What makes stocks sensitive to investor sentiment: An analysis based on Google Trends
DOI:
https://doi.org/10.18559/ebr.2025.2.1790Keywords:
investor sentiment, stock market, search engine, firm characteristics, Google trendsAbstract
We capture Google’s vast search volume through Google Trends to generate a weekly investor sentiment index (2018–2022) using the most popular keywords (extracted from Google Search) from a keywords collection of 92,000+ words found in business, finance, and common language dictionaries. The results show that Google Trends is an efficient measure of investor sentiment as reflected in relative trading volume. To check what makes stocks sensitive to investor sentiment, 500 randomly selected US firms from various industries are categorised by firm characteristics. We generate two sub-portfolios: large, old, profitable, and dividend-yielding firms versus small, young, unprofitable, and non-dividend-yielding firms—and find the relative trading volume of the latter to be more sensitive to investor sentiment. Our results remain robust when control and autoregressive variables are introduced, in addition to when an alternative measure of sentiment is used, thereby confirming our primary findings.
JEL Classification
General (G10)
Portfolio Choice • Investment Decisions (G11)
Information and Market Efficiency • Event Studies • Insider Trading (G14)
General (G40)
Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets (G41)
Downloads
References
Aboody, D., Even-Tov, O., Lehavy, R., & Trueman, B. (2018). Overnight returns and firm-specific investor sentiment. Journal of Financial and Quantitative Analysis, 53(2), 485–505. DOI: https://doi.org/10.1017/S0022109017000989
View in Google Scholar
Agarwal, S., Kumar, S., & Goel, U. (2019). Stock market response to information diffusion through internet sources: A literature review. International Journal of Information Management, 45, 118–131. DOI: https://doi.org/10.1016/j.ijinfomgt.2018.11.002
View in Google Scholar
Anggoro, D. A., & Supriyanti, W. (2019). Improving accuracy by applying Z-Score normalization in linear regression and polynomial regression model for real estate data. International Journal of Emerging Trends & Technology in Computer Science, 7(11), 549–555. DOI: https://doi.org/10.30534/ijeter/2019/247112019
View in Google Scholar
Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross-section of stock returns. Journal of Finance, 61(4), 1645–1680. DOI: https://doi.org/10.1111/j.1540-6261.2006.00885.x
View in Google Scholar
Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129–152. DOI: https://doi.org/10.1257/jep.21.2.129
View in Google Scholar
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. DOI: https://doi.org/10.1093/rfs/hhm079
View in Google Scholar
Barberis, N. (2003). Handbook of the economics of finance. University of Chicago.
View in Google Scholar
Bashir, U., Kayani, U. N., Khan, S., Polat, A., Hussain, M. & Aysan, A. F. (2024). Investor sentiment and stock price crash risk: The mediating role of analyst herding. Computers in Human Behavior Reports, 13, 100371. DOI: https://doi.org/10.1016/j.chbr.2024.100371
View in Google Scholar
Berger, D. (2022). Investor sentiment: A retail trader activity approach. Review of Accounting and Finance, 21(2), 61–82. DOI: https://doi.org/10.1108/RAF-06-2021-0152
View in Google Scholar
Challet, D., & Ayed, A. B. (2013). Predicting financial markets with Google Trends and not so random keywords. SSRN Electronic Journal. DOI: https://doi.org/10.2139/ssrn.2310621
View in Google Scholar
Chen, H. L., & Stejskalova, J. (2024). A collection of wisdom in predicting sector returns: The use of Google Search Volume Index. Journal of Risk and Financial Management, 17(10), 452. DOI: https://doi.org/10.3390/jrfm17100452
View in Google Scholar
