Author

  • Nasha Shafridah Binti Abdullah

    • Faculty of Business and Management, UiTM Shah Alam, Malaysia
    • Malaysia
  • Siti Nurhidayah Mohd Roslen

    • Faculty of Business and Management, UiTM Cawangan Selangor, Puncak Alam
    • Malaysia
  • Nur Zahidah Binti Bahrudin

    • Faculty of Business and Management, UiTM Cawangan Selangor, Puncak Alam
    • Malaysia
  • Pang Mary

    • School of Accounting and Finance, Nilai University, Nilai, Negeri Sembilan, Malaysia
    • Malaysia
  • Siti Nur Nadhirah Binti Mohd Roslen

    • Corporate and Institutional Business Group, Hong Leong Investment Bank Berhad, Kuala Lumpur, Malaysia
    • Malaysia

Abstract

Market sentiment has been found to be one of the key determinants of stock market volatility, especially in globally interconnected economies such as Malaysia. With the growing influence of retail investors operating in advanced digital trading environments, understanding the role of sentiment in shaping market behavior has become more critical. Despite existing regulatory frameworks designed to protect investors from manipulation and misconduct, current surveillance practices still rely on traditional indicators such as price and trading volume. These indicators may not fully capture how the market works in a real investment setting. This study addresses a critical gap by empirically examining the effects of market sentiment (measured through the Consumer Sentiment Index) along with other macroeconomic variables on stock return and its volatility in Malaysia. Using the GARCH-X Model on stock return data from January 1990 until January 2025, we find that CSI has a positive and significant relationship with stock returns, indicating that Malaysia’s stock market returns were more strongly influenced by investor confidence than by geopolitical events and monetary policy factors. In addition, the results from the GARCH-X model show that the stock market does not always move in line with the macroeconomic fundamentals in the short term. Additionally, the model suggests that in the short term, stock returns become more volatile during periods of market instability, with volatility amplifying when new shocks emerge. However, in the long run, the market tends to adjust and return to equilibrium. These findings underscore the need for regulatory authorities to enhance real-time volatility monitoring tools and promote better access to structured, legal, and accessible data sharing in ensuring effective market responses during periods of crisis or adverse global events.

