The Impact of the Cryptocurrency Market on Islamic vs. Conventional Stock Returns: Evidence from Gulf Cooperation Council Countries
Abstract
:1. Introduction
Cryptocurrency Development and Stock Markets in the Gulf Cooperation Council (GCC) Region
2. Literature Review and Theoretical Framework
2.1. Literature Review
2.2. Theoretical Framework
2.3. Hypotheses Development
3. Data and Methodology
3.1. Sampling and Data Collection
3.2. Methods
4. Empirical Results and Discussion
4.1. Descriptive Statistics
4.2. Results
4.3. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
1 | https://fastcompanyme.com/news/mena-region-is-the-fastest-growing-crypto-market-in-the-world (accessed on 18 March 2024). |
2 | Chinalysis issues an annual crypto index, namely, “The Global Crypto Adoption Index”, which identifies the countries where the most people are putting the greatest share of their wealth into cryptocurrency. |
3 | The 2017–2018 cryptocurrency crash period represents the period in which the price of cryptocurrencies plunged in December 2017 (Tan et al. 2021). |
4 | It is important to note that it is not only Muslims who are interested in Islamic financial markets; non-Muslims are, too. For example, Alam et al. (2017) showed that “Islamic investment products offer an avenue for Muslims and non-Muslims to invest in ethically responsible funds with an underlying asset”. This implies that only Muslims may be reluctant to use cryptocurrencies; thus, their investment decisions may not be affected by movement in the cryptocurrency markets. In contrast, non-Muslim investors in Islamic financial assets may be affected if they find better investment opportunities in the cryptocurrency market. |
5 | The data were sourced from major stock indices in the GCC countries, as presented in Table 2; they were retrieved from www.investing.com (accessed on 25 September 2021). |
6 | To exclude the effects of the COVID-19 pandemic on the markets, the study looked at the problem by analyzing data until the end of 2019. |
7 | All the models’ coefficients were −0.009, with the exception of Model 3, where the coefficient was −0.008. Furthermore, the models were statistically significant at the 10% level, except for Model 3, which was non-significant. |
8 | “The GCC cryptocurrency market may exhibit characteristics similar to those of the equity market, such as volatility clustering, bubbles and inefficiency” (Abdeldayem and Aldulaimi 2023). |
9 | As mentioned above, cryptocurrencies have grown in popularity among investors as they offer more appealing returns compared to traditional investment instruments (Maitra et al. 2022; Bakar and Foziah 2023). |
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No. | Cryptocurrency Name | Symbol |
---|---|---|
1 | Bitcoin | BTC |
2 | Ethereum | ETH |
3 | Ripple | XRP |
4 | Stellar | XLM |
5 | Litecoin | LTC |
6 | Monero | XMR |
7 | Nem | XEM |
8 | Dash | DASH |
9 | Ethereum Classic | ETC |
10 | Maker | MKR |
No. | Country | Index Type | Index Name |
---|---|---|---|
1 | Bahrain | Islamic Conventional | Bahrain Islamic Index Bahrain All Share |
2 | Kuwait | Islamic Conventional | FTSE Lujain Kuwait Shariah FTSE Lujain Kuwait |
3 | Oman | Islamic Conventional | MSCI Oman Islamic MSCI Oman Index |
4 | Qatar | Islamic Conventional | QE Al Rayan Islamic QE General |
5 | UAE | Islamic Conventional | FTSE NASDAQ Dubai 15 Shariah Dubai Financial Market General Index |
Variable | Definition | Reference | Data Source | |
---|---|---|---|---|
Dependent variables: | ||||
1- | Islamic stock return | The daily return of the Islamic stock index of a country. | Swamy et al. (2019) Nomran and Haron (2021) | https://www.investing.com (accessed on 25 September 2021) |
2- | Conventional stock return | The daily return of the conventional stock index of a country. | ||
Independent variable: | ||||
3- | Cryptocurrency return | The average daily returns for the top ten cryptocurrencies expressed in logarithmic form, which is calculated as follows: the average for the closing price of each of the ten cryptocurrencies per day is calculated. Based on the average daily price, the average daily return is calculated as ln(Pt) − ln(Pt − 1). | Sami and Abdallah (2021) Palamalai et al. (2021) | Authors’ calculation with data from https://coinmarketcap.com (accessed on 25 September 2021) |
Control variables: | ||||
