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題名：網路關鍵字搜尋行為反映投資人情緒之研究
作者：許信輝
作者(外文)：Hsin-Hui Hsu
校院名稱：淡江大學
系所名稱：財務金融學系博士班
指導教授：邱建良
張鼎煥
學位類別：博士
出版日期：2023
主題關鍵詞：Google搜尋趨勢指數；股票報酬；投資人情緒；波動；縱橫門檻迴歸模型；Google Search Volume Index；Stock Return；Investor Sentiment；Volatility；Panel Threshold Regression
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本論文由三篇研究構成，旨在探討Google搜尋趨勢指數對股票報酬及市場波動的影響，並評估其作為投資人情緒代理變數的效果。研究樣本包括臺灣資本額百億以上上市公司，涵蓋2012年7月至2022年6月共10年期間資料。並分析Google搜尋趨勢指數與成交量、股價淨值比、股價盈餘比交互作用對股票報酬影響，以深入瞭解其相關性。
研究發現，前5年樣本中股票報酬風險相對較低且受系統性風險影響較小。後5年之樣本較前5年之樣本股市成交量放大且Google搜尋趨勢指數上升。Google搜尋趨勢指數作為投資人情緒的代理變數正向顯著影響股票報酬，並推升成交量和股價。Google搜尋趨勢指數與股價盈餘比交乘項在後5年出現正向顯著之反轉現象。
研究進一步發現，在縱橫資料門檻迴歸模型下，股票報酬做為門檻變數得以區隔Google搜尋趨勢指數對股票報酬不同程度影響之門檻效果，於門檻值-4.8086%上、下兩側形成區間分界。投資人易因搜尋資訊產生過度樂觀與過度悲觀反應，導致股票報酬持續上漲與下跌，因此Google搜尋趨勢指數做為投資人情緒代理變數，得以解釋股票報酬，促進價格發現與市場效率，成為投資決策重要參考指標。
最後探討Google搜尋趨勢指數及其分別與股票報酬與臺灣加權指數報酬交互作用對市場波動之影響。研究結果顯示，Google搜尋趨勢指數做為投資人情緒代理變數與正向顯著影響股價波動風險解釋因子；搜尋頻率強度，反映投資人對股票關注程度與情緒反應；相較單一參數股票報酬情形下，Google搜尋趨勢指數與股票報酬交乘項對股價波動風險有收斂效果及負向顯著影響；Google搜尋趨勢指數與臺灣加權指數報酬交乘項，對臺灣加權指數報酬對股價波動風險有收斂效果及正向顯著影響。
研究顯示Google搜尋趨勢指數做為投資人情緒代理變數，得以解釋股票報酬，促進價格發現與市場效率，有助投資人更準確地預測股價波動風險趨勢，成為投資決策重要參考指標，亦隱含投資人易受情緒影響。
This thesis consists of three studies aimed at exploring the impact of the Google Search Volume Index on stock returns and market volatility, as well as evaluating its effectiveness as a proxy variable for investor sentiment using the Panel Threshold Regression model. The research sample includes listed companies in Taiwan with a capitalization of over one billion NT dollars, covering data from July 2012 to June 2022, a total of ten years. Additionally, the study analyzes the interactive effects of the Google Search Volume Index with trading volume, price-to-book ratio, and price-earnings ratio on stock returns to gain deeper insights into their correlations.
The findings reveal that in the first five years of the sample, stock returns exhibited relatively lower risk and were less influenced by systematic risk. In the latter five years, the stock market's trading volume expanded, and the Google Search Volume Index increased. The Google Search Volume Index, as a proxy variable for investor sentiment, significantly and positively influenced stock returns, driving trading volume and stock prices higher. During the latter five years, there was a reversal phenomenon with a significant positive interaction effect between the Google Search Volume Index and the price-earnings ratio.
Furthermore, this research discovered that, in the Panel Threshold Regression model, stock returns serve as a threshold variable to distinguish the varying impacts of the Google Search Volume Index on stock returns, forming an interval boundary on both sides of the threshold value of -4.8086%. Investors are prone to exhibit excessively optimistic or pessimistic reactions due to search information, leading to sustained increases or decreases in stock returns. Therefore, the Google Search Volume Index, as a proxy variable for investor sentiment, can explain stock returns, promote price discovery, and enhance market efficiency, becoming an essential reference indicator for investment decisions.
Finally, the study explores the interactive effects of the Google Search Volume Index, stock returns, and the Taiwan Weighted Index returns on market volatility using the Panel Threshold Regression model. The results indicate that the Google Search Volume Index, as a proxy variable for investor sentiment, positively and significantly affects the explanatory factors of stock price volatility. The intensity of search frequency reflects investors' attention and emotional responses to stocks. Compared to the scenario with a single parameter of stock returns, the interaction effect between the Google Search Volume Index and stock returns has a converging and negative significant impact on stock price volatility. The interaction effect between the Google Search Volume Index and Taiwan Weighted Index returns has a converging and positively significant impact on the stock price volatility of the Taiwan Weighted Index returns.
The research demonstrates that the Google Search Volume Index, as a proxy variable for investor sentiment, can explain stock returns, promote price discovery, and enhance market efficiency, helping investors predict stock price volatility trends more accurately. It becomes a crucial reference indicator for investment decisions, also implying that investors are susceptible to emotional influences.
