This paper aims to investigate the effect of cryptocurrency on the Egyptian stock market from November 2021 to January 2024 daily and to determine if cryptocurrencies function as complements or substitutes for domestic equities in a constrained emerging economy.
An ARIMA model analysis was employed to test the hypotheses using logarithmic returns for EGX30 as the dependent variable. The model incorporates four cryptocurrency proxies (Bitcoin, Binance, Ethereum and Neo) and is controlled for sovereign risk using macroeconomic variables, specifically the daily interbank rate and the EGP/USD exchange rate.
The results revealed a significant negative effect of cryptocurrency trading on the performance of the Egyptian stock market. There is a significant and negative effect of NEOR on the Egyptian stock market performance, indicating a liquidity substitution effect. These results support the risk-return trade-off theory, since the findings indicated that investors prefer reducing their investments in traditional stocks, believing that investments in cryptocurrencies would provide them with higher returns, particularly during the pandemic when virtual cryptocurrencies served as a possible shield against economic instability.
The findings have practical implications for scholars, governments, investors and portfolio managers who aim to understand the relationship between cryptocurrency and stock market performance to develop competitive investment strategies within a legislative framework. Hence, Egyptian policymakers should monitor cryptocurrency movements to mitigate negative impacts on stock market stability through enhancing the attractiveness of regulated domestic investment alternatives to counter the speculative pull of unregulated digital assets. Moreover, investors need to diversify their portfolios by integrating cryptocurrencies to reduce stock market volatility.
This paper extends the literature by providing a comprehensive examination of the effect of the cryptocurrency market on the performance of the Egyptian stock market, during a period of acute domestic policy shifts and currency devaluation (2022–2023). It offers a nuanced refinement to modern portfolio theory (MPT) by demonstrating how institutional constraints and sovereign risk drive capital flight towards speculative altcoins.
1. Introduction
Cryptocurrencies are virtual currencies secured by encryption and founded on blockchain technology (Ratha, 2022). Cryptocurrencies, notably Bitcoin, introduced in 2009, have become increasingly popular, especially in poorer nations where consumers lack full access to traditional banking systems (Nakamoto, 2009; Demirgüç-Kunt et al., 2015). Blandin et al. (2020) emphasised that the widespread adoption of cryptocurrencies in Africa is attributed to their widely accepted utilisation for money transfer as a buffer against currency devaluation. In Egypt, this trend is underscored by the country’s emergence as the Arab world’s largest population of cryptocurrency users, exceeding 1.7 million individuals (CNN, 2022). This occurs despite a stringent legal environment in which the Central Bank of Egypt (CBE) prohibits trading, viewing it as a threat to national security.
The utilisation of cryptocurrencies offers several potential advantages to emerging economies, including foreign remittances, enhanced access to capital and investment prospects (Gomber et al., 2018; Schär, 2021). The World Bank Global Findex Database (World Bank, 2017) indicates that cross-border remittances including digital currencies may be less expensive for developing nations.
Modern portfolio theory (MPT) emphasises that selecting a financial instrument for an investment portfolio must not be determined just by the attributes of a single investment (Markowitz, 1952). Therefore, the MPT supports the integration of cryptocurrencies in investors’ investment portfolios as an effective way to mitigate risk and attain diversification advantages in both short- and long-term investing horizons (Ali et al., 2024a; Krishna et al., 2023). In contrast, the risk-return trade-off theory of finance posits that increased volatility is accompanied by higher anticipated profits. Therefore, investors might decide to decrease their investment in conventional stocks if they believe that investments in cryptocurrencies offer superior returns or diversification advantages. This can result in a negative association between cryptocurrencies and the stock market return (Iqbal et al., 2023; Kurka, 2019), causing an increase in prices in crypto markets and a decrease in stock prices as investors shift their capital into alternative investments such as cryptocurrencies.
The integration of the cryptocurrency market is a developing and dynamic area within the financial industry. The cryptocurrency market has been viewed as an important digital financial technology breakthrough that enables transactions and serves as an essential tool to facilitate exchange (Fröwis and Böhme, 2017). This artificially created currency has established a distinct position in global capital markets, particularly due to its rapid growth and spread (Aharon et al., 2025; Saksonova and Kuzmina-Merlino, 2019).
Corbet (2020) provided a comprehensive classification of cryptocurrencies into three distinct categories; first, currency cryptocurrencies, which primarily serve as a means of making monetary payments, currency-related transactions, or as a store of financial value such as Bitcoin and Binance; second, protocol cryptocurrencies, which primarily operate as a blockchain protocol or platform such as Ethereum; and third, decentralised application (dApps) cryptocurrencies, which are utilised in third-party applications developed on an existing blockchain, with primary purposes other than serving as currency such as Neo. The market capitalisation of BIT currency is the largest at $1,158,854,141,096, followed by ETH at $79,841,303,787, BNB at $275,789,297,400, and NEO at $658,288,677 (Statista, 2024).
Despite extensive empirical research on cryptocurrency-stock market linkages in developed economies and a few major emerging markets (e.g. China, India), the literature is notably silent on highly constrained emerging economies characterised by volatile exchange rate regimes and regulatory ambiguity. Studies of developed markets, which assume robust capital controls and well-integrated financial systems, may fail to capture the unique dynamic where cryptocurrencies serve not just as an alternative investment class, but as a critical financial escape valve or hedge against sovereign risk (Bouri et al., 2022; Al Mamun et al., 2020; Sayed and Abbas, 2018). Existing studies on the MENA region often aggregate diverse markets, failing to account for the specific pressures of the Egyptian context, such as acute currency devaluation and the managed float regime. The current study addresses this research gap by isolating the “liquidity substitution” mechanism – where capital flees domestic equities for decentralised assets – during a period of intense economic reform.
Based on the above discussion, the motivation of this paper arises from the distinctive characteristics of an emerging country as Egypt; where conventional financial systems frequently face challenges that result from the growing prominence of digital currencies. This paper seeks to examine the impact of cryptocurrency volatility on stock market indices, especially within a developing context where financial inclusion and economic empowerment are essential. Precisely, Egypt is categorised as a developing country, facing challenges such as restricted financial inclusion and economic instability, especially with the shift from a fixed to a floating currency rate, which considerably affects stock prices (Omran, 2003; Mahmoud et al., 2024). Consequently, the adoption of cryptocurrencies may provide solutions to these challenges, thereby improving financial access and economic empowerment (Hajj and Farran, 2024).
The association between the cryptocurrency market and stock market performance has been an area of extensive scholarly debate. Although previous empirical studies have illustrated the potential advantages of diversifying stock portfolios with cryptocurrencies (Baur et al., 2018; Brière et al., 2015; Corbet et al., 2019; Feng et al., 2018; Kajtazi and Moro, 2019; Bae et al., 2020), an increasing amount of evidence (Baek and Elbeck, 2015; Chan et al., 2017; Iqbal et al., 2023) suggested that including cryptocurrency investing portfolios may potentially increase instead of reducing the total market risk. A possible reason for this negative relationship is the inherent volatility of cryptocurrencies (Ali et al., 2024b, c). Virtual currencies are distinguished by their unpredictable price fluctuations, frequently influenced by speculative trading, uncertainty in regulations and advancements in technology (Cheah and Fry, 2015; Phillips and Gorse, 2018; Wang and Vergne, 2017).
