本研究主要探討企業 的網路搜尋量與分析師提供投資建議報告之關聯，考慮到企業 網路搜尋量與分析師提供投資建議報告之關聯，考慮到企業 網路搜尋量與分析師提供投資建議報告之關聯，考慮到企業 財報公布日前的網路搜尋量提高，代表投資人對該企 業關注升促使市場財報公布日前的網路搜尋量提高，代表投資人對該企 業關注升促使市場財報公布日前的網路搜尋量提高，代表投資人對該企 業關注升促使市場財報公布日前的網路搜尋量提高，代表投資人對該企 業關注升促使市場財報公布日前的網路搜尋量提高，代表投資人對該企 業關注升促使市場業的討論更加熱絡，進而吸引分析師目光增對於企之報告提供意願故 業的討論更加熱絡，進而吸引分析師目光增對於企之報告提供意願故 業的討論更加熱絡，進而吸引分析師目光增對於企之報告提供意願故 業的討論更加熱絡，進而吸引分析師目光增對於企之報告提供意願故 業的討論更加熱絡，進而吸引分析師目光增對於企之報告提供意願故 業的討論更加熱絡，進而吸引分析師目光增對於企之報告提供意願故 業的討論更加熱絡，進而吸引分析師目光增對於企之報告提供意願故 業的討論更加熱絡，進而吸引分析師目光增對於企之報告提供意願故 以網路搜尋量出發，討論分析師投資建議報告與企業 以網路搜尋量出發，討論分析師投資建議報告與企業 以網路搜尋量出發，討論分析師投資建議報告與企業 盈餘宣告 日前後的網路搜尋量之 關聯性，驗證 關聯性，驗證 關聯性，驗證 企業 盈餘宣告日 前網路搜尋量的 變化 ， 是否會增加分析師提供報告之意願， 是否會增加分析師提供報告之意願， 是否會增加分析師提供報告之意願再探討 企業 盈餘宣告 日前網路搜尋量的變化，是否加強 日前網路搜尋量的變化，是否加強 日前網路搜尋量的變化，是否加強 分析師投資建議報告量 與投資人 對該企業的網路搜尋量 之正向影響 。實證結果顯示， 實證結果顯示， 企業盈餘宣告日前之網路搜尋熱度 和分析師提供之投資建議報告量不存在顯著的正相關，另外企業盈餘宣日前網路搜 分析師提供之投資建議報告量不存在顯著的正相關，另外企業盈餘宣日前網路搜 分析師提供之投資建議報告量不存在顯著的正相關，另外企業盈餘宣日前網路搜 分析師提供之投資建議報告量不存在顯著的正相關，另外企業盈餘宣日前網路搜 分析師提供之投資建議報告量不存在顯著的正相關，另外企業盈餘宣日前網路搜 尋量 提高，對分析師投資建議報告尋量 提高，對分析師投資建議報告尋量 提高，對分析師投資建議報告和與投資人對該企業的網路搜尋量 沒有顯著 有顯著 正相關 ， 且分析師投資評等的不一致性與人對該企業網路搜尋量同樣沒有顯著 且分析師投資評等的不一致性與人對該企業網路搜尋量同樣沒有顯著 且分析師投資評等的不一致性與人對該企業網路搜尋量同樣沒有顯著 且分析師投資評等的不一致性與人對該企業網路搜尋量同樣沒有顯著 正向影響 。

The present study primarily investigates the association between a company's online search volume and the provision of investment recommendation reports by analysts.Considering the increased online search volume prior to a company's financial statement release,which signifies heightened investor interest and greater market discussions about the company,analysts are more likely to pay attention and provide analysis reports on the company.Thus,starting from the perspective of online search volume,the study explores the relationship between the volume of analyst investment recommendation reports and the changes in online search volume before and after the company's earnings announcement. It examines whether the fluctuations in online search volume prior to the earnings announcement strengthen the willingness of analysts to provide reports and whether they enhance the positive impact of analyst recommendation reports on investor online search volume.The empirical results indicate that there is no significant positive correlation between online search intensity prior to the earnings announcement and the volume of analyst investment recommendation reports.Additionally,the increase in online search volume prior to the earnings announcement does not significantly moderate the relationship between analyst recommendation reports and investor online search volume. Moreover,the inconsistency of analyst investment ratings does not significantly moderate the relationship between investor online search volume and the volume of analyst recommendation reports.

