احمدپور، احمد و عظیمیان معز، امیرحسین. (1391). بررسی ارتباط رشد دارایی ها با بازده سهام در بورس اوراق بهادار تهران. فصلنامه پژوهشنامه اقتصادی. 12 (46). 227-42.
اسلامی بیدگلی، غلامرضا و طهرانی، اشرف. (1389). بررسی رابطه اعتماد بیش از حد سرمایه گذاران انفرادی و حجم مبادلات آنها در بورس اوراق بهادار تهران. فصلنامه پژوهشنامه اقتصادی. 10 (39). 231-253.
پناهی،علی و حبیبیراد، امین. (1400). بررسی تطبیقی رابطه بین شاخص بورس و حجم جستوجو به منظور شناسایی الگوی رفتاری معاملهگران بازار بورس. فصلنامه پژوهشهای راهبردی بودجه و مالی. (1). 141-169. https://sid.ir/paper/1026841/fa
حسینی ابراهیمآباد، سید علی، جهانگیری، خلیل، حیدری، حسن و قائمیاصل، مهدی. (1398). بررسی سرریزهای تکانه و تلاطم میان شاخصهای منتخب بورس اوراق بهادار تهران با استفاده از مدل Asymmetric BEKK-GARCH . فصلنامه مطالعات اقتصادی کاربردی ایران. 8 (29). 123-155. 2018.15376.2578.aes.10.22084
رضازاده، علی، شررخواه الانق، محمدحسین و جهانگیری، شهاب. (1402). تاثیر سیاست پولی بر رفتار رمهای در بورس اوراق بهادار تهران. فصلنامه مطالعات اقتصادی کاربردی ایران. 12 (47). 101-132. 10.22084/aes.2023.27600.3569
صمدی، علی حسین و ابوالحسن بیگی، کبری. (1391). آزمون قانون واگنر در کشورهای منتخب عضو سازمان همکاری اسلامی: شواهدی از همجمعی پانلی. فصلنامه پژوهشها و سیاستهای اقتصادی. 20 (64). 115-130.
عباسیان، عزتاله، مرادپوراولادی، مهدی و عباسیون، وحید. (1387). اثر متغیرهای کلان اقتصادی بر شاخص کل بورس اوراق بهادار تهران. فصلنامه پژوهشهای اقتصادی ایران. 36. 135-152.
عیوضی، احیا، مجاهدی موخر، محمدمهدی و محمدی، تیمور. (1399). تاثیر بازدهی نرخ ارز در چندکهای مختلف بازدهی سهام-رهیافت رگرسیون کوانتیل. نشریه مجلس و راهبرد، 27 (103 ). 279-306.
کشاورز حداد، غلامرضا. (1395). اقتصادسنجی دادههای خرد و ارزیابی سیاست. تهران: نشر نی.
موسوی گوکی، سید علی و بهنامراد، مهسا. (1398). پیشبینی فعالیت بازار سهام: نقش موتور جستوجوی گوگل. فصلنامه راهبرد مدیریت مالی، 27، 195-219.
Abbasian, E., Moradpour Oladi, M., & Abbasioon, V. (2008). The effect of macroeconomic variables on the Tehran Stock Exchange index. Quarterly Journal of Iranian Economic Research, 36, 135-152. [In Persian]
Adachi, Y., Masuda, M., & Takeda, F. (2017). Google search intensity and its relationship to the returns and liquidity of Japanese startup stocks.
Pacific-Basin Finance Journal, 46, 243-257.
https://doi.org/10.1016/j.pacfin.2017.09.009
Ahmadpour, A., & Azimian Moaz, A. H. (2012). Investigating the relationship between asset growth and stock returns in the Tehran Stock Exchange. Quarterly Journal of Economic Research, 12(46), 227-242. [In Persian]
Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors.
The Review of Financial Studies, 21(2), 785-818.
https://doi.org/10.1093/rfs/hhm079
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market.