In the light of the above discussion, this paper aims to investigate the degree to which the cryptocurrency market is absorbing capital from the stock market. It evaluates whether cryptocurrencies function as complements or substitutes to the stock market in Egypt. Derived from the risk-return trade-off theory and the context of Egypt’s constrained financial environment. Hence, this paper would answer the following precise question:
Does the return volatility of cryptocurrencies (Bitcoin, Binance, Ethereum and Neo) significantly and negatively affect the performance of the Egyptian stock market (EGX30) during the post-pandemic devaluation period?
This paper has several contributions. First, it expands the current literature by addressing the identified gap. This paper initially employs a singular methodology to provide a comprehensive examination of the effect of the cryptocurrency market on the performance of the Egyptian stock market since the findings of previous studies executed in the Middle East and North Africa (MENA) region (Sami and Abdallah, 2021; Mensia et al., 2019) cannot be generalised. Specifically, we introduce and test the hypothesis that in emerging markets facing acute currency devaluation and capital control risk, high-volatility, globally-accessible cryptocurrencies (such as Neo) function as a stronger substitute for domestic equities than in less-constrained economies. This offers a nuanced refinement to MPT’s application in emerging markets. Second, it adds to the limited empirical investigations that have explored the association between the cryptocurrency market and Egyptian stock market performance. To the authors’ best knowledge, the only study examining the influence of the relative riskiness of cryptocurrencies on stock market indices in Egypt is that conducted by Eldomiaty and Mohab (2024). Third, while earlier studies employed comprehensive sample analysis, this paper differs by concentrating on the crucial days from 1st November 2021 to 1st January 2024. These days represent duration of economic subsequent to the recovery from the COVID-19 epidemic and more critically, encompass major domestic policy shifts, including the substantial currency devaluations of 2022–2023, which fundamentally altered the risk-return calculus for Egyptian investors. Fourth, this paper examined the association between specific cryptocurrency proxies like Bitcoin, Binance, Ethereum and Neo daily return which represents the most trending cryptocurrencies worldwide. Fifth, this paper included macroeconomic variables (daily interbank and daily exchange rate Egypt pound/dollar) in the econometric model to capture the impact of the cryptocurrency market on Egyptian stock market performance. The inclusion of the volatile exchange rate alongside cryptocurrency returns allows us to effectively isolate the effects of sovereign risk hedging vs pure diversification, a critical methodological advancement for emerging market finance research (Stojanovikj and Petrevski, 2021). Eventually, this investigation demonstrates that the attributes of the cryptocurrency market have a crucial role in influencing the success of the Egyptian stock market. The major finding is that cryptocurrency is regarded as a significant alternative to stocks in Egypt based on the significant adverse impact of NEOR on EGX30. This finding offers novel, time-sensitive evidence for policymakers regarding the unintended consequences of capital constraints on domestic market liquidity.
The rest of this paper is outlined as follows: Section 2 presents a literature review and the hypothesis development, while Section 3 illustrates the research design. Section 4 shows the data analysis and the discussion of results. Section 5 is the conclusion, practical implications, limitations and suggestions for further research.
2. Literature review and hypotheses development
2.1 The cryptocurrency market in Egypt
The cryptocurrency market in Egypt is distinguished by its secretive nature, mostly because of Egypt’s legal position that opposes its trade and exchange. Given its perception as a threat to national security, it is deemed illegal and forbidden. Prior arrests have been made for those engaged in cryptocurrency mining. Despite the official prohibition of virtual currencies and cryptocurrency transactions, an unofficial cryptocurrency market continues to expand, especially on social platforms, which can, in some cases, be likened to the black market for the US dollar prior to the devalued status of the Egyptian Pound. The CBE announcements indicate an increasing trend of bitcoin usage in Egypt (Diaa, 2018). In the time from 2019 to 2020, Local Bitcoins, a cryptocurrency trading platform, witnessed a 100% increase in new user registrations. Furthermore, it has been reported that the cryptocurrency traders in Egypt are individuals from the millennial younger generation who seek other sources of income (Sanadali, 2021). Egypt possesses the largest population of cryptocurrencies users in the Arab world, exceeding 1.7 million individuals. Morocco ranked second after Egypt with 878.1 thousand users, followed by Saudi Arabia with 452.7 thousand users, Iraq with 375.7 thousand users and Yemen with 278.3 thousand users (CNN, 2022). On an international level, India is ranked the first globally, displaying over 100 million users. America ranked second with over 27 million participants, followed by Russia with 17 million, Nigeria with 13 million and Brazil with over 10 million. Furthermore, in 2019, CBE announced a proposed initiative to introduce the nation’s digital currency. However, this initiative remained unfulfilled until the present moment (Galal, 2022). The context of Egypt’s managed exchange rate regime and recent sharp devaluations amplifies the potential role of cryptocurrencies not merely as speculative assets, but as a critical, unregulated conduit for capital seeking stable value outside the domestic financial system. This institutional factor is central to our hypothesis on cryptocurrency substitutability for local equities.
2.2 Theoretical review
The theoretical grounding of this study is rooted in the Efficient Market Hypothesis (EMH), which holds that asset prices reflect all available information. However, in emerging markets, this is often challenged by the Substitution Effect and Safe-Haven behaviour. The history of financial investing commenced with classical transactions involving stocks and/or bonds in conventional financial markets. This was succeeded by the incorporation of financial derivatives, including securities futures and option contracts, mostly inside the financial systems of developed countries. In the 2000s, the primary objective of individual investors and portfolio fund managers was to create innovative financial instruments that optimise returns while maintaining acceptable levels of risk. This stimulated the creation and swift proliferation of cryptocurrencies in 2008 as an entirely new investment instrument within the global financial system. The cryptocurrency market, presently comprising over 5,000 currencies, is a recently formed financial instrument based on blockchain technology (Coinmarketcap, 2020). They enable global trade and exchange operations owing to their numerous advantages, including high security, absence of border constraints, decentralisation and transparency (Abboushi, 2017; Ciaian et al., 2016; Ryan, 2017; Swan, 2017). Moreover, portfolio fund managers utilise cryptocurrencies as an effective investing instrument to mitigate risk and capitalise on promising prospects (Corbet et al., 2018; Trimborn et al., 2020; Saksonova and Kuzmina-Merlino, 2019).
2.2.1 Modern portfolio theory (MPT) and diversification
The aforementioned advantages align with Harry Markowitz’s MPT, which outlines methods for diversifying assets within a financial portfolio to optimise the expected rate of return based on the owner’s risk tolerance and capability.
