臺灣博碩士論文加值系統

　　Fama et al. (1969)、French and Roll (1986) 認為資訊在資本市場中扮演著非常重要的角色。為了降低資訊不對稱性的影響，理性的投資人往往會在進行投資交易之前，先用網路進行搜尋，以確保其作最適的投資決策。

　　本研究以Google趨勢（Google trends）之搜尋量指數（SVI）做為投資人資訊需求的代理變數，旨在探討散戶投資人之網路搜尋行為對其股票交易活動之影響。以台灣50中的成分股為研究對象，研究期間為2006年至2018年，參考Bijl et al. (2016)的做法，進行實證分析。

　　實證結果發現，搜尋量指數（SVI）與股票報酬間呈正向關係，t-2期的SVI意味著較高的搜尋量預示著較低的未來超額報酬。除了SVI變量的係數具有統計顯著性外，從迴歸結果中可發現SVI的預測能力在金融危機期間和在一般市場狀況下都是相似的。

　　本研究發現高Google搜尋量可以預測前一週的正報酬，隨後是負報酬，有價格逆轉之現象，故支持Google搜尋量可以預測股票報酬之結果，此與Da et al.（2011a）和Joseph et al.（2011）等學者之實證研究發現一致。

　　Fama et al. (1969), French and Roll (1986) argued that information plays a very important role in the capital market. In order to reduce the effects of information asym-metry, rational investors often search the Internet before investment deals, to ensure they make the most appropriate investment decisions.

　　Taking the search volume index (SVI) of Google trends as the proxy variable for investors’ information needs, this study aimed to explore the effects of Internet search of retail investors on their stock trading activities. With the study period from 2006 to 2018, the constituent stocks in FTSE TWSE Taiwan 50 Index were taken as the study objects for empirical analysis by reference with the practice of Bijl et al. (2016).

　　The empirical results show that, search volume index (SVI) is positively related to stock return, and SVI of t-2 period means that a higher search volume indicates a lower future excess return. In addition, that the coefficient of SVI variable is statistically sig-nificant, the regression results show that the predictive ability of SVI is similar during financial crisis and under normal market conditions.

　　It is found in this study that a high Google search volume can predict the positive return in the previous week, followed by the negative return, with the phenomenon of price reversals. Therefore, the result that Google search volume can predict the stock return is supported, which is consistent with the findings in the empirical study of the scholars such as Da et al. (2011a) and Joseph et al. (2011).

中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 v
圖目錄 vi
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 研究流程 4
第二章 文獻探討 5
第一節 Google搜尋量指數之相關應用 6
第二節 Google搜尋量指數與股票報酬 7
第三章 研究設計與方法 10
第一節 資料來源與樣本選取 10
第二節 樣本定義與衡量方法 11
第四章 實證結果與分析 18
第一節 敘述性統計 18
第二節 Panel Data 模型 22
第五章 結論 27
參考文獻 28

一、中文文獻
1.吳長閎，2017，股票波動性與網路搜索量之關係，逢甲大學財務金融學系，碩士論文。
2.洪文琳，2013，搜尋量指數與臺灣股票流動性與報酬率之研究，國立中山大學財務管理學系， 碩士論文。
3.陳怡靜，2014，谷歌搜尋引擎與臺灣股市交易活動之關聯性，國立高雄第一科技大學財務金融學系，碩士論文。

二、英文文獻
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14.Joseph, K., Wintoki, M.B.and Zhang, Z., (2011), “Forecasting abnormal stock re-turns and trading volume using investor sentiment: Evi¬dence from online search”, International Journal of Forecasting, 27, 1116–1127.
15.Kristoufek, L., (2015), “Power-law correlations in finance-related Google searches, and their cross-correlations with volatility and traded volume: Evidence from the Dow Jones Industrial components” , Physica A: Statistical Mechanics and its Ap-plications , 428, 194-205.
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19.Poon, S., H.andGranger, C., W., (2003), “Forecasting volatility in financial markets: A review”, Journal of Economic Literature, 41, 478-539.
20.Tantaopas, P., Padungsaksawasdi, C. and Treepongkaruna, S., (2016), “Attention effect via internet search intensity in Asia-Pacific stock markets”, Pacific-Basin Finance Journal, 38, 107-124.
21.Vlastakis, N., Markellos, R.N., (2012), “Information demand and stock market volatility” , Journal of Banking and Finance, 36, 1808-1821.
22.Venkataraman, M., Panchapagesan, V., Jalan, E., (2018), “Does internet search in-tensity predict house prices in emerging markets? a case of India”, Property Management , 36, 103-118.
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