Time-Varying Causality between Equity Investor Sentiment and Sukuk Returns

Document Type : Original Article

Authors

1 Assistant Prof., Department of Accounting, University of Qom, Qom, Iran.

2 Ph.D. in Strategic Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.

Abstract

Investor's sentiment is one of the most important issues in behavioral finance that plays an important role in financial markets. This paper has been investigated the dynamic causality between investor sentiment in the stock market and OTC sukuk returns in the period of 2013-2023 with monthly frequency using bootstrap rolling-window causality approach. Sukuk return is measured based on sukuk index, non govermental bonds sukuk index and govermental bonds sukuk index. The findings show that there was a causality from investor sentiment to the sukuk market (based on all three criteria) and vice versa. Investor sentiment has had a positive effect on the sukuk index in most of the period. On the other hand, the sukuk index has generally had a positive effect on investor sentiment. The effect of investor sentiment on the index of non-government bonds was negative until the end of 2015, and after that, the effect was positive and its absolute value decreased. The effect sign in the opposite case was also similar. Investor sentiment had a negative effect on the government bond index until the middle of 2018, and after that it had a positive effect. The government bond index has generally had a negative impact on investor sentiment. The findings confirm the effect of both capital flow and contagion hypotheses, with opposite predictions in explaining the relationship between the variables. In addition, the difference in the sign of the effect of the variables on each other during the research period, emphasizes the use of dynamic approaches.

Keywords


  1. Allen, D., Amram, R., & McAleer, M. (2011). Volatility spillovers from the Chinese stock market to economic neighbors. Mathematics and Computers in Simulation, 94, 238-257.
  2. Bauwens, L., Laurent, S., & Rombouts, J. V. (2006). Multivariate GARCH models: a survey. Journal of applied econometrics, 21(1), 79-109.
  3. Bollerslev, T., Engle, R. F., & Wooldridge, J. M. (1988). A capital asset pricing model with time-varying covariances. Journal of political Economy, 96(1), 116-131
  4. Cappiello, L., Engle, R., & Sheppard, K. (2006). Asymmetric dynamics in the correlations of global equity and bond returns. Journal of Financial Econometrics, 4, 537–572.
  5. Einabadi, J., & moradi, N. (2021). The effects of exchange rate appreciation on the stock value of pharmaceutical companies based on the estimated value obtained from the evaluation models of cash dividend discount, free cash flow and residual profit and real price. Journal of Business Management,13(52), 467-485. (In Persian)
  6. Engle, R. (2002). Dynamic conditional correlation: A simple class of multivariate generalized Auttoregressive conditional heteroskedasticity models. Journal of Business & Economic Statistics, 20(3), 339-350.
  7. Engle, R.F., Sheppard, K., 2001. Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH. Working Paper. University of California, San Diego.
  8. Hamao, Y., Masulis, R. W., & Ng, V. (1990). Correlations in price changes and volatility across international stock markets. The review of financial studies, 3(2), 281-307.
  9. Heidari, H., Mohammadzadeh, Y., & Refah-Kahriz, A. (2018). An Investigation of the Effect of Exchange Rate on the Pharmaceutical Industry Stock Return in Tehran Stock Exchange: An Application of the Markov Switching Approach. Journal of Asset Management and Financing, 6(2), 35-56. doi: 10.22108/amf.2017.21420(In Persian)
  10. Hou, Y. G., & Li, S. (2020). Volatility and skewness spillover between stock index and stock index futures markets during a crash period: New evidence from China. International Review of Economics & Finance, 66, 166-188.
  11. Koutmos, G., & Spillover Effect On Different industries For Capital MarketBooth, G. G. (1995). Asymmetric volatility transmission in international stock markets. Journal of international Money and Finance, 14(6), 747-762.
  12. Shams Safa, F., Daman keshideh, M., Afsharirad, M., HadiNejad, M., & Daghighi Asl, A. (2022). The Effects of Exchange Rate Volatility and Entry of Real Shareholders on the Return on Assets in the Food and Drink Companies of Tehran Stock Exchange (Dynamic Panel Data Approach). Financial Management Perspective, 12(39), 121-145. doi: 10.52547/JFMP.12.39.121(In Persian)
  13. Shokri, N., Sahab Khodamoradi, M., & Hajiloo moghadam, A. H. (2021). Investigating the effects of financial volatility spillover between digital currencies (application of multivariate GARCH approach). Financial Management Perspective, 11(35), 143-172. doi: 10.52547/jfmp.11.35.143(In Persian(
  14. Y, Jiang, F, Yuyuan, R, Weihuan. (2019). Risk Spillover and Portfolio management between precious metal and BRICS stock markets. Physica A (534)
  15. Yadav, N., Singh, A. B., & Tandon, P. (2023). Volatility Spillover Effects between Indian Stock Market and Global Stock Markets: A DCC-GARCH Model. FIIB Business Review.