本研究以資本額逾新臺幣壹百億元之臺灣上市、上櫃公司為樣本，運用縱橫資料與門檻迴歸模型（PTR），探討Google搜尋趨勢指數作為投資人關注程度代理變數，對股票報酬與風險之非線性影響。實證結果顯示，在控制市場與基本面變數下，Google搜尋關注程度對股票報酬與股價波動風險皆具1%顯著水準，驗證其可同時預測市場報酬與反映風險感知，具高度解釋力。在波動風險門檻模型中，發現兩個顯著轉折點（3.2353%、7.7847%）。當風險處於中段（3.2353%～7.7847%）時，搜尋關注與報酬呈正向顯著，反映樂觀情緒；惟當風險超過7.7847%時，轉為顯著負向顯著，顯示在高風險下資訊過載與避險情緒抑制報酬。在股票報酬門檻模型中，揭示三個顯著轉折點（-2.5872%、3.0233%、12.8316%）。報酬低於-2.5872%時，關注程度與報酬波動呈正向顯著，反映市場恐慌；第二區間（-2.5872%～3.0233%）不具顯著性，顯示理性預期主導；第三區間（3.0233%～12.8316%）則顯著放大報酬反應，惟超過12.8316%後轉為負向顯著，顯示投資人傾向停利與觀望。研究發現投資人關注程度對報酬與風險具「放大—中性—壓制」之非線性行為，驗證「投資人關注—風險感知架構」在臺股市場之適用性，亦補足傳統財務模型對行為關注因素之不足。

This study uses a sample of publicly listed companies in Taiwan with paid-in capital exceeding NT\$10 billion, employing panel data and a Panel Threshold Regression (PTR) model to investigate the nonlinear impact of investor attention—proxied by Google Search Trends—on stock returns and risk. The empirical results show that, after controlling for market indicators and fundamental variables, the level of investor attention significantly explains both stock returns and price volatility at the 1% level, confirming its strong predictive power for market performance and its reflection of risk perception.
In the volatility threshold model, two significant breakpoints are identified (3.2353% and 7.7847%). When volatility is within the intermediate range (3.2353%–7.7847%), investor attention is positively and significantly associated with stock returns, reflecting optimistic market sentiment. However, when volatility exceeds 7.7847%, the relationship turns significantly negative, suggesting that information overload and risk-averse behavior in high-volatility environments suppress returns.
In the return threshold model, three significant breakpoints are revealed (-2.5872%, 3.0233%, and 12.8316%). When returns fall below -2.5872%, investor attention is positively and significantly related to return volatility, indicating market panic. In the second interval (-2.5872% to 3.0233%), the relationship is insignificant, implying rational expectations dominate. In the third interval (3.0233% to 12.8316%), attention significantly amplifies return reactions; however, beyond 12.8316%, the relationship becomes significantly negative, suggesting that investors tend to lock in profits or adopt a wait-and-see attitude.
Overall, the study finds a nonlinear "amplification–neutral–suppression" behavioral pattern in how investor attention affects returns and risk. The findings validate the applicability of the "Investor Attention–Risk Perception Framework" in Taiwan's stock market and complement traditional financial models by incorporating behavioral factors.

目  次
目  次……………………………………………………………………………………  I
表  次…………………………………………………………………………………… II
圖  次……………………………………………………………………………………III
第一章	緒論	        1
第一節	研究背景與動機	1
第二節	研究目的	        8
第三節  研究架構  	9
第二章	文獻回顧	                                10
第一節	投資人情緒與股價波動、股票報酬相關研究	10
第二節	Google搜尋趨勢指數與股票報酬相關研究	13
第三節	股價淨值比、週轉率與股票報酬相關研究	14
第四節	股價波動對股票報酬相關研究	        19
第五節	前景理論與風險偏好等投資行為金融觀點	22
第三章	研究方法	        25
第一節	變數定義	        25
第二節	縱橫資料迴歸模型	28
第三節	縱橫門檻迴歸模型	32
第四章	實證結果分析	                39
第一節	資料來源 	                39
第二節	基本敘述統計	                40
第三節 股票報酬之縱橫資料迴歸模型分析 	42
第四節 波動風險之縱橫資料模型分析	        47
第五節 股票報酬之雙門檻縱橫門檻模型分析	52
第六節 波動風險之三門檻縱橫門檻迴歸模型分析	58
第五章	結論	                        67
參考文獻 	                        70

表  次
表1 樣本之基本敘述統計分析	        41
表2 股票報酬之縱模資料模型實證結果	        46
表3 波動風險之縱模資料模型實證結果	        51
表4 股票報酬雙門檻之門檻值模型檢定	        55
表5 股票報酬雙門檻之縱橫迴歸模型估計係數	55
表6 波動風險之三門檻值模型檢定	        64
表7 波動風險之三門檻縱橫迴歸模型估計係數	64

圖  次
圖 1 2013年至2022年週平均之Google搜尋趨勢指數	7
圖 2 2025年2月臺灣搜尋引擎市佔率	         7
圖 3 股票報酬第一門檻參數估計值	        56
圖 4 股票報酬第二門檻參數估計值	        56
圖 5 股票報酬第一門檻回測參數估計值	57
圖 6 股票報酬第三門檻參數估計值	        57
圖 7 波動風險第一門檻參數估計值	        65
圖 8 波動風險第二門檻參數估計值	        65
圖 9 波動風險第一門檻回測參數估計值	66
圖 10波動風險第三門檻參數估計值	        66