1.Abarbanell, J. S. (1991). Do analysts' earnings forecasts incorporate information in prior stock price changes? Journal of Accounting and Economics, 14(2), 147-165.
2.Barber, B., Lehavy, R., McNichols, M., & Trueman, B. (2001). Can investors profit from the prophets? Security analyst recommendations and stock returns. The Journal of finance, 56(2), 531-563.
3.Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261-292.
4.Bradshaw, M. T., Richardson, S. A., & Sloan, R. G. (2001). Do analysts and auditors use information in accruals? Journal of Accounting Research, 39(1), 45-74.
5.Breyer, B. N., Sen, S., Aaronson, D. S., Stoller, M. L., Erickson, B. A., & Eisenberg, M. L. (2011). Use of Google Insights for Search to track seasonal and geographic kidney stone incidence in the United States. Urology, 78(2), 267-271.
6.Brown, L. D., Richardson, G. D., & Schwager, S. J. (1987). An information interpretation of financial analyst superiority in forecasting earnings. Journal of Accounting Research, 49-67.
7.Brown, S. J., & Warner, J. B. (1985). Using daily stock returns: The case of event studies. Journal of Financial Economics, 14(1), 3-31.
8.Chan, L. K., & Lakonishok, J. (1995). The behavior of stock prices around institutional trades. The Journal of Finance, 50(4), 1147-1174.
9.Chen, J., Hong, H., & Stein, J. C. (2002). Breadth of ownership and stock returns. Journal of Financial Economics, 66(2-3), 171-205.
10.Chen, T., Harford, J., & Lin, C. (2015). Do analysts matter for governance? Evidence from natural experiments. Journal of financial Economics, 115(2), 383-410.
11.Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, 2-9.
12.Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461-1499.
13.Darrough, M. N., & Russell, T. (2002). A positive model of earnings forecasts: Top down versus bottom up. The Journal of Business, 75(1), 127-152.
14.Della Penna, N., & Huang, H. (2009). Constructing consumer sentiment index for US using internet search patterns. Department of Economics, WP, 26.
15.DellaVigna, S., & Pollet, J. M. (2009). Investor inattention and Friday earnings announcements. The Journal of Finance, 64(2), 709-749.
16.Dimpfl, T., & Jank, S. (2016). Can internet search queries help to predict stock market volatility? European Financial Management, 22(2), 171-192.
17.Drake, M. S., Roulstone, D. T., & Thornock, J. R. (2012). Investor information demand: Evidence from Google searches around earnings announcements. Journal of Accounting Research, 50(4), 1001-1040.
18.Firth, M., Lin, C., Liu, P., & Xuan, Y. (2013). The client is king: Do mutual fund relationships bias analyst recommendations? Journal of Accounting Research, 51(1), 165-200.
19.Fondeur, Y., & Karamé, F. (2013). Can Google data help predict French youth unemployment? Economic Modelling, 30, 117-125.
20.Gilson, S. C. (2000). Analysts and information gaps: lessons from the UAL buyout. Financial Analysts Journal, 56(6), 82-110.
21.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.
22.Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M., & Watts, D. J. (2010). Predicting consumer behavior with Web search. Proceedings of the National Academy of Sciences, 107(41), 17486-17490.
23.Hartzmark, S. M., & Solomon, D. H. (2013). The dividend month premium. Journal of Financial Economics, 109(3), 640-660.
24.Hennessey, S. M. (1995). Earnings forecast revisions and security returns: Canadian evidence. Accounting and Business Research, 25(100), 240-252.