Keywords

References

  • Abdelmalek, W. (2022). Investor sentiment, realized volatility and stock returns. Review of Behavioral Finance, 14(5), 668–700.
  • Aggarwal, V., Doifode, A., & Tiwary, M. K. (2022). Do lower foreign flows and higher domestic flows reduce indian equity market volatility? Vision, 26(4), 461–470.
  • Apergis, N., & Rezitis, A. N. (2011). Food price volatility and macroeconomic factors: evidence from GARCH and GARCH-X estimates. Journal of Agricultural and Applied Economics, 43(1), 95–110.
  • Batala, L. K., Yu, W., Khan, A., Regmi, K., & Wang, X. (2021). Natural disasters' influence on industrial growth, foreign direct investment, and export performance in the South Asian region of Belt and road initiative. Natural Hazards, 108(2), 1853-1876.
  • Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
  • Bursa Malaysia Berhad. (2025). Market Surveillance Overview. Retrieved June 29, 2025, from https://www.bursamalaysia.com/regulation/about_bursa_malaysia_regulatory/market_surveillance/overview
  • Caldara, D., & Iacoviello, M. (2022). Measuring geopolitical risk. American Economic Review, 112(4), 1194–1225.
  • Deveikyte, J., Geman, H., Piccari, C., & Provetti, A. (2022). A sentiment analysis approach to the prediction of market volatility. Frontiers in Artificial Intelligence, 5, 836809.
  • Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: journal of the Econometric Society, 1057-1072.
  • Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the econometric society, 987-1007.
  • Engle, R. F., & Bollerslev, T. (1986). Modelling the persistence of conditional variances. Econometric reviews, 5(1), 1-50.
  • Ghulam, S., Mubashar, A., Bashir, M., & Hussain, S. (2025). Foreign Direct Investment and Stock Market Development: Evidence from Asia. Journal of Social Sciences Review, 5(1), 1-10.
  • Hsu, Y. J., Lu, Y. C., & Yang, J. J. (2021). News sentiment and stock market volatility. Review of Quantitative Finance and Accounting, 57(3), 1093-1122.
  • Kim, T., & Ha, A. (2010, August). Investor sentiment and market anomalies. In 23rd Australasian Finance and Banking Conference.
  • Kong, E. Y. F., & Tang, K. B. (2024). Are Malaysian IPO Investors Influenced by Sentiment Factors or Fundamental Factors? Capital Markets Review, 32(1), 29-58.
  • Lai, F., Li, S., Lv, L., & Zhu, S. (2023). Do global geopolitical risks affect connectedness of global stock market contagion network? Evidence from quantile-on-quantile regression. Frontiers in Physics, 11, 1124092.
  • Lamine, A., & Zribi, S. (2024). Do geopolitical risks affect stock market returns and volatilities: an analysis based on the TVP-VAR model. European Journal of Government and Economics, 13(2), 240-261.
  • Lamoureux, C. G., & Lastrapes, W. D. (1990). Persistence in variance, structural change, and the GARCH model. Journal of Business & Economic Statistics, 8(2), 225-234.
  • Li, Y., Shi, Y., Shi, Y., Xiong, X., & Yi, S. (2024). Time-frequency extreme risk spillovers between COVID-19 news-based panic sentiment and stock market volatility in the multi-layer network: Evidence from the RCEP countries. International Review of Financial Analysis, 94, 103339.
  • Loang, O. K., Candra, S., & Naveenan, R. V. (2024). Stability in chaos: Impact of monetary, fiscal, and firm characteristics on investor sentiment in Asian emerging markets. International Journal of Business and Society, 25(3), 1145-1164.
  • Luong, A. T. (2025). How does the asymmetric spillover of investor sentiment respond to economic policy uncertainty in G7 and BRICS countries? Review of Behavioral Finance, 17(2), 317-341.
  • Mohnot, R., Banerjee, A., Ballaj, H., & Sarker, T. (2024). Re-examining asymmetric dynamics in the relationship between macroeconomic variables and stock market indices: empirical evidence from Malaysia. The Journal of Risk Finance, 25(1), 19-34.
  • New Straits Times. (2025, May). Impact of FOMO on stock investing. New Straits Times. Retrieved June 29, 2025, from https://www.nst.com.my/business/insight/2025/05/1209894/impact-fomo-stock-investing
  • Pan, C., Zhang, W., & Wang, W. (2023). Global geopolitical risk and volatility connectedness among China's sectoral stock markets. Finance Research Letters, 58, 104487.
  • Paramanik, R. N., & Singhal, V. (2020). Sentiment analysis of Indian stock market volatility. Procedia Computer Science, 176, 330-338.
  • Ph, H., & Rishad, A. (2020). An empirical examination of investor sentiment and stock market volatility: evidence from India. Financial Innovation, 6(1), 34.
  • Ruan, Q., Yang, H., Lv, D., & Zhang, S. (2018). Cross-correlations between individual investor sentiment and Chinese stock market return: New perspective based on MF-DCCA. Physica A: Statistical Mechanics and its Applications, 503, 243-256.
  • Securities Commission Malaysia. (2025, June). Equity Market Statistics. https://www.sc.com.my/api/documentms/download.ashx?id=8ebbc40c-8ce6-479f-b8d6-e6f1afdb23e5
  • Staikouras, S. K. (2006). Testing the stabilization hypothesis in the UK short-term interest rates: Evidence from a GARCH-X model. The Quarterly Review of Economics and Finance, 46(2), 169-189.
  • Sun, C., Yi, S., Wu, F., Li, Y., & Dong, Z. (2025). Mainshocks and aftershocks: Assessing the resilience of Asia-Pacific stock markets amid global financial cycle shocks. Pacific-Basin Finance Journal, 91, 102720.
  • Wang, J., Zhang, L., & Sun, Y. (2025). Geopolitical risk and corporate environmental investment. Applied Economics, 1-15.
  • Xiao, J., Jiang, J., & Zhang, Y. (2024). Policy uncertainty, investor sentiment, and good and bad volatilities in the stock market: Evidence from China. Pacific-Basin Finance Journal, 84, 102303.
  • Xie, D., Cui, Y., & Liu, Y. (2023). How does investor sentiment impact stock volatility? New evidence from Shanghai A-shares market. China Finance Review International, 13(1), 102-120.

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How to Cite

Binti Abdullah, N. S., Mohd Roslen, S. N., Binti Bahrudin, N. Z., Mary, P., & Binti Mohd Roslen, S. N. N. (2025). Market Sentiment and Stock Return Volatility in Malaysia: Evidence using the GARCH-X Model. Information Management and Business Review, 17(3(I), 394-402. https://doi.org/10.22610/imbr.v17i3(I).4667

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Research Paper

Published

2025-09-13

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