Impact of Investors’ Attention on the Global Stock Market : A Bibliometric Analysis
DOI:
https://doi.org/10.17010/ijf/2023/v17i10/170881Keywords:
bibliometric analysis
, biblioshiny, global stock market, investor’s attention, scientific mapping, VOSviewerJEL Classification Codes
, G01, G15, G40, G41Paper Submission Date
, October 10, 2022, Paper sent back for Revision, June 14, 2023, Paper Acceptance Date, July 5, Paper Published Online, October 15, 2023Abstract
Purpose: The stock market is a highly dynamic financial marketplace that drives economic progress. This study employed bibliometric analysis, using tools like Biblioshiny and VOSviewer, to execute quantitative analysis on research papers around the stock market in terms of investor attention. It improved the literature review’s quality by analyzing 632 research papers from the Scopus database.
Design/Methodology/Approach: The selected papers from 1994 to 2022 were reviewed and analyzed. A conceptual model identified significant themes, while a thematic map provided a comprehensive visual representation of the interconnections.
Results: The analysis yielded noteworthy findings, highlighting a significant surge in academic publications. Most of the research regarding investor attention and the stock market has been concentrated in China, the UK, the USA, and Australia, indicating this topic’s global relevance. Among the identified high-frequency keywords, investor attention, investment, and attention stood out as prominent themes within the literature. Finance Research Letters and Pacific-Basin Finance Journal were identified as influential publication outlets.
Originality/Value: This research introduced novel bibliometric analysis techniques, providing robust insights into the stock market concerning investor’s attention. Integrating a conceptual model and thematic map enhanced understanding and contributed innovatively to the field.
Practical Implications: The study offered regulatory implications, aiding policymakers in understanding the stock market concerning investor’s attention. Practitioners could utilize the findings for informed decision-making in stock companies and gain insights into emerging research trends.
Downloads
Downloads
Published
How to Cite
Issue
Section
References
Andrei, D., & Hasler, M. (2015). Investor attention and stock market volatility. The Review of Financial Studies, 28(1), 33-72. https://doi.org/10.1093/rfs/hhu059
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007
Chen, Y., & Huang, Z. (2021). Measuring the effects of investor attention on China's stock returns. Data Science in Finance and Economics, 1(4), 327–344. https://doi.org/10.3934/DSFE.2021018
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
Dimpfl, T., & Jank, S. (2016). Can internet search queries help to predict stock market volatility? European Financial Management, 22(2), 171–192. https://doi.org/10.1111/eufm.12058
Ellegaard, O., & Wallin, J. A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809–1831. https://doi.org/10.1007/s11192-015-1645-z
Han, L., Li, Z., & Yin, L. (2018). Investor attention and stock returns: International evidence. Emerging Markets Finance and Trade, 54(14), 3168–3188. https://doi.org/10.1080/1540496X.2017.1413980
Hao, J., & Xiong, X. (2021). Retail investor attention and firms' idiosyncratic risk: Evidence from China. International Review of Financial Analysis, 74, 101675. https://doi.org/10.1016/j.irfa.2021.101675
He, F., Qin, S., & Zhang, X. (2021). Investor attention and platform interest rate in Chinese peer-to-peer lending market. Finance Research Letters, 39, 101559. https://doi.org/10.1016/j.frl.2020.101559
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
Hirshleifer, D., Lim, S. S., & Teoh, S. H. (2011). Limited investor attention and stock market misreactions to accounting information. The Review of Asset Pricing Studies, 1(1), 35–73. https://doi.org/10.1093/rapstu/rar002
Kakran, S., Sidhu, A., Bajaj, P. K., & Dagar, V. (2023). Novel evidence from APEC countries on stock market integration and volatility spillover: A Diebold and Yilmaz approach. Cogent Economics & Finance, 11(2), 2254560. https://doi.org/10.1080/23322039.2023.2254560
Kaur, S., & Pasricha, J. S. (2019). Problems faced by bank employees in implementation of financial inclusion schemes. Indian Journal of Finance, 13(12), 34–49. https://doi.org/10.17010/ijf/2019/v13i12/149267
Kavita, & Suman. (2019). Determinants of financial inclusion in India: A literature review. Indian Journal of Finance, 13(11), 53–61. https://doi.org/10.17010/ijf/2019/v13i11/148417
Khongwir, C. M., & Sharmiladevi, J. C. (2023). A bibliometric analysis on financial inclusion and financial literacy – Analyzing the current research trends. Indian Journal of Finance, 17(2), 27–53. https://doi.org/10.17010/ijf/2023/v17i2/172638
Kulshrestha, V., & Jain, K. (2018). Technology integration in the mobile communication industry: A review. Prabandhan: Indian Journal of Management, 11(4), 7–26. https://doi.org/10.17010/pijom/2018/v11i4/122824
Li, H., An, H., Wang, Y., Huang, J., & Gao, X. (2016). Evolutionary features of academic articles co-keyword network and keywords co-occurrence network: Based on two-mode affiliation network. Physica A: Statistical Mechanics and Its Applications, 450, 657–669. https://doi.org/10.1016/j.physa.2016.01.017
Li, J., Tang, L., & Wang, S. (2020). Forecasting crude oil price with multilingual search engine data. Physica A: Statistical Mechanics and Its Applications, 551, 124178. https://doi.org/10.1016/j.physa.2020.124178
Li, J., & Yu, J. (2012). Investor attention, psychological anchors, and stock return predictability. Journal of Financial Economics, 104(2), 401–419. https://doi.org/10.1016/j.jfineco.2011.04.003
Li, Y., Goodell, J. W., & Shen, D. (2021). Comparing search-engine and social-media attentions in finance research: Evidence from cryptocurrencies. International Review of Economics & Finance, 75, 723–746. https://doi.org/10.1016/j.iref.2021.05.003
Liu, Y., Niu, Z., Suleman, M. T., Yin, L., & Zhang, H. (2022). Forecasting the volatility of crude oil futures: The role of oil investor attention and its regime switching characteristics under a high-frequency framework. Energy, 238(Part A), 121779. https://doi.org/10.1016/j.energy.2021.121779
Lohan, S., Sidhu, A., & Kakran, S. (2023). The impact of investor's attention on global stock market: Statistical review of literature. International Journal of Business Forecasting and Marketing Intelligence, 9, 1111.