25.Hilbert, M., & López, P. (2011). The world’s technological capacity to store, communicate, and compute information. Science, 332(6025), 60-65.
26.Huang, Z. J., Huang, H., Song, Y. Y., & Feng, T. Y. (2020). Earnings Management, Analyst Forecasts and Credit Rating of Corporate Bond: Empirical Evidences from Chinese Listed Companies. Journal of Finance and Economics, 8(1), 21-32.
27.Huang, A. H., Lehavy, R., Zang, A. Y., & Zheng, R. (2018). Analyst information discovery and interpretation roles: A topic modeling approach. Management Science, 64(6), 2833-2855.
28.Johnson, T. C. (2004). Forecast dispersion and the cross section of expected returns. The Journal of Finance, 59(5), 1957-1978.
29.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.
30.Kim, K., Ryu, D., & Yang, H. (2019). Investor sentiment, stock returns, and analyst recommendation changes: The KOSPI stock market. Investment Analysts Journal, 48(2), 89-101.
31.Lin, H. W., & McNichols, M. F. (1998). Underwriting relationships, analysts' earnings forecasts and investment recommendations. Journal of Accounting and Economics, 25(1), 101-127.
32.Matsumoto, D. A. (2002). Management's incentives to avoid negative earnings surprises. The Accounting Review, 77(3), 483-514.
33.Mavragani, A., & Tsagarakis, K. P. (2016). YES or NO: Predicting the 2015 GReferendum results using Google Trends. Technological Forecasting and Social Change, 109, 1-5.
34.McInish, T. H., & Wood, R. A. (1992). An analysis of intraday patterns in bid/ask spreads for NYSE stocks. The Journal of Finance, 47(2), 753-764.
35.Mikhail, M. B., Walther, B. R., & Willis, R. H. (2004). Do security analysts exhibit persistent differences in stock picking ability? Journal of Financial Economics, 74(1), 67-91.
36.Moat, H. S., Preis, T., Olivola, C. Y., Liu, C., & Chater, N. (2014). Using big data to predict collective behavior in the real world. Behavioral and Brain Sciences, 37(1), 92-93.
37.Negash, E. S., Zhu, W., Lu, Y., & Wang, Z. (2020). Does Chinese inward foreign direct investment improve the productivity of domestic firms? Horizontal linkages and absorptive capacities: Firm-level evidence from Ethiopia. Sustainability, 12(7), 3023.
38.Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3(1), 1-6.
39.Premti, A., Garcia-Feijoo, L., & Madura, J. (2017). Information content of analyst recommendations in the banking industry. International Review of Financial Analysis, 49, 35-47.
40.Ramnath, S., Rock, S., & Shane, P. (2008). The financial analyst forecasting literature: A taxonomy with suggestions for further research. International Journal of Forecasting, 24(1), 34-75.
41.Schipper, K. (1991). Analysts' forecasts. Accounting Horizons, 5(4), 105-121.
42.Siganos, A. (2013). Google attention and target price run ups. International Review of Financial Analysis, 29, 219-226.
43.Stickel, S. E. (1990). Predicting individual analyst earnings forecasts. Journal of Accounting Research, 28(2), 409-417.
44.Van Campen, J. S., Van Diessen, E., Otte, W. M., Joels, M., Jansen, F. E., & Braun, K. P. (2014). Does saint Nicholas provoke seizures? Hints from Google trends. Epilepsy & Behavior, 32, 132-134.
45.Womack, K. L. (1996). Do brokerage analysts' recommendations have investment value? The Journal of Finance, 51(1), 137-167.
46.Wu, L., & Brynjolfsson, E. (2015). The future of prediction: How Google searches foreshadow housing prices and sales. In Economic Analysis of the Digital Economy (pp. 89-118). University of Chicago Press.
47.Yvonne, I., & Lee, F. (2016). Analyst Coverage and Stock Price Crash Risk (Doctoral dissertation).