4- | Democratic accountability | An index that represents the quality of political institutions. The higher values show higher democratic accountability. | Ashraf (2020) Nomran and Haron (2021) | International Country Risk Guide Database |
5- | Investment freedom | An index that measures the level of freedom to invest in financial markets. Its values range from 0 to 100, where higher values represent higher investment freedom. | Ashraf (2020) Nomran and Haron (2021) | Heritage Foundation |
6- | GDP | The annual gross domestic product (GDP) of each country to measure the level of economic development, which is expressed in logarithmic form. | Ashraf (2020) Nomran and Haron (2021) | The World Economic Outlook database, International Monetary Fund website |
7- | Inflation | Inflation, as measured by the consumer price index. | Patil and Bagodi (2021) | https://www.theglobaleconomy.com (accessed on 22 June 2024) |
8- | Government spending | General government final consumption expenditure in billion USD. | Belo et al. (2013) | https://www.theglobaleconomy.com (accessed on 22 June 2024) |
9- | Political rights index | The political rights ratings from the Freedom House evaluate three categories: electoral process, political pluralism and participation, and the functioning of government. | Listyarti and Suryani (2014) Ahmed et al. (2022) | https://www.theglobaleconomy.com (accessed on 22 June 2024) |
10- | Population size | Total population, in millions, is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. | He et al. (2024) | https://www.theglobaleconomy.com (accessed on 22 June 2024) |
Variable | Observations | Mean | Std. Dev. | Minimum | Maximum | Skewness | Kurtosis |
---|---|---|---|---|---|---|---|
Panel (A): Initial full sample | |||||||
Islamic stock returns | 4358 | 0.0003 | 0.0106 | −0.0870 | 0.0902 | 0.4258 | 14.6183 |
Conventional stock returns | 4358 | 0.0002 | 0.0083 | −0.0727 | 0.0551 | −0.1951 | 10.4790 |
Cryptocurrency returns | 4358 | 0.0015 | 0.0410 | −0.2177 | 0.2040 | −0.2304 | 7.5327 |
Democratic accountability | 4358 | 22.9966 | 7.8533 | 12.0000 | 36.0000 | 0.2600 | 2.1565 |
Investment freedom | 4358 | 58.7586 | 11.5669 | 40.0000 | 75.0000 | −0.1813 | 1.9136 |
Log (GDP) | 4358 | 2.0535 | 0.3521 | 1.5100 | 2.6255 | 0.0376 | 1.9955 |
Inflation | 4358 | 1.2757 | 1.2578 | −1.9000 | 3.2000 | −0.5537 | 3.1267 |
Government spending | 4358 | 27.2099 | 14.3418 | 5.5200 | 51.2300 | 0.0796 | 2.1858 |
Political rights index | 4358 | 6.0999 | 0.7000 | 5.0000 | 7.0000 | −0.1398 | 2.0398 |
Population size | 4358 | 4.4109 | 2.5943 | 1.4100 | 9.2100 | 0.8347 | 2.5139 |
Panel (A1): Before crash period | |||||||
Islamic stock returns | 2136 | 0.0005 | 0.0120 | −0.0870 | 0.0902 | 0.5436 | 14.7792 |
Conventional stock returns | 2136 | 0.0000 | 0.0088 | −0.0727 | 0.0551 | −0.3707 | 11.2726 |
Cryptocurrency returns | 2136 | 0.0068 | 0.0376 | −0.2177 | 0.2040 | 0.1154 | 10.9015 |
Panel (A2): After crash period | |||||||
Islamic stock returns | 2222 | 0.0000 | 0.0090 | −0.0616 | 0.0586 | 0.0627 | 9.7963 |
Conventional stock returns | 2222 | 0.0003 | 0.0077 | −0.0585 | 0.0497 | 0.0708 | 8.7805 |
Cryptocurrency returns | 2222 | −0.0036 | 0.0434 | −0.1872 | 0.1515 | −0.3618 | 5.5129 |
Panel | A | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Dependent variable | Islamic stock returns | Conventional stock returns | ||||||||
Model | (1) | (2) | (3) | (4) | (5) | (1) | (2) | (3) | (4) | (5) |
OLS | OLS (Rob) | FE | RE | GLM | OLS | OLS (Rob) | FE | RE | GLM | |
Cryptocurrency returns | −0.008 ** (0.004) | −0.008 ** (0.004) | −0.008 ** (0.004) | −0.008 ** (0.004) | −0.008 ** (0.004) | −0.010 *** (0.003) | −0.010 *** (0.003) | −0.010 *** (0.003) | −0.010 *** (0.003) | −0.010 *** (0.003) |
Constant | 0.018 (0.012) | 0.018 (0.011) | 0.056 ** (0.028) | 0.018 (0.012) | 0.018 (0.012) | 0.020 ** (0.009) | 0.020 ** (0.009) | 0.024 (0.022) | 0.020 ** (0.009) | 0.020 ** (0.009) |
Country-level control variables | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Time-fixed effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Number of observations | 4358 | 4358 | 4358 | 4358 | 4358 | 4358 | 4358 | 4358 | 4358 | 4358 |
Number of countries | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
R-squared | 0.025 | 0.025 | 0.025 | 0.024 | 0.031 | 0.031 | 0.031 | 0.030 |