Journal of Computational Science, 2(1), 1-8.
https://doi.org/10.1016/j.jocs.2010.12.007
Bontempi, M. E., Frigeri, M., Golinelli, R., & Squadrani, M. (2021). EURQ: A new web search-based uncertainty index.
Economica, 88(352), 969-1015.
https://doi.org/10.1111/ecca.12372
Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention.
The Journal of Finance, 66(5), 1461-1499.
https://doi.org/10.1111/j.1540-6261.2011.01679.x
Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor psychology and security market under- and overreactions.
The Journal of Finance, 53(6), 1839-1885.
https://doi.org/10.1111/0022-1082.00077
Eslami Bidgoli, G., & Tehrani, A. (2010). Investigating the relationship between overconfidence of individual investors and their trading volume in the Tehran Stock Exchange. Quarterly Journal of Economic Research, 10(39), 231-253. [In Persian]
Eyvazi, A., Mojahedi Mokharr, M. M., & Mohammadi, T. (2020). The effect of exchange rate returns on different quantiles of stock returns: A quantile regression approach. Journal of Parliament and Strategy, 27(103), 279-306. [In Persian]
Hidayat, Y. M., Nugraha, N., Disman, D., Sari, M., & Bustam, M. R. (2023). Investor sentiment based on search engine data for predicting stock returns in Indonesia industrial sector.
Journal of Eastern European and Central Asian Research (JEECAR), 10(6), 810-818.
https://doi.org/10.15549/jeecar.v10i6.1500
Hirshleifer, D., & Teoh, S. H. (2003). Limited attention, information disclosure, and financial reporting.
Journal of Accounting and Economics, 36(1-3), 337-386.
https://doi.org/10.1016/j.jacceco.2003.10.002
Hirshleifer, D., Lim, S. S., & Teoh, S. H. (2009). Driven to distraction: Extraneous events and underreaction to earnings news.
The Journal of Finance, 64(5), 2289-2325.
https://doi.org/10.1111/j.1540-6261.2009.01501.x
Hosseini Ebrahimabad, S. A., Jahanbiri, K., Heydari, H., & Ghaemi Asl, M. (2019). Investigating shock and volatility spillovers among selected indices of the Tehran Stock Exchange using the asymmetric BEKK-GARCH model. Quarterly Journal of Applied Economic Studies in Iran, 8(29), 123-155. [In Persian]
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.
https://doi.org/10.1016/j.ijforecast.2010.11.001
Keshavarz Haddad, G. R. (2016). Microeconometrics and policy evaluation. Ney Publishing. [In Persian]
Khosrowjerdi, M., Fylking, C. B., & Zeraatkar, N. (2023). Online information seeking during the COVID-19 pandemic: A cross-country analysis.
IFLA Journal, 49(2), 328-344.
https://doi.org/10.1177/03400352221141466
Koenker, R., & Bassett, G. (1978). Regression quantiles. Econometrica, 46(1), 33–50.
Lai, H. H., Chang, T. P., Hu, C. H., & Chou, P. C. (2022). Can Google search volume index predict the returns and trading volumes of stocks in a retail investor dominant market.
Cogent Economics & Finance, 10(1), Article 2014640.
https://doi.org/10.1080/23322039.2021.2014640
Mao, H., Counts, S., & Bollen, J. (2011). Predicting financial markets: Comparing survey, news, Twitter and search engine data.
arXiv preprint.
https://doi.org/10.48550/arXiv.1112.1051
Mousavi Goki, S. A., & Behnamrad, M. (2019). Forecasting stock market activity: The role of Google search engine. Quarterly Journal of Financial Management Strategy, (27), 195-219. [In Persian]
Nie, R. X., Wu, C., & Liang, H. M. (2024). Exploring appropriate search engine data for interval tourism demand forecasting responding a public crisis in Macao: A combined Bayesian model.