MPT emphasises the significance of diversification for mitigating risk, and the use of cryptocurrencies into portfolios to enhance diversity has also been addressed in the cryptocurrency literature (Chen, 2023). Bitcoin significantly influences return and volatility spillovers among primary cryptocurrencies, indicating an increasing dependency and, consequently, contagion risk (Ali et al., 2024b; Platanakis and Urquhart, 2020). Investors’ capacity to diversify their portfolios may be limited if they exclusively invest in cryptocurrency. The exchange prices of cryptocurrencies may be adversely affected when individuals transfer funds between them in the presence of intense competition. Cryptocurrency portfolios should likely be an integral part of an investor’s comprehensive investment strategy. In this scenario, despite a substantial association, a cryptocurrency portfolio would outperform the performance of an individual coin, at least until one cryptocurrency attains sufficient market dominance. The principal objective of portfolio investment is to acquire benefits from a variety of investment assets that cannot be obtained from a singular asset. To achieve the optimal equilibrium of risk and return, a portfolio must be constructed. Risk is frequently minimised when the assets within the portfolio exhibit substantial uncorrelation. Diversification should ensure that the overall value of the portfolio does not significantly decrease when the value of its individual assets declines (Chan et al., 2019; Klein et al., 2018). Consequently, investing in a diverse portfolio may assist investors in mitigating their exposure to risks linked to certain cryptocurrencies, such as hacking incidents and the collapse of major exchanges. To select the optimal portfolio, one must optimise it. Investors can determine the optimal allocation of assets to different cryptocurrencies to maximise returns by utilising portfolio optimisation techniques.
2.2.2 The risk-return trade-off and substitution hypothesis
Conversely, the risk-return trade-off theory posits that investments with greater risk generally generate greater potential returns to offset the uncertainty, whereas safer investments result in lower returns. This theory clarifies the behaviour of investors in the financial markets, taking into account their risk tolerance and return expectations. The risk-return trade-off is dependent on the level of risk that investors are prepared to assume. Cryptocurrencies exhibit significant volatility, so, during times of economic growth, investors may be more eager to consider additional risk, seeking greater returns, hence boosting cryptocurrency prices (Ahmed, 2024; Bouoiyour et al., 2016; Kristoufek, 2015). Consequently, the stock market, as a substitute investment option, will experience a decrease in stock prices due to low demand. While in case of an economic recession, investors may reduce their risk appetite, as they may transfer from higher-risk assets such as cryptocurrencies and return to safer investments like stocks, resulting in a significant increase in stock market performance (Abdul Rahim et al., 2021; Havidz et al., 2022). Thus, the risk-return trade-off theory assumes an inverse association between the cryptocurrency market and the stock market performance.
Corbet et al. (2024) investigated the inverse relationship between cryptocurrency market dynamics and stock market performance, including time-varying risk aversion. Results indicated that during times of high market uncertainty, increased risk aversion pushes investors to swap from conventional assets, such as equities, to alternative investments, including cryptocurrencies, in accordance with the risk-return trade-off theory. That study used time-varying parameter vector autoregressions (TVP-VAR) to clarify the dynamic interconnections between these markets, demonstrating that cryptocurrency returns frequently increase during periods of stock market a poor performance, particularly when cryptocurrencies function as a hedge or diversifier. This link is dynamic, fluctuating with market sentiment, macroeconomic conditions, and regulatory changes, highlighting the immediate influence of risk aversion on investment decisions.
2.3 Empirical review: determinants of stock markets and the cryptocurrency–equity nexus
The existing literature on the relationship between cryptocurrencies and stock market performance presents highly divergent empirical conclusions, reflecting differences in market structure, regulatory regimes and methodological approaches (Bayar, 2016; Garcia and Liu, 1999; Niroomand et al., 2014). Much of the early literature – largely focused on developed economies – emphasises the diversification and hedging potential of cryptocurrencies (Brière et al., 2015; Tiwari et al., 2018). Studies such as Brière et al. (2015), Baur et al. (2018) and Bouri et al. (2017) document weak or time-varying correlations between Bitcoin and equity markets, concluding that cryptocurrencies may enhance portfolio efficiency, particularly during periods of global uncertainty. This strand of research is closely aligned with MPT, implicitly assuming relatively open capital markets, regulatory clarity and stable macroeconomic environments.
In contrast, a growing body of empirical evidence challenges the diversification narrative, especially in emerging markets (Chan and Nadarajah, 2020; Feng et al., 2018). Studies including Baek and Elbeck (2015), Chan et al. (2017) and Kumah and Odei-Mensah (2021) report that cryptocurrency inclusion can amplify overall portfolio risk due to extreme volatility and speculative behaviour. Research focusing on African and MENA markets further complicates the picture (Naceur and Omran, 2008). While some studies find weak or insignificant linkages between cryptocurrencies and equities (Mensia et al., 2019; Lahiani and Jlassi, 2021), others report negative associations, suggesting competitive dynamics rather than complementarity (Sami and Abdallah, 2021; Eldomiaty and Mohab, 2024). However, much of this regional literature relies on aggregated market samples, low-frequency data or assumes homogeneity across emerging economies, thereby obscuring country-specific institutional mechanisms.
By showing that cryptocurrencies – particularly speculative altcoins – function as direct competitors to domestic equities during crisis periods, this study refines the application of MPT to emerging markets. It provides context-specific evidence that institutional constraints and macroeconomic instability can transform digital assets from diversification tools into channels of capital flight, thereby offering a more realistic framework for understanding crypto–equity dynamics in MENA and other non-liberalised economies. Table 1 contrasts cryptocurrency–stock market relationships in developed markets, emerging markets and the Egyptian case as supported by the results of this paper.
Comparative empirical evidence on cryptocurrency–stock market relationships
| Dimension | Developed markets | Emerging markets (general) | Egypt (this study) |
|---|---|---|---|
| Dominant Interpretation | Diversification/hedging asset | Mixed: diversification or risk amplifier | Liquidity substitution/capital competition |
| Typical Cryptocurrencies Studied | Bitcoin-dominant | Bitcoin and Ethereum | Bitcoin, Ethereum, Neo (altcoin) |
| Correlation with Equity Markets | Weak, time-varying, often insignificant | Inconsistent; positive, negative, or insignificant | Significantly negative (altcoins) |
| Role of Macroeconomic Stress | Global uncertainty (e.g., crises) | Inflation, capital controls, FX pressure | Currency devaluation and sovereign risk |
| Regulatory Environment | Generally permissive, regulated | Partially regulated, heterogeneous | Restrictive/prohibition-based |
| Investor Motivation | Portfolio optimisation | Speculation and partial hedging | Informal hedging and capital preservation |
| Empirical Methods | DCC-GARCH, VAR, wavelets | GARCH, copulas, panel regressions | ARIMAX with macro-financial controls |
| Primary Contribution | Tests MPT assumptions | Highlights volatility spillovers | Identifies altcoin-driven substitution mechanism |
The contrast across market types reveals a clear structural break in how cryptocurrencies interact with equity markets. In developed economies, where capital mobility is high and regulatory frameworks are relatively mature, empirical studies largely support the view that cryptocurrencies – especially Bitcoin – offer diversification or hedging benefits through weak and unstable correlations with equities. These findings are consistent with MPT and assume frictionless capital allocation.
In emerging markets, empirical evidence is considerably more fragmented. While some studies report diversification benefits, others document heightened portfolio risk due to cryptocurrency volatility. A key limitation of this literature is its tendency to treat emerging economies as institutionally similar, overlooking differences in currency regimes, capital controls and regulatory enforcement.