Lou, D. (2014). Attracting investor attention through advertising. The Review of Financial Studies, 27(6), 1797-1829. https://doi.org/10.1093/rfs/hhu019
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A systematic review of fundamental and technical analysis of stock market predictions. Artificial Intelligence Review, 53(4), 3007–3057. https://doi.org/10.1007/s10462-019-09754-z
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
Sathiyan, S., & Panda, P. K. (2016). Financial inclusion in India: An analysis of pattern and determinants. Indian Journal of Finance, 10(4), 41–53. https://doi.org/10.17010/ijf/2016/v10i4/90799
Seasholes, M. S., & Wu, G. (2007). Predictable behavior, profits, and attention. Journal of Empirical Finance, 14(5), 590–610. https://doi.org/10.1016/j.jempfin.2007.03.002
Sen, S. (2020). Parsimonious motivational factors for participation in sporting events: A review. Indian Journal of Marketing, 50(5–7), 61–74. https://doi.org/10.17010/ijom/2020/v50/i5-7/153072
Seth, N., & Sidhu, A. (2021). Price discovery and volatility spillover for Indian energy futures market in pre- and post-crisis periods. Indian Journal of Finance. 15(8), 24–39. https://doi.org/10.17010/ijf/2021/v15i8/165816
Shah, T. A. (2018). Risk parity approach: A thematic review of literature and research opportunities. Indian Journal of Research in Capital Markets, 5(3), 42–56. https://doi.org/10.17010/ijrcm/2018/v5/i3/138186
Sharma, S., & Sharma, S. K. (2017). Development of ITC E-Choupal based rural financial inclusion model. Indian Journal of Finance, 11(10), 20–32. https://doi.org/10.17010/ijf/2017/v11i10/118773
Shen, D., Zhang, W., Xiong, X., Li, X., & Zhang, Y. (2016). Trading and non-trading period Internet information flow and intraday return volatility. Physica A: Statistical Mechanics and Its Applications, 451, 519–524. https://doi.org/10.1016/j.physa.2016.01.086
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. https://doi.org/10.1016/j.pacfin.2016.03.008
Tripathi, M., Kumar, S., Sonker, S. K., & Babbar, P. (2018). Occurrence of author keywords and keywords plus in social sciences and humanities research: A preliminary study. COLLNET Journal of Scientometrics and Information Management, 12(2), 215–232. https://doi.org/10.1080/09737766.2018.1436951
Tuyon, J., & Ahmad, Z. (2016). Behavioural finance perspectives on Malaysian stock market efficiency. Borsa Istanbul Review, 16(1), 43–61. https://doi.org/10.1016/j.bir.2016.01.001
Urquhart, A. (2018). What causes the attention of Bitcoin? Economics Letters, 166, 40–44. https://doi.org/10.1016/j.econlet.2018.02.017
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
Wu, Y., Han, L., & Yin, L. (2019). Our currency, your attention: Contagion spillovers of investor attention on currency returns. Economic Modelling, 80, 49–61. https://doi.org/10.1016/j.econmod.2018.05.012
Zhang, C., Jiang, F., Wang, S., & Shang, W. (2020). A novel hybrid approach with a decomposition method and the RVFL model for crude oil price prediction. 2020 Proceedings IEEE International Conference on Big Data, Atlanta, GA, USA, 2020, 4446–4449. https://doi.org/10.1109/BigData50022.2020.9377826
Zhang, Y., Li, Y., & Shen, D. (2022). Investor attention and the carbon emission markets in China: A nonparametric wavelet-based causality test. Asia-Pacific Financial Markets, 29(1), 123–137. https://doi.org/10.1007/s10690-021-09348-2
Zhang, Y., & Tao, L. (2019). Haze, investor attention and China's stock markets: Evidence from internet stock forum. Finance Research Letters, 31, 363–368. https://doi.org/10.1016/j.frl.2018.12.001
Zupic, I., & Cater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629