Panel | A1 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Dependent variable | Islamic stock returns | Conventional stock returns | ||||||||
Model | (1) | (2) | (3) | (4) | (5) | (1) | (2) | (3) | (4) | (5) |
OLS | OLS (Rob) | FE | RE | GLM | OLS | OLS (Rob) | FE | RE | GLM | |
Cryptocurrency returns | −0.009 (0.007) | −0.009 (0.006) | −0.009 (0.007) | −0.009 (0.007) | −0.009 (0.007) | −0.009 * (0.005) | −0.009 * (0.005) | −0.008 (0.005) | −0.009 * (0.005) | −0.009 * (0.005) |
Constant | 0.060 (0.079) | 0.060 (0.070) | 0.478 (0.443) | 0.060 (0.079) | 0.060 (0.079) | 0.006 (0.058) | 0.006 (0.058) | 0.576 (0.328) | 0.006 (0.058) | 0.006 (0.058) |
Country-level control variables | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Time-fixed effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Number of observations | 2136 | 2136 | 2136 | 2136 | 2136 | 2136 | 2136 | 2136 | 2136 | 2136 |
Number of countries | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
R-squared | 0.045 | 0.045 | 0.043 | 0.042 | 0.061 | 0.061 | 0.062 | 0.060 |
Panel | A2 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Dependent variable | Islamic stock returns | Conventional stock returns | ||||||||
Model | (1) | (2) | (3) | (4) | (5) | (1) | (2) | (3) | (4) | (5) |
OLS | OLS (Rob) | FE | RE | GLM | OLS | OLS (Rob) | FE | RE | GLM | |
Cryptocurrency returns | −0.006 (0.004) | −0.006 (0.004) | −0.006 (0.004) | −0.006 (0.004) | −0.006 (0.004) | −0.009 ** (0.004) | −0.009 ** (0.004) | −0.009 ** (0.004) | −0.009 ** (0.004) | −0.009 ** (0.004) |
Constant | 0.030 (0.042) | 0.030 (0.027) | 0.015 (0.145) | 0.030 (0.042) | 0.030 (0.042) | 0.065 * (0.035) | 0.065 ** (0.027) | −0.013 (0.125) | 0.065 * (0.035) | 0.065 * (0.035) |
Country-level control variables | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Time-fixed effects | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Number of observations | 2222 | 2222 | 2222 | 2222 | 2222 | 2222 | 2222 | 2222 | 2222 | 2222 |
Number of countries | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 |
R-squared | 0.053 | 0.053 | 0.049 | 0.047 | 0.057 | 0.057 | 0.056 | 0.054 |
Dependent Variable | Islamic Stock Returns | Conventional Stock Returns | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Model | (1) | (2) | (3) | (4) | (5) | (1) | (2) | (3) | (4) | (5) |
OLS | OLS (Rob) | FE | RE | GLM | OLS | OLS (Rob) | FE | RE | GLM | |
Panel (A): Initial full sample | −0.008 ** (0.004) | −0.008 ** (0.004) | −0.008 ** (0.004) | −0.008 ** (0.004) | −0.008 ** (0.004) | −0.010 *** (0.003) | −0.010 *** (0.003) | −0.010 *** (0.003) | −0.010 *** (0.003) | −0.010 *** (0.003) |
Panel (A1): Before crash period | −0.009 (0.007) | −0.009 (0.006) | −0.009 (0.007) | −0.009 (0.007) | −0.009 (0.007) | −0.009 * (0.005) | −0.009 * (0.005) | −0.008 (0.005) | −0.009 * (0.005) | −0.009 * (0.005) |
Panel (A2): After crash period | −0.006 (0.004) | −0.006 (0.004) | −0.006 (0.004) | −0.006 (0.004) | −0.006 (0.004) | −0.009 ** (0.004) | −0.009 ** (0.004) | −0.009 ** (0.004) | −0.009 ** (0.004) | −0.009 ** (0.004) |
Sample Period | Islamic Stock Returns | Conventional Stock Returns |
---|---|---|
Panel (A): Initial full sample (2016–2019) | Negative and significant | Negative and significant |
Panel (A1): Before crash period (2016–2017) | Not significant | Negative and significant |
Panel (A2): After crash period (2018–2019) | Not significant | Negative and significant |
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Mansour Nomran, N.; Laallam, A.; Haron, R.; Kashi, A.; Hossen Shaikh, Z.; Abey, J. The Impact of the Cryptocurrency Market on Islamic vs. Conventional Stock Returns: Evidence from Gulf Cooperation Council Countries. J. Risk Financial Manag. 2024, 17, 305. https://doi.org/10.3390/jrfm17070305
Mansour Nomran N, Laallam A, Haron R, Kashi A, Hossen Shaikh Z, Abey J. The Impact of the Cryptocurrency Market on Islamic vs. Conventional Stock Returns: Evidence from Gulf Cooperation Council Countries. Journal of Risk and Financial Management. 2024; 17(7):305. https://doi.org/10.3390/jrfm17070305
Chicago/Turabian StyleMansour Nomran, Naji, Abdelkader Laallam, Razali Haron, Aghilasse Kashi, Zakir Hossen Shaikh, and Joji Abey. 2024. "The Impact of the Cryptocurrency Market on Islamic vs. Conventional Stock Returns: Evidence from Gulf Cooperation Council Countries" Journal of Risk and Financial Management 17, no. 7: 305. https://doi.org/10.3390/jrfm17070305