This study advances the literature by demonstrating that Egypt represents a distinct case within emerging and MENA markets. Under conditions of currency devaluation, capital constraints and regulatory prohibition, cryptocurrencies – particularly speculative altcoins – operate as direct competitors to domestic equities rather than complementary assets. By identifying Neo as the primary driver of negative equity market effects and embedding cryptocurrency dynamics within a macro-financial ARIMAX framework, this study provides novel evidence that institutional constraints fundamentally alter crypto–equity interactions. As such, it refines existing theories by showing that diversification outcomes are conditional on market structure rather than universally applicable.
Based on the prior literature, the following hypothesis is formulated:
The return volatility of cryptocurrencies significantly and negatively affects the performance of the EGX30 during the post-pandemic devaluation period.
3. Research design
3.1 Data collection
According to prior literature, the cryptocurrency market can explain fluctuations in stock market performance. To account for the cryptocurrency market, most studies used Bitcoin, Binance coin, Ethereum coin and Neo coin as proxies for the crypto market, whereas EGX30 returns served as proxies for Egyptian stock market performance. These proxies are selected based on market capitalisation and liquidity, providing a comprehensive representation of both established and speculative digital assets. The EGX30 index value is computed in local currency and expressed in US dollars. The EGX30 index comprises the 30 leading companies based on liquidity and activity. To investigate the association between fluctuations in the cryptocurrency market and the performance of the Egyptian stock market, daily data from November 2021 to January 2024 is used. This time frame highlighted the explosive rise in the value of cryptocurrencies, driven by reduced interest rates during the COVID pandemic, which facilitated easier borrowing and investing. Bitcoin has exhibited the characteristics of a speculative asset (Bariviera et al., 2017), a high-risk category of investments that attracts attention for its potential for significant growth rather than its inherent practicality (Urquhart, 2016).
The required data are daily Egyptian stock market returns, extracted from the Egyptian Stock Exchange website. In contrast, BTCR is the daily Bitcoin return, BNBR is the daily Binance Coin return, ETHR is the daily Ethereum Coin return, NEOR is the daily Neo Coin return and all cryptocurrencies’ returns are extracted from the website “investing”. Finally, the control variables are INTR, the interbank rate and USD, the exchange rate, both extracted from the CBE. This sample period (November 2021–January 2024) is critical, as it captures the immediate post-pandemic recovery alongside periods of acute domestic financial stress, notably the shift to a floating exchange rate regime and sharp interest rate hikes in 2022 and 2023. These events likely amplified the substitutability role of cryptos, particularly among investors seeking to hedge against rapid currency devaluation, which is the core mechanism tested.
3.2 The study’s variables
This study employs three types of variables: the dependent variable, the independent variables and the control variables. Table 2 shows the study variables, their descriptions, measurements and sources. The raw daily price data were preprocessed to derive logarithmic returns for all crypto and stock variables (EGX30, BTCR, BNBR, ETHR, NEOR) to ensure stationarity and address volatility clustering, which is often observed in high-frequency financial time series. Missing observations (primarily due to public holidays in one market not aligning with the other) were handled using the previous trading day’s price (Last Observation Carried Forward) to maintain daily series continuity, a standard approach for connecting asynchronous time series. Furthermore, the decision to apply winsorising at the 1 and 99% significance levels was implemented to mitigate the undue influence of extreme outlier returns, which are prevalent in the volatile cryptocurrency market, thereby improving the robustness of the ARIMA estimation.
The study’ variables
| Variables | Description | Measurement | Prior studies | Source |
|---|---|---|---|---|
| Dependent variable | ||||
| EGX30 | Daily Egyptian stock market return | EGXRt = EGXRt: EGX daily return at time t, EGXPt: EGX price at time t; EGXPt−1: EGX price at time t−1 | Omran (2003), Naceur and Omran (2008) | Egyptian Stock Exchange website |
| Independent variables | ||||
| BTCR | Daily Bitcoin return | BTCRt = BTCRt: Bitcoin daily return at time t, BTCPt: Bitcoin price at time t, BTCPt−1: Bitcoin price at time t−1 | Cheah and Fry (2015), Urquhart (2016) | Investing.com website |
| BNBR | Daily Binance Coin return | BNBRt = BNBRt: Binance daily return at time t, BNBPt: Binance price at time t, BNBPt−1: Binance price at time t−1 | Cheah and Fry (2015), Urquhart (2016) | Investing.com website |
| ETHR | Daily Ethereum Coin return | ETHRt = ETHRt: Ethereum daily return at time t, ETHPt: Ethereum price at time t, ETHPt−1: Ethereum price at time t−1 | Cheah and Fry (2015), Urquhart (2016) | Investing.com website |
| NEOR | Daily Neo Coin return | NEORt = NEORt: Neo daily return at time t, NEOPt: Neo price at time t, NEOPt−1: Neo price at time t−1 | Cheah and Fry (2015), Urquhart (2016) | Investing.com website |
| Control variables | ||||
| INTR | Interbank interest rate for each tenor is the weighted average rate for all transactions within this tenor. Overnight interbank interest rates are the operational target of monetary policy | Ii: Interbank interest rate for tenor iii Vj: Volume of transaction, Rj: Rate of transaction | Bernanke and Blinder (1992), Mishkin (2011) | Central bank of Egypt (CBE) |
| USD | Exchange rate is a relative price of one currency (US dollar) expressed in terms of another currency (Egyptian pounds) | FXt is the relative value of Egyptian Pounds to USD | Obstfeld and Rogoff (1995), Meese and Rogoff (1983) | Central bank of Egypt (CBE) |
3.3 Method
The analysis starts by extracting descriptive statistics for the model variables, then proceeds to a correlation analysis. Then, examining whether the variables are stationary or not using the unit root to guarantee their appropriateness to the application of the autoregressive integrated moving average (ARIMA) estimation model, which is unsuitable if any of the variables are not stationary. Given the objective of this paper to investigate the effect of the cryptocurrency market on the Egyptian stock market, this paper employs the ARIMA model. The ARIMA model is used because it is particularly adept at modeling temporal dependencies (autocorrelation) and volatility clustering, which are common in financial return series, thereby ensuring the estimated relationships are not spurious (Foster, 1973; Iliev, 2010). While alternative models such as GARCH or VAR/VECM are often considered for volatility clustering and long-memory processes, the ARIMAX (1,0,1) specification is prioritised here for its parsimony in quantifying direct return-to-return shock transmission (Gorodnichenko and Weber, 2016), while controlling for exogenous macroeconomic factors. Including the four cryptocurrency return variables and the two macroeconomic control variables within the ARIMA framework allows us to test contemporaneous shock transmission between the crypto market and the Egyptian equity market, net of domestic financial policy effects. The ARIMA model is a statistical and econometric modelling technique used to quantify events over time and forecast future values of a time series. Moreover, the ARIMA forecasting equation for a stationary time series is a linear equation similar to regression, wherein the predictors consist of the lags of the dependent variable and/or the lags of the estimation errors. A significant difference between the ARIMA model and regression lies in their respective applications. Given that regression analysis addresses autocorrelation by either removing or factoring out such autocorrelation in the error term before estimating associations, ARIMA models aim to incorporate autocorrelation when it is present (Kalpakis et al., 2001). Data analysis was conducted using EViews 12 for descriptive statistics, correlation analysis and ARIMA modelling. For all variables, winsorising was implemented at the 1 and 99% significance levels.
