Investor Sentiment from Google Searches and Its Impact on the Stock Market

Authors

DOI:

https://doi.org/10.17010/ijrcm/2025/v12i2/175457

Keywords:

investor sentiment, Google search, movement of stocks, stepwise regression, lead–lag.
JEL Classification Codes : C58, D84, G12, G41, O33
Publication Chronology: Paper Submission Date : February 15, 2025 ; Paper sent back for Revision : April 25, 2025 ; Paper Acceptance Date : May 15, 2025

Abstract

Purpose : This study investigated the influence of investor sentiment—proxied through Google search trends—on stock market movements in India. With behavioral finance gaining prominence, the research aimed to quantify how digital search behavior reflected investor mood and impacted market dynamics.

Methodology : Using weekly Google search data for finance-related keywords, the study constructed a sentiment index through the Affin lexicon. This index was analyzed alongside macroeconomic indicators and stock market variables using stepwise regression and lead–lag analysis. Structural breaks were identified to assess shifts in sentiment impact over time.

Findings : The results revealed a significant relationship between investor sentiment and stock market performance. Specifically, heightened search activity correlated with increased market volatility and directional shifts. The sentiment index demonstrated predictive power, especially during periods of economic uncertainty and policy announcements.

Practical Implications : The findings suggested that Google search trends could serve as a real-time proxy for investor mood, offering valuable insights for portfolio managers, analysts, and policymakers. Incorporating sentiment analytics into forecasting models might enhance market timing and risk assessment strategies.

Originality/Value : Unlike traditional sentiment measures, this study leveraged publicly available digital search data to construct a dynamic sentiment index. It contributed to behavioral finance literature by integrating technology-driven sentiment tracking with empirical market analysis in the Indian context.

Downloads

Download data is not yet available.

Published

2025-04-15

How to Cite

Gupta, P., & Gupta, N. (2025). Investor Sentiment from Google Searches and Its Impact on the Stock Market. Indian Journal of Research in Capital Markets, 12(2), 38–57. https://doi.org/10.17010/ijrcm/2025/v12i2/175457

References

1) Aziz, T., & Ansari, V. A. (2017). Idiosyncratic volatility and stock returns: Indian evidence. Cogent Economics & Finance, 5(1), Article ID 1420998. https://doi.org/10.1080/23322039.2017.1420998

2) Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129–151. https://doi.org/10.1257/jep.21.2.129

3) Chen, Z., & Craig, K. A. (2023). Active attention, retail investor base, and stock returns. Journal of Behavioral and Experimental Finance, 39, Article ID 100820. https://doi.org/10.1016/j.jbef.2023.100820

4) Dash, S., & Mahakud, J. (2012). Investor sentiment and stock price: Evidence from India. SSRN. https://doi.org/10.2139/ssrn.2258330

5) Deb, S. (2023). Analyzing airlines stock price volatility during COVID-19 pandemic through internet search data. International Journal of Finance & Economics, 28(2), 1497–1513. https://doi.org/10.1002/ijfe.2490

6) Dimpfl, T., & Kleiman, V. (2019). Investor pessimism and the German stock market: Exploring Google search queries. German Economic Review, 20(1), 1–28. https://doi.org/10.1111/geer.12137

7) Huang, M. Y., Rojas, R. R., & Convery, P. D. (2020). Forecasting stock market movements using Google Trend searches. Empirical Economics, 59, 2821–2839. https://doi.org/10.1007/s00181-019-01725-1

8) Jamalallail, R., Tayachi, T., & BenSaïda, A. (2024). Company online presence and its effect on stock returns. International Journal of Electronic Finance, 13(1), 36–58. https://doi.org/10.1504/IJEF.2024.135167

9) James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning: With applications in R (2nd ed.). Springer Nature. https://doi.org/10.1007/978-1-0716-1418-1

10) Jiang, B., Zhu, H., Zhang, J., Yan, C., & Shen, R. (2021). Investor sentiment and stock returns during the COVID-19 pandemic. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.708537

11) 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

12) Kamath, A. N., Shenoy, S. S., Abhilasha, A., & Kumar, S. (2024). Does investor sentiment affect the Indian stock market? Evidence from Nifty 500 and sectoral indices. Cogent Economics & Finance, 12(1), Article ID 2303896. https://doi.org/10.1080/23322039.2024.2303896

13) Kostopoulos, D., Meyer, S., & Uhr, C. (2020). Google search volume and individual investor trading. Journal of Financial Markets, 49, Article ID 100544. https://doi.org/10.1016/j.finmar.2020.100544

14) Mao, H., Counts, S., & Bollen, J. (2011). Predicting financial markets: Comparing survey, news, Twitter and search engine data. arXiv Preprint. https://arxiv.org/abs/1112.1051

15) Mathur, S., & Rastogi, A. (2018). Investor sentiment and asset returns: The case of Indian stock market. Afro-Asian Journal of Finance and Accounting, 8(1), 48–64. https://doi.org/10.1504/AAJFA.2018.089198

16) Muradoglu, G., & Harvey, N. (2012). Behavioural finance: The role of psychological factors in financial decisions. Review of Behavioral Finance, 4(2), 68–80. https://doi.org/10.1108/19405971211284862

17) Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). Predicting stock market price movement using sentiment analysis: Evidence from Ghana. Applied Computing Systems, 25(1), 33–42. https://doi.org/10.2478/acss-2020-0004

18) Preis, T., Moat, H. S., & Stanley, H. E. (2013). Quantifying trading behavior in financial markets using Google Trends. Scientific Reports, 3, Article no. 1684. https://doi.org/10.1038/srep01684

19) Qadan, M., & Nama, H. (2018). Investor sentiment and the price of oil. Energy Economics, 69, 42–58. https://doi.org/10.1016/j.eneco.2017.10.035

20) Rao, T., & Srivastava, S. (2013). Modeling movements in oil, gold, forex and market indices using search volume index and Twitter sentiments. In Proceedings of the 3rd Annual ACM Web Science Conference (pp. 336–343). Association for Computing Machinery. https://doi.org/10.1145/2464464.2464521

21) Rizkiana, A., Sari, H., Hardjomidjojo, P., & Prihartono, B. (2019). The development of composite sentiment index in Indonesia based on internet-available data. Cogent Economics & Finance, 7(1), Article ID 1669399. https://doi.org/10.1080/23322039.2019.1669399

22) 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. https://doi.org/10.1002/for.2722

23) Stice, H. (2023). The supply of information and price formation: Evidence from Google's search engine. Contemporary Accounting Research, 40(3), 1999–2031. https://doi.org/10.1111/1911-3846.12866

24) Suresh, P. S., & George, S. (2016). Market sentiment dynamics and return volatility in the Indian equity market. Indian Journal of Finance, 10(6), 7–23. https://doi.org/10.17010/ijf/2016/v10i6/94872

25) 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. https://doi.org/10.1108/RBF-04-2018-0033

26) 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

27) Wang, H., Xue, L., Du, W., Wang, F., Li, P., Chen, L., & Ma, H. (2021). The effect of online investor sentiment on stock movements: An LSTM approach. In R. Lee (ed.), Computer and Information Science 2021—Summer, Studies in Computational Intelligence (Vol. 985, pp. 1–14). Springer. https://doi.org/10.1007/978-3-030-79474-3_1