第一篇
王明昌、許婉琪、李飛涵與柯建全(2022)，市場恐慌情緒對台股新聞事件之股價反應的影響，管理與系統，29(2)：147-186。
何怡滿與陳雯琪(2019)，投資人關注度對台灣50指數成分股之股票報酬與公司績效的影響，屏東大學學報-管理類，(2)：73-103。
李永隆、杜玉振與王瑋瑄(2017)，Google搜尋量指數對臺灣股票報酬與成交量之影響，管理與系統，24(4)：565-590。
劉清標、林筱鳳與陳宏榮(2017)，股價報酬與投資人情緒之預測，財金論文叢刊，(26)：1-18。
趙慶祥、葉錦徽與梁景婷(2022)，情緒與總經宣告對風險與期望報酬抵換關係之影響，證券市場發展季刊，34(1)：135-176。
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Chai, D., Dai, M., Gharghori, P., & Hong, B. (2021). Internet search intensity and its relation with trading activity and stock returns. International Review of Finance, 21(1), 282-311.
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Fama, E. F., & French, K. R. (1992). The cross‐section of expected stock returns. Journal of Finance, 47(2), 427-465.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Fama, E. F., & French, K. R. (1995). Size and book‐to‐market factors in earnings and returns. Journal of Finance, 50(1), 131-155.
Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. Journal of Finance, 51(1), 55-84.
Fama, E. F., & French, K. R. (1998). Value versus growth: The international evidence. Journal of Finance, 53(6), 1975-1999.
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Hausman, J. A. (1978). Specification tests in econometrics. Econometrica: Journal of the Econometric Society, 1251-1271.
Hu, H., Tang, L., Zhang, S., & Wang, H. (2018). Predicting the direction of stock markets using optimized neural networks with Google Trends. Neurocomputing, 285, 188-195.
Joseph, K., Wintoki, M. B., & Zhang, Z. (2011). Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search. International Journal of Forecasting, 27(4), 1116-1127.
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Takeda, F., & Wakao, T. (2014). Google search intensity and its relationship with returns and trading volume of Japanese stocks. Pacific-Basin Finance Journal, 27, 1-18.
Wanidwaranan, P., & Padungsaksawasdi, C. (2022). Unintentional herd behavior via the Google search volume index in international equity markets. Journal of International Financial Markets, Institutions and Money, 77, 101503.

第二篇
王明昌、許婉琪、李飛涵與柯建全(2022)，市場恐慌情緒對台股新聞事件之股價反應的影響，管理與系統，29(2)，147-186。
何怡滿與陳雯琪(2019)，投資人關注度對臺灣50指數成分股之股票報酬與公司績效的影響，屏東大學學報—管理類，2，73-103。
李永隆、杜玉振與王瑋瑄(2017)，Google搜尋量指數對臺灣股票報酬與成交量之影響，管理與系統，24(4)，565-590。
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劉玉珍、周行一與潘璟靜(1996)，臺灣股市價格限制與交易行為，中國財務學刊，4(2)，41-60。
趙慶祥、葉錦徽與梁景婷(2022)，情緒與總經宣告對風險與期望報酬抵換關係之影響，證券市場發展季刊，34(1)，135-176。
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Chan, K. S. (1993). Consistency and Limiting Distribution of the Least Squares Estimator of a Threshold Autoregressive Model. The Annals of Statistics ,21 (1): 520-533.
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Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. Journal of Finance, 66(5), 1461-1499.
Fama, E. F., & French, K. R. (1992). The cross‐section of expected stock returns. Journal of Finance, 47(2), 427-465.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Fama, E. F., & French, K. R. (1995). Size and book‐to‐market factors in earnings and returns. Journal of Finance, 50(1), 131-155.
Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. Journal of Finance, 51(1), 55-84.
Fama, E. F., & French, K. R. (1998). Value versus growth: The international evidence. Journal of Finance, 53(6), 1975-1999.
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Hansen, B. E. (1999). Threshold effects in non-dynamic panels: Estimation, testing, and inference. Journal of Econometrics, 93(2), 345-368.
Hausman, J. A. (1978). Specification tests in econometrics. Econometrica: Journal of the Econometric Society, 1251-1271.
Hu, H., Tang, L., Zhang, S., & Wang, H. (2018). Predicting the direction of stock markets using optimized neural networks with Google Trends. Neurocomputing, 285, 188-195.
Joseph, K., Wintoki, M. B., & Zhang, Z. (2011). Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search. International Journal of Forecasting, 27(4), 1116-1127.
Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541-1578.
Lee, C. M., & Swaminathan, B. (2000). Price Momentum and Trading Volume. Journal of Finance, 55(5), 2017-2069.
Lo, A. W., & MacKinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. Review of Financial Studies, 1(1), 41-66.
Takeda, F., & Wakao, T. (2014). Google search intensity and its relationship with returns and trading volume of Japanese stocks. Pacific-Basin Finance Journal, 27, 1-18.
Vlastakis, N., & Markellos, R. N. (2012). Information demand and stock market volatility. Journal of Banking & Finance, 36(6), 1808-1821.
Wanidwaranan, P., & Padungsaksawasdi, C. (2022). Unintentional herd behavior via the Google search volume index in international equity markets. Journal of International Financial Markets, Institutions and Money, 77, 101503.

第三篇
李永隆、杜玉振與王瑋瑄(2017)，Google搜尋量指數對臺灣股票報酬與成交量之影響，管理與系統，24(4)：565-590。
何怡滿與陳雯琪(2019)，投資人關注度對臺灣50指數成分股之股票報酬與公司績效的影響，屏東大學學報—管理類，2，73-103。
周賓凰、張宇志與林美珍(2019)，投資人情緒與股票報酬互動關係，證券市場發展季刊：行為財務學特別專刊，153。
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