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、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，9(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。
李能綉、李沃牆與徐孟淳(2016)，台灣股票型基金之擇時與選股能力，國立金門大學學報，6(1)，31-57.
林哲鵬、李春安、與葉智丞（2012），市場情緒與股票報酬率：來自台灣股票市場的實證分析。財務金融學刊，20(2)，55-88。
周賓凰、張宇志與林美珍（2007），投資人情緒與股票報酬互動關係。證券市場發展季刊，19(2)，153-190。
胡星陽(1998)，流動性對台灣股票報酬率的影響，中國財務學刊，5(4)，1-19.
張鼎煥、許信輝（2023），Google搜尋趨勢指數對臺灣股市大型公司之影響。屏東大學學報-管理類，(6)，1-21。
張鼎煥、許信輝 (2024)，股價漲跌是否引發網路熱搜反映投資人情緒?. 電子商務學報.
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。
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.
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。
蔡宗廷、陳欽雨（2018），Google搜尋量與台股報酬之關係研究。中山管理評論，26(1)，123–150。
謝文良與林苑宜(2012)，台灣股市之流動性共變現象，證券市場發展季刊，24(4)，135-186.

Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there?. Review of Financial Studies, 20(3), 651-707.
Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). The Cross-Section of Volatility and Expected Returns. Journal of Finance, 61(1), 259–299.
Antweiler, W., & Frank, M. Z. (2004). Is all that talk just noise? The information content of internet stock message boards. Journal of Finance, 59(3), 1259-1294.
Arrow, K. J. (1971). Essays in the theory of risk-bearing.Chicago； Markham Publishing Company.
Baker, M., & Wurgler, J. (2006). Investor Sentiment and the Cross-Section of Stock Returns. Journal of Finance, 61(4), 1645–1680.
Bali, T. G., Cakici, N., & Whitelaw, R. F. (2011). Maxing out: Stocks as lotteries and the cross-section of expected returns. Journal of financial economics, 99(2), 427-446.
Barberis, N., & Huang, M. (2008). Stocks as lotteries: The implications of probability weighting for security prices. American Economic Review, 98(5), 2066-2100.
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.
Bock, J. (2018). Quantifying macroeconomic expectations in stock markets using Google trends. SSRN Electronic Journal.
Campbell, J. Y., Grossman, S. J., & Wang, J. (1993). Trading Volume and Serial Correlation in Stock Returns, Quarterly Journal of Economics,108(4), 905-939.
Capaul, C., Rowley, I., & Sharpe, W. F. (1993). International value and growth stock returns. Financial Analysts Journal, 49(1), 27-36.
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.
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.
Chordia, T., & Swaminathan, B. (2000). Trading Volume and Cross Autocorrelations in Stock Returns. Journal of Finance,55(2),913-935.
Chou, R. Y. (1988). Volatility persistence and stock valuations: Some empirical evidence using GARCH. Journal of applied econometrics, 279-294.
Conrad, J. S., Hameed, A., & Niden, C. (1994). Volume and Autocovariances in Short Horizon Individual Security Returns, Journal of Finance,49(4), 1305-1330.
Cooper, M. (1999), Filter Rules based on Price and Volume in Individual Security Overreaction, Review of Financial Studies,12(4), 901-935.
Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. Journal of Finance, 66(5), 1461-1499.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. Journal of Finance, 25(2), 383–417.
Fama, E. F. (1976). Efficient capital markets: reply. Journal of Finance, 31(1), 143-145.
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.
Gennotte, G., & Marsh, T. A. (1993). Variations in economic uncertainty and risk premiums on capital assets. European Economic Review, 37(5), 1021-1041.
Gervais, S., Kaniel, R., & Mingelgrin, D. H. (2001). The High-Volume Return Premium. Journal of Finance, 56(3), 877-919.
Guiso, L., Sapienza, P., & Zingales, L. (2008). Trusting the stock market. Journal of Finance, 63(6), 2557–2600.
Greene, W. (1999). Econometric Analysis, Macmillan, New York.
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.
Jorion, P., & Zhang, G. (2007). Good and bad credit contagion: Evidence from credit default swaps. Journal of Financial Economics, 84(3), 860-883.
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.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291.
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.
Merton, R. C. (1987). A Simple Model of Capital Market Equilibrium with Incomplete Information. Journal of Finance, 42(3), 483–510.
Mundlak, Y. (1978). On the pooling of time series and cross section data. Econometrica: journal of the Econometric Society, 69-85.
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica: Journal of the econometric society, 347-370.
Pagan, A. R., & Pesaran, M. H. (2008). Econometric analysis of structural systems with permanent and transitory shocks. Journal of Economic Dynamics and control, 32(10), 3376-3395.
Poterba, J. M., & Summers, L. H. (1988). Mean reversion in stock prices: Evidence and implications. Journal of financial economics, 22(1), 27-59.
Pratt, J. W. (1964). Risk aversion in the small and in the large. Econometrica, 32(1), 122–136.
Salisu, A. A., Ogbonna, A. E., & Adediran, I. (2021). Stock‐induced Google trends and the predictability of sectoral stock returns. Journal of Forecasting, 40(2), 327-345.
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.
Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. Journal of Finance, 62(3), 1139-1168.
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.
Tversky, A., & Kahneman, D. (1991). Loss aversion in riskless choice: A reference-dependent model. The Quarterly Journal of Economics, 106(4), 1039–1061.