Open Access Open Access  Restricted Access Subscription Access

A Bibliometric Review on Use of Google Trends in Stock Market Research


Affiliations
1 Research Scholar, Manav Rachna International Institute of Research and Studies, (Deemed to be University) Faridabad, and Assistant Professor, Delhi Institute of Advanced Studies, India
2 Associate Professor, Manav Rachna International Institute of Research and Studies, (Deemed to be University) Faridabad, India
 

As an investor sentiment measurement tool, there is growing adoption of internet technology in financial market. On a large scale, the public mood represented on the internet reflects the entire society. The internet’s pervasiveness in people’s lives has sparked a surge of interest among scholars in the field. The study of financial theories has been changed by the subject of market efficiency as a result of increasing anomalies. As a guide for future research, the purpose of this study is to conduct a bibliometric analysis focusing on tracing of investor sentiments through their web search-based queries measured by Google Trends. The analysis through VOS viewer covers 272 papers refined from SCOPUS database of 1330 articles. With the help of Citation and Co-citation analysis results identify foundation articles, turning point articles and article clusters. The study closes with recommendations for future research as well as theoretical implications.

Keywords

Bibliometric Study, Google Trends, Search Engine Data, Stock Markets Returns, VOS Viewer
User
Notifications
Font Size

  • Abdulrasool, H. D., & Othman, R. B. (2020). A review and bibliometric analysis of global research trends on the behavioural finance using scopus databases. International Journal of Management (IJM), 11(10). http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=11&IType=10
  • 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. https://doi.org/10.1016/j.ijinfomgt.2018.11.002
  • Avilés-Ochoa, E., Flores-Sosa, M., & Merigó, J. M. (2021). A bibliometric overview of volatility. Journal of Intelligent & Fuzzy Systems, 40(2), 1997–2009. https://doi.org/10.3233/jifs-189202
  • Choijil, E., Méndez, C. E., Wong, W. K., Vieito, J. P., & Batmunkh, M. U. (2022). Thirty years of herd behavior in financial markets: A bibliometric analysis. Research in International Business and Finance, 59, 101506. https://doi.org/10.1016/j.ribaf.2021.101506
  • Culnan, M. J. (1987). Mapping the intellectual structure of MIS, 1980– 1985: A co-citation analysis. MIS Quarterly, 11(3), 341. https://doi.org/10.2307/248680
  • Costa, D. F., Carvalho, F. D. M., & Moreira, B. C. D. M. (2019). Behavioral economics and behavioral finance: A bibliometric analysis of the scientific fields. Journal of Economic Surveys, 33(1), 3-24.
  • Da, Z., Engelberg, J., & Gao, P. (2011). The Sum of All FEARS: Investor Sentiment and Asset Prices. SSRN Electronic Journal. Published. https://doi.org/10.2139/ssrn.1509162
  • Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461-1499.
  • Fariska, P., & Nugraha, M. M. A. R. (2021, September). Defining and Measuring Microblogging Sentiment Investors on Stock Market: A Literature Review.
  • In 5th Global Conference on Business, Management and Entrepreneurship (GCBME 2020) (pp. 29-35). Atlantis Press.
  • Farrukh, M., Shahzad, I. A., Meng, F., Wu, Y., & Raza, A. (2020). Three decades of research in the technology analysis & strategic management: a bibliometrics analysis. Technology Analysis & Strategic Management, 33(9), 989–1005. https://doi.org/10.1080/09537325.2020.1862413
  • 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.
  • Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572. https://doi.org/10.1073/pnas.0507655102
  • Jun, S. P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Techno-logical Forecasting and Social Change, 130, 69–87. https://doi.org/10.1016/j.techfore.2017.11.009
  • Kim, N., Lučivjanská, K., Molnár, P., & Villa, R. (2019). Google searches and stock market activity: Evidence from Norway. Finance Research Letters, 28, 208–220. https://doi.org/10.1016/j.frl.2018.05.003
  • Komalasari, P. T., Asri, M., Purwanto, B. M., & Setiyono, B. (2021). Herding behaviour in the capital market: What do we know and what is next? Management Review Quarterly. Published. https://doi.org/10.1007/s11301-021-00212-1
  • López-Cabarcos, M. N., Pérez-Pico, A. M., Vázquez-Rodríguez, P., & López-Pérez, M. L. (2019). Investor sentiment in the theoretical field of behavioural finance. Economic Research-Ekonomska Istraživanja, 33(1), 2101–2119. https://doi.org/10.1080/1331677x.2018.1559748
  • Paule-Vianez, J., Gómez-Martínez, R., & Prado-Román, C. (2020). A bibliometric analysis of behavioural finance with mapping analysis tools. European Research on Management and Business Economics, 26(2), 71–77. https://doi.org/10.1016/j.iedeen.2020.01.001
  • Pilkington, A., & Fitzgerald, R. (2006). Operations management themes, concepts and relationships: A forward retrospective of IJOPM. International Journal of Operations & Production Management, 26(11), 1255–1275. https://doi.org/10.1108/01443570610705854
  • Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269. https://doi.org/10.1002/asi.4630240406
  • Su, Y., Qu, Y., & Kang, Y. (2021). Online public opinion and asset prices: a literature review. Data Science in Finance and Economics, 1(1), 60–76. https://doi.org/10.3934/dsfe.2021004
  • Subudhi, R. N. (2019). Testing of Hypothesis: Concepts and Applications. Methodological Issues in Management Research: Advances, Challenges, and the Way Ahead, 127–143. https://doi.org/10.1108/978-1-78973-973-220191009
  • Tantaopas, P., Padungsaksawasdi, C., & Treepongkaruna, S. (2016). Attention effect via internet search intensity in Asia-Pacific stock markets. Pacific-Basin Finance Journal, 38, 107-124.
  • Urquhart, A. (2018). What causes the attention of Bitcoin? Economics Letters, 166, 40–44. https://doi.org/10.1016/j.econlet.2018.02.017
  • Valcanover, V. M., Sonza, I. B., & da Silva, W. V. (2020). Behavioral Finance Experiments: A Recent Systematic Literature Review. SAGE Open, 10(4),215824402096967. https://doi.org/10.1177/2158244020969672
  • Vozlyublennaia, N. (2014). Investor attention, index performance, and return predictability. Journal of Banking & Finance, 41, 17–35. https://doi.org/10.1016/j.jbankfin.2013.12.010
  • Zhang, J. Z., Srivastava, P. R., Sharma, D., & Eachempati, P. (2021). Big data analytics and machine learning: A retrospective overview and bibliometric analysis. Expert Systems with Applications, 184, 115561. https://doi.org/10.1016/j.eswa.2021.115561