The ARIMA estimation for EGX30 return utilises an ARIMA(p, d, q) model, where p is the autoregressive order, d is the differencing order, and q is the moving average order. Based on the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), the optimal model for the EGX30 return series was determined to be ARIMA(1, 0, 1), implying one significant lag in the autoregressive component and one significant lag in the moving average component. The differencing order d = 0 is justified by the unit root tests confirming stationarity at levels for EGX30 (Table 4). The lag selection was meticulously chosen using the correlogram’s Partial Autocorrelation Function (PACF) and Autocorrelation Function (ACF), ensuring the residuals are white noise prior to estimation.
The model’s structural integrity relies on the careful integration of control variables. The control variables, INTR (Interbank Rate) and USD (Exchange Rate), are integrated as exogenous regressors in the ARIMA structure, transforming the model into an ARIMAX specification. The exchange rate is critical for capturing the sovereign risk channel – investors’ flight from local currency assets (stocks) to global assets (cryptos) during devaluation pressures (Meese and Rogoff, 1983; Obstfeld and Rogoff, 1995). The interbank rate captures the effect of monetary policy (CBE) on market confidence, a common determinant of emerging market equity returns (Bernanke and Blinder, 1992; Mishkin, 2011).
Thus, the regression model is formulated as follows;
Where:
EGX30t is the Egyptian stock market return on the day (t),
α0 is the model constant,
β1- β6 are the model parameters,
BTCRt is the Bitcoin return in the day (t),
BNBRt is the Binance return in the day (t),
ETHRt is the Ethereum return in the day (t),
NEORt is the Neo currency return in the day (t),
INTRt is the interbank rate on the day (t),
USDt is the exchange rate on the day (t), and
Ɛt is the random error.
4. Data analysis and discussion of results
4.1 Descriptive statistics
The preliminary investigation of the time series begins with descriptive statistics as shown in Table 3. A thorough analysis of the descriptive statistics of the variables is essential for comprehending and evaluating their characteristics, which will be utilised in ARIMA model estimation.
Descriptive statistics
| EGX30 | BTCR | BNBR | ETHR | NEOR | INTR | USD | |
|---|---|---|---|---|---|---|---|
| Mean | 0.001569 | 0.001530 | 0.000731 | 0.001317 | 0.001649 | 0.001423 | 0.001352 |
| Median | 0.000600 | −0.000700 | 0.000500 | −0.000350 | 0.002000 | 0.001342 | 0.000000 |
| Maximum | 0.055500 | 0.107100 | 0.139000 | 0.180100 | 0.403000 | 0.001955 | 0.174400 |
| Minimum | −0.041500 | −0.142500 | −0.185000 | −0.175600 | −0.166000 | 0.000828 | −0.011200 |
| Standard deviation | 0.014044 | 0.025114 | 0.027093 | 0.031382 | 0.043886 | 0.000400 | 0.011481 |
| Skewness | 0.297503 | 0.108510 | −0.225914 | −0.114012 | 1.426340 | 0.034841 | 12.20139 |
| Kurtosis | 4.351124 | 8.518815 | 10.57556 | 9.185529 | 17.01783 | 1.372808 | 169.5634 |
As apparent from Table 3, there are considerable differences between the mean and the median for the Bitcoin and Ethereum coins, as in both cases the mean is substantially larger than the median. This may imply that the mean is closer to the tail in these right-skewed distributions, reflecting the substantial price increases in recent days compared to earlier days. The variation among the three variables is low, as shown by the narrow range of their standard deviations, indicating small disparities. Kurtosis values are slightly less than 3 for the interbank rate only, while for all other variables they are higher than 3, implying that these variables yield occasional extreme returns – either large positive or extreme negative returns – resulting in broad tails on the bell-shaped distribution curve.
The extremely high Kurtosis value for the USD variable (169.5634) highlights the severe volatility and tail risk inherent in the Egyptian Pound/USD exchange rate during the sample period (Nov 2021–Jan 2024). This statistical confirmation of the acute sovereign risk environment supports the core institutional justification for our substitutability hypothesis. This non-normal distribution across all return series (Kurtosis >3) underscores the necessity of using the ARIMA model to properly manage the conditional heteroskedasticity and time-series dependencies typical of crisis-laden emerging markets (Kristoufek, 2019).
4.2 Correlation analysis
Table 4 summarises the results of testing for multicollinearity among the variables, indicating that the variables display no multicollinearity. The correlation matrix also shows the exchange rate’s importance in explaining Egyptian stock market performance, as it has a significant correlation with the dependent variable (EGX30).
Correlation analysis
| EGX30 | BTCR | BNBR | ETHR | NEOR | INTR | USD | |
|---|---|---|---|---|---|---|---|
| EGX30 | 1.000000 | ||||||
| BTCR | 0.082771 | 1.000000 | |||||
| BNBR | 0.032172 | 0.686942** | 1.000000 | ||||
| ETHR | 0.083216 | 0.850135** | 0.699153** | 1.000000 | |||
| NEOR | −0.018284 | 0.657816** | 0.567658** | 0.639498** | 1.000000 | ||
| INTR | 0.081781 | 0.037028 | −0.004925 | 0.004938 | 0.016470 | 1.000000 | |
| USD | 0.124013** | 0.008611 | −0.004224 | −0.033723 | −0.010169 | −0.106069* | 1.000000 |
Note(s): ** Significant correlation at the 0.01 level (2-tailed); * Significant correlation at the 0.05 level (2-tailed)
4.3 Unit root tests
Assessing the stationarity of the variables is essential before estimating the model with ARIMA, as this method is applicable only when the variables are stationary at levels or in first differences. The Augmented Dickey–Fuller (ADF) and Phillips–Perron (PP) tests as shown in Table 5 demonstrate that the variables are stationary at their levels, except INT, which is stationary solely at first differences. Consequently, it is assured that none of the remaining variables are I (2). Reporting both ADF and PP tests ensures that results are robust against structural breaks and potential size distortions.