Costa, L., Veira, E., & Madaleno, M. (2024). Does googling impact Euronext Stock Returns? IBIMA Business Review. DOI: https://doi.org/10.5171/2024.963956
View in Google Scholar
Costola, M., Iacopini, M., & Santagiustina, C. R. (2021). Google Search volumes and the financial markets during the COVID-19 outbreak. Finance Research Letters, 42, 101884. DOI: https://doi.org/10.1016/j.frl.2020.101884
View in Google Scholar
Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. Journal of Finance, 66(5), 1461–1499. DOI: https://doi.org/10.1111/j.1540-6261.2011.01679.x
View in Google Scholar
Da, Z., Engelberg, J., & Gao, P. (2015). The sum of All FEARS investor sentiment and asset prices. The Review of Financial Studies, 28(1), 1–32. DOI: https://doi.org/10.1093/rfs/hhu072
View in Google Scholar
Das, S. R., & Chen, M. Y. (2007). Yahoo! for Amazon: Sentiment extraction from small talk on the web. Management Science, 53(9), 1375–1388. DOI: https://doi.org/10.1287/mnsc.1070.0704
View in Google Scholar
Dimpfl, T., & Jank, S. (2016). Can Internet Search Queries help to predict stock market volatility? European Financial Management, 171–192. DOI: https://doi.org/10.1111/eufm.12058
View in Google Scholar
Duc, D. T., Hoai, N. T., Nguyen, V. P., Nguyen, D. P., Anh, N. H., & Hai, H. H. (2024). Google Search intensity and stock returns in frontier markets: Evidence from the Vietnamese market. Economics and Business Review, 10(1), 30–56. DOI: https://doi.org/10.18559/ebr.2024.1.778
View in Google Scholar
Ekinci, C., & Bulut, A. E. (2021). Google search and stock returns: A study on BIST 100 stocks. Global Finance Journal, 47, 100518. DOI: https://doi.org/10.1016/j.gfj.2020.100518
View in Google Scholar
El Oubani, A. (2024). Quantile connectedness between social network sentiment and sustainability index volatility: Evidence from the Moroccan financial market. Economics and Business Review, 10(3), 163–196. DOI: https://doi.org/10.18559/ebr.2024.3.1200
View in Google Scholar
Ferguson, N. J., Philip, D., Lam, H., & Guo, J. (2015). Media content and stock returns: The predictive power of press. Multinational Finance Journal, 19(1), 1–31. DOI: https://doi.org/10.17578/19-1-1
View in Google Scholar
Google Search. (2023, December). How search works: Ranking results. https://www. google.com/search/howsearchworks/how-search-works/ranking-results/
View in Google Scholar
Hadjmohamed, W., & Bouri, A. (2023). Investor sentiment and firm characteristics. Global Business and Economics Review, 29(4), 509–524. DOI: https://doi.org/10.1504/GBER.2023.134360
View in Google Scholar
Haritha, P. H., & Rishad, A. (2020). An empirical examination of investor sentiment and stock market volatility: evidence from India. Financial Innovation, 6, 34. https://jfin-swufe.springeropen.com/articles/10.1186/s40854-020-00198-x DOI: https://doi.org/10.1186/s40854-020-00198-x
View in Google Scholar
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–292. DOI: https://doi.org/10.2307/1914185
View in Google Scholar
Kenton, W. (2024). S&P 500 Index: What it’s for and why it’s important in investing. https://www.investopedia.com/terms/s/sp500.asp#toc-what-is-the-sp-500-index
View in Google Scholar
Lee, C. M., & Kumar, A. (2006). Retail investor sentiment and return comovements. Journal of Finance, 61(5), 2451–2486. DOI: https://doi.org/10.1111/j.1540-6261.2006.01063.x
View in Google Scholar
Maddodi, S., & Kunte, S. R. (2024). Precision forecasting in perilous times: stock market predictions leveraging Google Trends and momentum indicators during COVID-19. Managerial Finance, 50(10), 1747–1772. DOI: https://doi.org/10.1108/MF-02-2024-0128
View in Google Scholar
Molnár, P., Kim, N., Lučivjanská, K., & Villa, R. (2019). Google searches and stock market activity: Evidence from Norway. Finance Research Letters, 28, 208–220. DOI: https://doi.org/10.1016/j.frl.2018.05.003
View in Google Scholar