Abstract Views: 315

PDF Views: 0




  • A Bibliometric Review on Use of Google Trends in Stock Market Research

Abstract Views: 315  |  PDF Views: 0

Authors

Divya Jain
Research Scholar, Manav Rachna International Institute of Research and Studies, (Deemed to be University) Faridabad, and Assistant Professor, Delhi Institute of Advanced Studies, India
Meghna Chhabra
Associate Professor, Manav Rachna International Institute of Research and Studies, (Deemed to be University) Faridabad, India

Abstract


As an investor sentiment measurement tool, there is growing adoption of internet technology in financial market. On a large scale, the public mood represented on the internet reflects the entire society. The internet’s pervasiveness in people’s lives has sparked a surge of interest among scholars in the field. The study of financial theories has been changed by the subject of market efficiency as a result of increasing anomalies. As a guide for future research, the purpose of this study is to conduct a bibliometric analysis focusing on tracing of investor sentiments through their web search-based queries measured by Google Trends. The analysis through VOS viewer covers 272 papers refined from SCOPUS database of 1330 articles. With the help of Citation and Co-citation analysis results identify foundation articles, turning point articles and article clusters. The study closes with recommendations for future research as well as theoretical implications.

Keywords


Bibliometric Study, Google Trends, Search Engine Data, Stock Markets Returns, VOS Viewer

References





DOI: https://doi.org/10.23862/kiit-parikalpana%2F2023%2Fv19%2Fi1%2F220838