Unit root tests
| Variable | ||||||||
|---|---|---|---|---|---|---|---|---|
| ADF test | PP test | |||||||
| Level | 1st difference | Level | 1st difference | |||||
| Intercept | Intercept and trend | Intercept | Intercept and trend | Intercept | Intercept and trend | Intercept | Intercept and trend | |
| EGX30 | −20.61704*** | −20.75216*** | −13.95946*** | −13.94472*** | −20.60705*** | −20.85684*** | −177.6572*** | −177.5125*** |
| BTCR | −23.02032*** | −23.07866*** | −16.84142*** | −16.82087*** | −23.02031*** | −23.07866*** | −312.8761*** | −310.4181*** |
| BNBR | −24.78386*** | −24.76459*** | −17.45991*** | −17.44514*** | −24.78105*** | −24.76210*** | −270.4517*** | −269.7960*** |
| ETHR | −25.09712*** | −25.08598*** | −12.25432*** | −12.24231*** | −25.22837*** | −25.22746*** | −231.2027*** | −231.2443*** |
| NEOR | −24.48433*** | −24.49767*** | −18.47677*** | −18.45794*** | −24.54308*** | −24.57969*** | −179.5596*** | −179.9410*** |
| INTR | −0.825148 | −2.576489 | −6.638135*** | −6.632885*** | −0.810591 | −3.118860 | −25.39414*** | −25.35421*** |
| USD | −20.08458*** | −20.18151*** | −13.48736*** | −13.47428*** | −20.14749*** | −20.18943*** | −187.7086*** | −187.4850*** |
Note(s): *** indicate the rejection of the null hypothesis at the 1% significance level. The number of lags is determined by the Schwarz information criteria, with a maximum of 18 lags allowed. The bandwidth for the PP test is automatically determined by the Newey-West Bandwidth, employing the Bartlett Kernel spectral estimate method. The crucial values at the 1%, 5%, and 10% significance levels for the ADF and PP tests are −3.442483, −2.866784, and −2.569624 for the test with only an intercept, and −3.975532, −3.418354, and −3.131670 for the test with an intercept and trend
As noted by Perron (1989), traditional unit root tests may bias results towards erroneously accepting the null of a unit root when the data exhibit trend stationarity with a structural break. Table 6 presents the outcomes of the breakpoint unit root testing.
Unit root tests with structural breaks
| Level | First difference | |
|---|---|---|
| EGX30 | −21.26294*** | −39.17890*** |
| BTCR | −24.57456*** | −41.04809*** |
| BNBR | −26.28273*** | −41.68131*** |
| ETHR | −26.81840*** | −41.62681*** |
| NEOR | −27.36723*** | −41.44876*** |
| INTR | −3.562709 | −22.27012*** |
| USD | −26.63425*** | −26.63425*** |
Note(s): *** indicate the rejection of the null hypothesis at the 1% significance level. The trend specification included an intercept and a trend, with the break anticipated to occur in both the intercept and the trend. The number of lags is determined by the Schwarz information criteria, with a maximum lag of 18. Breakpoint selection was determined based on the Dickey-Fuller minimum t-test. The critical values for the test at the 1%, 5%, and 10% significance levels are −4.949133, −4.443649, and −4.193627, respectively
The breakpoint unit root test displayed that INTR is stationary at the first difference. This result further advocates the use of the ARIMA, which, as previously stated, is appropriate when variables are integrated of order I (0) or I (1).
4.4 ARIMA model results
To examine the impact of the cryptocurrency market on the performance of the Egyptian stock market, the ARIMA model is employed. Hence, daily time-series data were used from November 2021 to January 2024, incorporating the EGX30 index for stock market returns alongside those of prominent cryptocurrencies, namely Bitcoin, Binance Coin, Ethereum and NEO. The estimated model is the ARIMA(1, 0, 1) with exogenous variables (ARIMAX). The findings indicated a significant negative impact of the cryptocurrency market on the Egyptian stock market, with NEO cryptocurrency generating the most significant negative influence on EGX30 (Chokor and Alfieri, 2021). This finding provides strong quantitative support for the liquidity substitution hypothesis in constrained emerging markets, where investors reallocate capital from domestic equities to decentralised digital assets, particularly during periods of high economic uncertainty.
The results of the coefficients estimated by the ARIMA model are presented in Table 7. Crucially, the estimated coefficients for the time-series components are: ϕ1 (AR(1)) = 0.045591 (t-statistic = 0.939784) and θ1 (MA(1)) = 0.042813 (t-statistic = 2.277140). The AR (1) term is statistically insignificant, suggesting that only past forecast errors, not past returns, significantly influence current EGX30 returns. The findings showed that the return of the NEO cryptocurrency has a significant negative impact on the EGX30. Accordingly, every 1% increase in the NEO cryptocurrency return decreases the EGX30 return by 4.3%. Therefore, it is evident why Egypt consistently issues cautious warnings on digital currency investment. This finding demonstrates that there is dynamic competition between the Egyptian stock market and the cryptocurrency market, as the two operate as mutually exclusive markets. The high significance of NEO over BTC, ETH and BNB is noteworthy. This divergence suggests that Egyptian investors may be using smaller, more volatile and less liquid alternative coins like NEO for speculative returns, which are perceived as detached from the heavily monitored or controlled domestic financial system, thereby amplifying the direct substitution effect during capital flight.
ARIMA coefficient estimates and diagnostic checks
| Dependent variable EGX30 | Coefficient | t-statistic |
|---|---|---|
| BITR | 0.045591 | 0.939784 |
| BNBR | −0.019541 | −0.602642 |
| ETHR | 0.058153 | 1.502730 |
| NEOR | −0.042813*** | −2.277140 |
| INTR | 3.316828*** | 2.192906 |
| USD | 0.166602*** | 3.155320 |
| BITR | 0.045591 | 0.939784 |
| C | −0.003438 | −1.533912 |
| Diagnostic checks | ||
| R-squared | 0.043422 | |
| Adjusted R-squared | 0.032448 | |
| F-statistic | 3.956774 | |
| Prob(F-statistic) | 0.000700 | |
| Breusch–Pagan–Godfrey, F-statistic | 0.722612 | |
| Breusch–Pagan–Godfrey Prob. F (6,523) | 0.6315 | |
| Jarque–Bera | 37.03867 | |
| Jarque–Bera Prob | 0.000000 | |
Note(s): *** indicates 1% significance level
Additionally, the results showed that the interbank rate has a significant positive impact on the performance of the Egyptian stock market (Bordo and Wheelock, 2007). Accordingly, every 1% increase in the interbank rate increases the EGX30 return by 330%. The increase in the interbank rate reflects the CBE’sresponse to control inflation and currency depreciation, which investors perceive as a commitment to stabilising prices, leading to investor confidence in the economy and more investment in stocks, especially from foreign investors seeking higher yields. This positive relationship confirms the importance of controlling for domestic monetary policy: the central bank’s actions to stabilise the market bolster confidence in traditional assets (stocks), partially mitigating the flight to decentralised assets.
Moreover, the exchange rate has a significant positive impact on the performance of the Egyptian stock market. Accordingly, every 1% increase in the exchange rate increases the EGX30 return by 17%. The appreciation of the exchange rate increases the attractiveness of Egyptian assets, such as stocks, for foreign investors. This is because they can purchase stocks at a lower price in dollars, particularly when the local currency depreciates. By buying stocks cheaply, they could potentially benefit from future appreciation of both the stock and the currency. Moreover, this attracts Egyptian investors, who frequently view stocks as an alternative hedging strategy against inflation (El-Wassal, 2005; Omran, 2003), particularly during periods of currency devaluation and rising inflationary pressures.
The R-squared score is 0.043422, indicating that the model explains 4.3% of the variance in EGX30 returns. The relatively low R2 is not a sign of poor model quality, but rather reflects the highly efficient and unpredictable nature of daily stock market returns. High-frequency returns are dominated by unpredictable idiosyncratic noise; thus, even models with statistically significant factors are expected to have low R2 values. The actual value lies in the statistical significance of the β coefficients (particularly NEO, INTR and USD), which robustly support the theoretical mechanisms of substitutability and control over endogenous factors.
