字搜尋之探討。未出版之(博)碩士論文，世新大學，財務金融研究所，台北市。
    二、英文文獻
    1. Askitas, N., & Zimmermann, K. F. (2009). Google econometrics and unemployment forecasting. Applied Economics Quarterly, 55(2), 107-120.
    2. Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. Review of Financial Studies, 21(2), 785-818.
    3. Chan, W. S. (2003). Stock price reaction to news and no-news: drift and reversal after headlines. Journal of Financial Economics, 70(2), 223-260.
    4. Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88(s1), 2-9.
    5. Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461-1499.
    6. Fama, E. F., & MacBeth, J. D. (1973). Risk, return, and equilibrium: Empirical tests. Journal of political economy, 81(3), 607-636.
    7. Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012-1014.
    8. Granger, C. W., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of econometrics, 2(2), 111-120.
    9. Hand, C., & Judge, G. (2012). Searching for the picture: forecasting UK cinema admissions using Google Trends data. Applied Economics Letters, 19(11), 1051-1055.
    10. Klibanoff, P., Lamont, O., & Wizman, T. A. (1998). Investor reaction to salient news in closed‐end country funds. The Journal of Finance, 53(2), 673-699.
    11. Kristoufek, L. (2013). Can Google Trends search queries contribute to risk diversification? arXiv preprint arXiv:1310.1444.
    12. Nofsinger, J. R. (2001). The impact of public information on investors. Journal of Banking & Finance, 25(7), 1339-1366.
    13. Ripberger, J. T. (2011). Capturing curiosity: Using Internet search trends to measure public attentiveness. Policy Studies Journal, 39(2), 239-259.
    14. Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607.
    15. Sims, C. A. (1980). Macroeconomics and reality. Econometrica: Journal of the Econometric Society, 1-48.
    16. Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62(3), 1139-1168.
    17. Tetlock, P. C., Saar‐Tsechansky, M., & Macskassy, S. (2008). More than words: Quantifying language to measure firms` fundamentals. The Journal of Finance, 63(3), 1437-1467.
    18. Vaughan, L., & Romero‐Frías, E. (2014). Web search volume as a predictor of academic fame: an exploration of Google Trends. Journal of the Association for Information Science and Technology, 65(4), 707-720.
    Description: 碩士
    國立政治大學
    金融學系
    104352022
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0104352022
    Data Type: thesis
    Appears in Collections:[Department of Money and Banking] Theses

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