Sustainability, 16(16), Article 6892.
https://doi.org/10.3390/su16166892
Panahi, A., & Habibi Rad, A. (2021). A comparative study of the relationship between stock market index and search volume to identify the behavioral pattern of stock market traders. Quarterly Journal of Strategic Budget and Finance Research, (1), 141-169. [In Persian]
Peng, L., & Xiong, W. (2006). Investor attention, overconfidence and category learning.
Journal of Financial Economics, 80(3), 563-602.
https://doi.org/10.1016/j.jfineco.2005.05.003
Pesaran, M. H., Schuermann, T., & Weiner, S. M. (2004). Modeling regional interdependencies using a global error-correcting macroeconometric model.
Journal of Business & Economic Statistics, 22(2), 129-162.
https://doi.org/10.1198/073500104000000019
Philippas, D., Rjiba, H., Guesmi, K., & Goutte, S. (2019). Media attention and Bitcoin prices.
Finance Research Letters, 30, 37-43.
https://doi.org/10.1016/j.frl.2019.03.031
Qureshi, A. A. (2025). What makes stocks sensitive to investor sentiment: An analysis based on Google Trends. Economics and Business Review, 11(2), 39-65.
Rezazadeh, A., Sharokhah Alanaq, M. H., & Jahanbiri, S. (2023). The effect of monetary policy on herding behavior in the Tehran Stock Exchange. Quarterly Journal of Applied Economic Studies in Iran, 12(47), 101-132. [In Persian]
Samadi, A. H., & Abolhasan Beigi, K. (2012). Testing Wagner's law in selected member countries of the Organization of Islamic Cooperation: Evidence from panel cointegration. Quarterly Journal of Economic Research and Policies, 20(64), 115-130. [In Persian]
Shen, D., & Wang, C. (2023). A systematic review of investor attention: Measurements, implications, and future directions. In
Artificial intelligence, learning and computation in economics and finance (pp. 121-140).
https://doi.org/10.1007/978-3-031-15294-8_8
Swamy, V., Dharani, M., & Takeda, F. (2019). Investor attention and Google Search Volume Index: Evidence from an emerging market using quantile regression analysis.
Research in International Business and Finance, 50, 1-17.
https://doi.org/10.1016/j.ribaf.2019.04.010
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.
https://doi.org/10.1016/j.pacfin.2014.01.003
Takeda, F., & Yamazaki, H. (2006). Stock price reactions to public TV programs on listed Japanese companies.
Economics Bulletin, 13(7), 1-7.
http://economicsbulletin.vanderbilt.edu/2006/volume13/EB-06M20002A
Van der Wielen, W., & Barrios, S. (2021). Economic sentiment during the COVID pandemic: Evidence from search behavior in the EU.
Journal of Economics and Business, 115, Article 105970.
https://doi.org/10.1016/j.jeconbus.2020.105970
Vlastakis, N., & Markellos, R. N. (2012). Information demand and stock market volatility.
Journal of Banking & Finance, 36(6), 1808-1821.
https://doi.org/10.1016/j.jbankfin.2012.02.007
Wu, B., Wang, L., Lv, S. X., & Zeng, Y. R. (2021). Effective crude oil price forecasting using new text-based and big-data-driven model.
Measurement, 168, Article 108468.
https://doi.org/10.1016/j.measurement.2020.108468
Xu, Q., Bo, Z., Jiang, C., & Liu, Y. (2019). Does Google search index really help predicting stock market volatility? Evidence from a modified mixed data sampling model on volatility.
Knowledge-Based Systems, 166, 170-185.
https://doi.org/10.1016/j.knosys.2018.12.025
Ying, Q., Kong, D., & Luo, D. (2015). Investor attention, institutional ownership, and stock return: Empirical evidence from China.
Emerging Markets Finance and Trade, 51(3), 672-685.
https://doi.org/10.1080/1540496X.2015.1046339