Despite the data not conforming to a normal distribution, as indicated by Jarque–Bera’s p-value, the central limit theorem suggests that the data approximate normality due to the substantial sample size of 530 observations. Given that the Breusch–Pagan–Godfrey p-value (0.6315) is above the designated threshold (p > 0.05), the null hypothesis of homoskedasticity is accepted, while heteroskedasticity is rejected. The absence of heteroskedasticity validates the use of ARIMAX over GARCH specifications, as the residuals do not exhibit the significant volatility clustering required to justify more complex models. Finally, we conducted a Ljung–Box Q-test on the ARIMA residuals, which confirmed the absence of residual autocorrelation, indicating that the chosen ARIMA (1,0,1) model adequately captures the serial dependence in the EGX30 return series.
4.5 Discussion of results
Theoretical contributions contradict Markowitz’s MPT in emerging markets such as Egypt. Despite the diversification benefits typically attributed to cryptocurrencies, the results indicate that their function is predominantly competitive rather than supplementary. This paper supports the risk-return trade-off theory, indicating that investors prefer higher-risk assets, such as cryptocurrencies, which offer the prospect of greater returns. The negative and significant coefficient for NEOR (−0.0428) carries high economic significance, indicating that a 1% increase in speculative altcoin returns triggers a substantial 4.28% withdrawal of liquidity from the Egyptian equity market. This magnitude underscores a powerful substitution mechanism in which capital is reallocated to digital assets during periods of domestic financial instability. Crucially, the dominance of this substitution effect over diversification suggests that, in the Egyptian context, cryptocurrencies act primarily as a financial competitor, siphoning liquidity from traditional markets to serve as an unregulated store of value during currency crises.
The findings of this paper contradict the MPT Theory, which proposes methods for diversifying an investment portfolio to optimise the expected rate of return, while accounting for shareholders’ risk tolerance and competence, since the aim of Markowitz (1952) was to reduce the idiosyncratic risk, which is the intrinsic risk associated with an investment due to its unique attributes. Although the benefits of diversification correspond to the cryptocurrency market, as it is lately includes more than 5,000 cryptocurrencies and is a recently invented trading instrument based on blockchain technology, which facilitates worldwide commerce by taking into account strict security measures, eliminating geographical restrictions, adapting decentralisation and complete transparency; the research results confirmed that the cryptocurrencies market and the stock exchange market are distinct and separate markets. As investors utilise cryptocurrencies as an effective alternative trading instrument that reduces risk and offers profitable prospects, rather than stocks listed on the Egyptian stock exchange. Thus, the cryptocurrency market is not only a fundamental component of investment but also an essential driver of stock market performance. Thus, the negative and significant impact of NEO on EGX30 is strong evidence of capital flight. Investors are choosing the perceived liquidity and independence of the decentralised NEO currency over domestic equities, especially as the volatility of smaller altcoins may better align with the speculative risk appetite for hedging against currency collapse than that of the less volatile major coins (BTC, ETH). Behaviourally, this suggests that Egyptian investors view smaller altcoins as a “financial escape valve” that is less integrated with formal global institutional cycles than Bitcoin, making them a preferred vehicle for bypassing domestic capital constraints. The specific negative impact of NEOR is likely a result of its high-beta nature; its extreme volatility attracts speculative investor behaviour, which, combined with regulatory uncertainty in the formal banking sector, makes it a more attractive, albeit riskier, alternative to traditional Egyptian stocks.
However, the findings of this paper contradict the results of Hung (2022), who revealed that Bitcoin exhibits an average positive association with the stock market of Central and Eastern European countries (Croatia, Hungary, Poland, Romania and the Czech Republic), using the Dynamic Equi-correlation GARCH (DECO-GARCH) model. In addition, the findings of this paper contradict those of Kumah and Odei-Mensah (2021), who found that Ethereum has a significant negative effect on the Egyptian market in the long term, whereas Litecoin has a positive effect. Furthermore, the findings contradict the findings of Lahiani and Jlassi (2021) and Jeribi and Ghorbel (2022), who separately examined the relationship between the top five prominent cryptocurrencies (Bitcoin, Dash, Ethereum, Monero, Ripple) and the Brazil, Russia, India, China and South Africa (BRICS) market and advanced economy. Nevertheless, none of this research found a substantial association between cryptocurrencies and the BRICS stock markets compared with developed economies. In BRICS nations, Bitcoin does not function as a hedging instrument relative to high-income ones. Instead, it is considered a multidimensional asset inside the emerging market. This study resolves these contradictions by demonstrating that the relationship is crisis-specific; in Egypt, the negative impact persists and is amplified by sovereign risk and the EGP devaluation cycle, a factor often overlooked in regional BRICS studies.
Furthermore, the finding that only NEO (and not BTC or ETH) exerts a significant negative influence is a key insight. The lack of significance for major cryptocurrencies suggests that these assets may have evolved beyond pure substitution vehicles for Egyptian retail investors, possibly due to their increasing institutional adoption and higher correlation with global markets. Neo, as a smaller, more volatile altcoin, better represents the speculative, high-risk “flight” capital seeking detachment from the domestic financial system, a dynamic consistent with the liquidity-substitution mechanism in highly regulated and constrained emerging markets. This specific “altcoin-substitution” mechanism confirms that the negative impact is speculative-driven, as retail investors chase the high-beta returns of coins like NEO to offset the erosion of local equity returns during inflation shocks. This highlights that while major cryptos might act as diversifiers, speculative altcoins like NEO act as direct competitors to domestic equity liquidity.
Conversely, this paper’s findings provide evidence supporting the risk-return trade-off theory, which posits that greater risk is associated with a higher probability of achieving higher returns. In comparison, low risk is associated with a lower probability of achieving higher returns. The risk-return trade-off theory focuses on the compromise investors make when assessing alternative financial assets, weighing the potential risks and returns. The theory suggests that investors aiming for significant returns will shift their assets from the stock market to the cryptocurrency market. The positive and significant effects of the Interbank rate (330%) and Exchange rate (17%) in our model further prove that this behavioural shift is influenced by policy announcements; as the CBE signals rising domestic risk through rate hikes and devaluations, investors’ risk-management response is to move towards the decentralised crypto market. In this context, the cryptocurrency market serves neither as a hedge nor as a diversifier, but as an alternative speculative platform that disrupts traditional market performance.
Additionally, the findings of this paper are consistent with those of Sami and Abdallah (2020), who illustrated that cryptocurrency and stock markets in Egypt serve as alternative investment platforms. Their analysis highlighted that cryptocurrencies hinder stock market performance in Egypt by serving as alternative assets for investors searching for diversification. These results, viewed through the lens of recent currency instability, suggest that the competition between the crypto market and EGX30 has intensified beyond normal portfolio rebalancing into a structural issue driven by a lack of trust in domestic financial assets amidst severe currency devaluation.
Furthermore, this paper provides important insights for regulators, portfolio managers and corporate financial decision makers in emerging markets characterised by currency volatility and regulatory uncertainty surrounding digital assets. The empirical results show that cryptocurrency returns – particularly those of speculative altcoins such as Neo – have a significant negative effect on Egyptian stock market performance. This indicates that, in financially constrained environments, cryptocurrencies act primarily as competing investment channels rather than as instruments that enhance portfolio diversification.
