From GARCH to Artificial Intelligence: Bibliometric and Thematic Review (1995-2025)
Keywords:
Stock Market Volatility; Bibliometric Analysis; Investor Sentiment; GARCH Modeling, Financial Integration; Artificial Intelligence; VosViewer
Abstract
This bibliometric study focuses on the changing nature of factors affecting the stock market volatility between the year 1995-2025. Using a data source of 89 reviewed research articles in Scopus and analyzed using VOSviewer through visualizations. The analysis also reveals the main clusters of influence, which include the macroeconomic indicators, political stability, and oil-price shocks, as well as behavioural drivers, including investor sentiment. The results show that the previous papers focused on the GARCH-based modeling and seasonality, whereas recent literature predicts the incorporation of artificial intelligence, deep learning methods, and ESG-associated volatility spill-overs. Bibliographic coupling highlights the fact that network theory is becoming more and more relevant to understand spatial correlations and dynamics of regional contagion. The study reveals that volatility is no longer necessarily understood as an economic variable but instead as a dynamic of complicated world systems. These results serve vital information to policy makers who are working hard to reduce systemic risk as well as retail investors who deal with information asymmetry or imbalance. Future studies should address the gap of methodological fragmentation by combining explainable artificial intelligence with theory-consistent financial models.
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Sanderson, A., Henry, M.-C., Mokumako, T., Mukarati, J., & Le Roux, P. (2024). Determinants of Share Prices of Agriculture Listed Firms. International Journal of Economics and Financial Issues, 14(4), 106–110. https://doi.org/10.32479/ijefi.16062
Tjandrasa, B. B., Siagian, H., & Jie, F. (2020). The macroeconomic factors affecting government bond yield in Indonesia, Malaysia, Thailand, and the Philippines. Investment Management & Financial Innovations, 17(3), 111–121. https://doi.org/10.21511/IMFI.17(3).2020.09
Hawaldar, I. T., & Rahiman, H. U. (2019). Investors Perception Towards Stock Market: An Exploratory Approach. Social Science Research Network. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3512560
Alexakis, P., & Xanthakis, M. (1995). Day of the week effect on the Greek stock market. Applied Financial Economics, 5(1), 43–50. https://doi.org/10.1080/758527670
Hassan, M. K. (2003). Portfolio investment of the OIC countries and their implications on trade. Managerial Finance, 29(2/3), 122–157. https://doi.org/10.1108/03074350310768715
Lin, C., & Cheng, W. (2008). Economic determinants of comovement across international stock markets: the example of Taiwan and its key trading partners. Applied Economics, 40(9), 1187–1205. https://doi.org/10.1080/00036840600771262
Richie, N., Daigler, R. T., & Gleason, K. C. (2008). The limits to stock index arbitrage: Examining S&P 500 futures and SPDRS. Journal of Futures Markets, 28(12), 1182–1205. https://doi.org/10.1002/fut.20365
Barrell, R., Holland, D., Liadze, I., & Pomerantz, O. (2008). Volatility, growth and cycles. Empirica, 36(2), 177–192. https://doi.org/10.1007/s10663-008-9080-5
Shih, K., & Fan, K. (2009). Analyzing financing strategy of public manufacturing companies. Industrial Management & Data Systems, 109(6), 775–792. https://doi.org/10.1108/02635570910968036
Tanjung, H., Siregar, H., Sembel, R., & Nurmalina, R. (2014). Factors Affecting the Volatility of the Jakarta Composite Index before and after the Merger of Two Stock and Bond Markets in Indonesia. Asian Social Science, 10(22). https://doi.org/10.5539/ass.v10n22p91
Apergis, N. (2015). Policy risks, technological risks and stock returns: New evidence from the US stock market. Economic Modelling, 51, 359–365. https://doi.org/10.1016/j.econmod.2015.08.021
Xb, S., & Zheng, W. (2015). Ownership Structure, Stock Volatility and Analyst Independence: Evidence from China. SSRN Electronic Journal. https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2620572_code493396.pdf?abstractid=2620572&mirid=1
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Li, Y., Zhuang, X., Wang, J., & Dong, Z. (2020). Spatial linkage of volatility spillovers and its explanation across China’s interregional stock markets: a network approach. Applied Economics Letters, 28(8), 668–674. https://doi.org/10.1080/13504851.2020.1770676
Li, Y., Zhuang, X., Wang, J., & Zhang, W. (2020). Analysis of the impact of Sino-US trade friction on China’s stock market based on complex networks. The North American Journal of Economics and Finance, 52, 101185. https://doi.org/10.1016/j.najef.2020.101185
Wu, B. (2020). Investor behavior and risk contagion in an Information-Based Artificial stock market. IEEE Access, 8, 126725–126732. https://doi.org/10.1109/access.2020.3008717
Hawaldar, I. T., & Rahiman, H. U. (2019). Investors Perception towards Stock Market: An Exploratory approach. SSRN Electronic Journal. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3512560
Al-Rimawi, M. A., & Kaddumi, T. A. (2021). Factors affecting stock market index volatility: Empirical study. Journal of Governance and Regulation, 10(3), 169–176. https://doi.org/10.22495/jgrv10i3art15
Mukan, M., Oskenbayev, Y., Naderi, N., & Dosmagambet, Y. (2020). Oil Price Fluctuations and Stock Market Behavior in a Resource-dominant Economy: Case of Kazakhstan. The Singapore Economic Review, 66(02), 569–594. https://doi.org/10.1142/s0217590820430055
Ziadat, S. A., & McMillan, D. G. (2022). Oil-stock nexus: the role of oil shocks for GCC markets. Studies in Economics and Finance, 39(5), 801–818. https://doi.org/10.1108/sef-12-2021-0529
Dalіak, N., Naumenko, V., Lozynska, T., Busarieva, T., Kazak, O., & Tolkachova, H. (2023). Econometric Assessment of the Effieciency and Volatility of the Stock Market in Ukraine. Financial and Credit Activity Problems of Theory and Practice, 5(52), 150–161. https://doi.org/10.55643/fcaptp.5.52.2023.4110
Sreenu, N., & Pradhan, A. K. (2022). The effect of COVID-19 on Indian stock market volatility: can economic package control the uncertainty? Journal of Facilities Management, 21(5), 798–815. https://doi.org/10.1108/jfm-12-2021-0162
Mamipour, S., Yazdani, S., & Sepehri, E. (2022). Examining the spillover effects of volatile oil prices on Iran’s stock market using wavelet-based multivariate GARCH model. Journal of Economics and Finance, 46(4), 785–801. https://doi.org/10.1007/s12197-022-09587-7
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Published
2026-04-27
How to Cite
Arora, V. (2026). From GARCH to Artificial Intelligence: Bibliometric and Thematic Review (1995-2025). International Journal of Social Science Research and Review, 9(5), 166-184. https://doi.org/10.47814/ijssrr.v9i5.3384
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