本研究嘗試利用三段式門檻迴歸模型將由Google搜尋量代理的投資人注意力區分為資訊驅動的搜尋量與雜訊驅動的搜尋量。以2009年1月到2014年12月台灣上市公司為例，本研究發現 (1) Google搜尋量和交易量顯著正相關。我們認為搜尋本身並不會影響報酬，唯有透過交易量才可能對報酬造成影響。(2)不同型態的搜尋量會對市場品質造成不同的影響。實證發現雜訊驅動的搜尋量與流動性和波動性皆正相關，而資訊驅動的搜尋量與流動性呈負相關、與波動性則呈現正相關。(3)上市較久、獲利能力較低與有高股價淨值比的公司有較高的資訊驅動搜尋比例，這代表上市較久、獲利能力較低與有高股價淨值比的公司較不是因為投資人從眾的關係而被搜尋。

We propose a new way to distinguish investor attention as measured by Google search volume index (SVI) from information-initiated searches and noise-initiated searches using a three-regime threshold model. In a sample of Taiwan Stock Exchange stocks from January 2009 to December 2014, we find that (1) SVI is significantly and positively correlated with trading volume. We consider that only a stock whose volume can be affected by search queries could see its return affected; (2) different types of SVI have different impacts on investor attention to market quality. Noise-initiated search is positively related to liquidity and positively related to volatility, while information-initiated search is negatively related to market liquidity and positively related to volatility; (3) firms which are older, are less profitable, and have a higher PB ratio will have a higher information search ratio which represents that these companies are less likely to cause herding for search information.

論文審定書 i
摘要 ii
Abstract iii
1. Introduction 1
2. Literature Review 3
2.1 Behavioral Finance and Investor Attention 3
2.2 Investor Attention and Return 6
2.3 Investor Attention and Market Quality 8
3. Overview 11
4. Data and Preliminary Studies 12
4.1 Google Search Volume Index (SVI) 13
4.2 Proxy of Market Quality 15
4.3 Threshold Value and Firm Characteristic Variables 17
5. Methodology 20
5.1 Google Search Volume and Trading Volume 20
5.2 Three-Regime Threshold Model 22
5.3 Dynamic Relationship Between Return and Volume of Individual Stocks 27
5.4 Different Types of Investor Attention and Market Quality 28
5.5 Threshold Value and Firm Characteristic Variables 31
6. Empirical Results 33
6.1 Unit Root Test 33
6.2 Google Search Volume and Trading Volume 33
6.3 Dynamic Relationship Between Return and Volume of Individual Stocks of the Three-Regime Threshold Model 37
6.4 Different Type of Investor Attention and Market Quality 38
6.5 Threshold Value and Firm Characteristic Variables 39
7. Conclusion 44
References vi
Appendix x

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