Oliveira, N., Cortez, P., & Areal, N. (2017). The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Systems with Applications, 73, 125–144. DOI: https://doi.org/10.1016/j.eswa.2016.12.036
View in Google Scholar
Papadamou, S. T., Fassas, A., Kenourgios, D., & Dimitriou, D. (2023). Effects of the first wave of COVID-19 pandemic on implied stock market volatility: International evidence using a google trend measure. The Journal of Economic Asymmetries, 28, e00317. DOI: https://doi.org/10.1016/j.jeca.2023.e00317
View in Google Scholar
Preis, T., Moat, H. S., & Stanley, E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3, 1684. DOI: https://doi.org/10.1038/srep01684
View in Google Scholar
Ricciardi, V., & Simon, H. K. (2001). What is behavioral finance? Business, Education and Technology Journal, 2(2), 1–9. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=256754
View in Google Scholar
Rogers, S. (2021). 15 tips for getting the most out of Google Trends. https://blog.google/products/search/15-tips-getting-most-out-google-trends/#:~:text=Data%20by%20date%3A%20Google%20Trends,to%20minute%2Dby%2Dminute
View in Google Scholar
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. DOI: https://doi.org/10.1002/for.2722
View in Google Scholar
Shleifer, A., & Summers, L. H. (1990). The noise trader approach to finance. Journal of Economic Perspectives, 4(2), 19–33. DOI: https://doi.org/10.1257/jep.4.2.19
View in Google Scholar
Smales, L. (2021). Investor attention and global market returns during the COVID-19 crisis. International Review of Financial Analysis, 73, 101616. DOI: https://doi.org/10.1016/j.irfa.2020.101616
View in Google Scholar
Statista. (2023). Statista – Online Search. https://www.statista.com/markets/424/topic/541/online-search/#overview
View in Google Scholar
Suer, O., & Akarsu, S. (2021). How investor attention affects stock returns? Some international evidence. Borsa Istanbul Review, 22(3), 616–626. DOI: https://doi.org/10.1016/j.bir.2021.09.001
View in Google Scholar
Swamy, V., & Dharani, M. (2019). Investor attention using the Google search volume index—impact on stock returns. Review of Behavioral Finance, 11(1), 56–70. DOI: https://doi.org/10.1108/RBF-04-2018-0033
View in Google Scholar
Szczygielski, J. J., Charteris, A., Bwanya, P. R., & Brzeszczyński, J. (2024). Google Search trends and stock markets: Sentiment, attention or uncertainty? International Review of Financial Analysis, 91, 102549. DOI: https://doi.org/10.1016/j.irfa.2023.102549
View in Google Scholar
Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. Journal of Finance, 62(3), 1139–1168. DOI: https://doi.org/10.1111/j.1540-6261.2007.01232.x
View in Google Scholar
Wang, J., Rao, Y., & Peng, D. (2015). What drives the stock market “media coverage effect”: Expected media attention or unexpected media attention? Systems Engineering – Theory & Practice, 35(1), 37–48. https://sysengi.cjoe.ac.cn/EN/10.12011/1000-6788(2015)1-37
View in Google Scholar
Weber, M., & Camerer, C. F. (1998). The disposition effect in securities trading: An experimental analysis. Journal of Economic Behavior & Organization, 33(2), 167– 184. DOI: https://doi.org/10.1016/S0167-2681(97)00089-9
View in Google Scholar
Yang, H., Ryu, D., & Ryu, D. (2017). Investor sentiment, asset returns and firm characteristics: Evidence from the Korean stock market. Investment Analysts Journal, 46(2), 132–147. DOI: https://doi.org/10.1080/10293523.2016.1277850
View in Google Scholar
Zhang, B., Ren, H., & Gao, Z. (2020). Googling investor sentiment around the world. Journal of Financial and Quantitative Analysis, 55(2), 549–580. DOI: https://doi.org/10.1017/S0022109019000061
View in Google Scholar
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Adeel Ali Qureshi
This work is licensed under a Creative Commons Attribution 4.0 International License.