From a regulatory perspective, the findings suggest that restrictive or unclear cryptocurrency policies may unintentionally accelerate capital migration towards informal digital markets, thereby weakening domestic equity liquidity. Adopting controlled supervisory approaches, such as regulatory sandboxes, could improve oversight of digital asset activity without full market liberalisation. Moreover, incorporating cryptocurrency volatility indicators into financial stability monitoring frameworks may help policymakers detect early signs of equity market stress. While credible monetary tightening supports investor confidence, it is insufficient on its own to counteract crypto-driven substitution without improving the depth and appeal of regulated investment alternatives.
From a practical standpoint, the results offer clear guidance for financial managers. Increases in speculative cryptocurrency returns should be interpreted as warning signals of potential equity outflows rather than diversification benefits. Portfolio managers should therefore integrate cryptocurrency-specific indicators into risk management and allocation decisions. Additionally, periods of strong altcoin performance are likely to coincide with reduced equity liquidity, suggesting that firms should exercise caution when timing equity issuance and major financing decisions.
5. Conclusions, limitations and suggestions for future research
Several studies have shown that the cryptocurrency market can improve stock market performance. However, research regarding the Egyptian stock market is scarce. As a result, this study addresses a gap in the literature by investigating the influence of the cryptocurrency market on the performance of the Egyptian stock exchange. This paper reveals significant evidence that the cryptocurrency market negatively impacts the performance of the Egyptian stock market, with NEO demonstrating the most significant influence on the EGX30 index. The analysis confirms that cryptocurrencies and the Egyptian stock market function as distinct financial markets, categorising cryptocurrencies as alternative investment instruments rather than supplementary assets. This indicates a shift in investor attention from conventional stock markets to cryptocurrencies in search of higher-risk, higher-reward opportunities, thereby diminishing stock market performance in Egypt. This finding refines the MPT by establishing institutional constraints – namely, regulatory prohibitions and currency instability – as critical moderating variables that convert the typical diversification role of crypto into a detrimental liquidity-substitution mechanism in emerging economies. This contribution is particularly significant for the MENA literature, as it provides novel empirical evidence that “altcoin momentum” serves as a leading indicator of capital flight in non-liberalised economies (Galal, 2022; Yartey and Adjasi, 2007), offering a unique perspective that matters globally for understanding financial contagion in emerging markets.
Moreover, this paper’s findings indicate that the devaluation of the Egyptian pound has a more significant and positive effect on stock market performance than its appreciation. The exchange rate and the interbank rate significantly impact stock returns. However, the positive effect of depreciation may become negative over time as foreign investors anticipate additional devaluation. Although devaluation initially lowers stock prices, which increases returns, it eventually reduces long-term investment due to lower returns. These results correspond with the flow-oriented strategy, indicating that currency depreciation improves competitiveness and raises stock prices, especially in countries with a greater proportion of export-oriented companies like Egypt. Historical data confirm this, since significant devaluations, such as in November 2016, resulted in a substantial increase in the EGX30 index, accompanied by increased foreign investment, especially from Gulf investors. However, Egypt’s trade deficit and reliance on imports highlight the complex dynamics between devaluation, inflation and stock market performance.
The implications of this cryptocurrency-driven substitution are profound for financial market stability and policy. The findings highlight the need for governments to assess the potential benefits of legalising cryptocurrency transactions and implementing comprehensive regulatory frameworks in regions where such transactions are currently prohibited (Chen and Liu, 2022), thereby increasing investor confidence, and attracting additional investors to the market. The legalisation and appropriate regulation of cryptocurrency trading could enhance regional economic growth, promote portfolio diversification for investors and increase overall market efficiency. For investors and portfolio managers, the study suggests a cautious risk-management strategy: while major cryptocurrencies like Bitcoin may offer long-term diversification, smaller altcoins like Neo should be viewed as high-beta competitors that signal periods of equity liquidity drain. Managers should use dynamic hedging that accounts for the inverse relationship between altcoin spikes and domestic equity returns to protect portfolio value during currency shocks.
The findings reveal that Egyptian policymakers face a dual dilemma that requires a two-pronged approach. In the short term, policy must focus on mitigating investor flight: The CBE and financial regulators must immediately acknowledge that the significant negative impact of a speculative altcoin like NEO signals systemic investor flight from domestic assets, primarily driven by acute currency devaluation fears. The actionable recommendation here is to focus on enhancing the attractiveness of domestic, regulated investment alternatives, such as establishing highly liquid, easily accessible local ETFs or issuing inflation-indexed certificates, to counter the speculative pull of the unregulated digital market. Furthermore, the CBE needs to clarify its official stance on cryptocurrencies to minimise the unique regulatory risk premium currently embedded in the thriving unofficial market. Specifically, implementing a “Regulatory Sandbox” for digital assets could allow the CBE to monitor transaction volumes and sentiment-driven flows without fully liberalising the market, thereby mitigating the risk of sudden capital flight. Conversely, the long-term strategy must prioritise structural stability to address the root cause of capital flight: the government should focus on fundamental structural reforms to improve exchange rate stability and enhance corporate disclosure and governance within the EGX30. Legalising and regulating cryptocurrency should be considered a long-term, structural strategy to monetise underground economic activity and integrate remittance flows, provided that robust consumer protection, anti-money laundering and systemic risk frameworks are established beforehand. This distinction between short-term damage control and long-term structural reform is essential for maximising policy impact.
Furthermore, policymakers face a dual dilemma regarding the devaluation of the Egyptian pound (LE). Although intentional currency devaluation may enhance the attractiveness of stocks to foreign investors by reducing their local-currency costs, subsequent inflation could deter investment. Two principal factors accelerate this hindering effect: first, higher import prices that diminish investor profitability; second, the attractiveness of alternative investments, such as real estate, compared to stocks, due to their rising prices. Moreover, the Egyptian government should carefully evaluate the long-term consequences of devaluation, as repeated devaluations may lead to a sustained decline in stock prices, especially if inflationary pressures outweigh the positive effects of increased foreign investment.
Further studies can extend the findings of this paper, which offer significant insights for stakeholders in the Egyptian stock market and other developing economies. This study has specific limitations that necessitate further investigation. Specifically, it did not broaden its investigation to encompass multiple countries, which could provide more extensive and generalisable conclusions. Data limitations must also be acknowledged, including the focus on daily frequency, which may overlook intraday volatility; the sample size constraints of the post-pandemic period; and the selection of only four major cryptocurrency proxies. Using panel data analysis rather than exclusively time-series analysis can improve the robustness of conclusions by integrating the advantages of both cross-sectional and longitudinal analysis. Furthermore, integrating macroeconomic factors such as GDP, crude oil production and cryptocurrency trading volume could enhance the analysis and improve its predictive reliability. Future research should also consider more sophisticated methodologies, such as machine learning forecasting, wavelet coherence and sentiment-driven models, to capture the complex crypto–stock contagion dynamics identified here (Okorie, 2021; Svaleryd and Vlachos, 2002). Eventually, this study focused on a limited subset of digital currencies, which may limit understanding of the broader digital asset market. Broadening the scope to include a wider range of cryptocurrencies would yield a more comprehensive dataset, enabling a more sophisticated investigation of correlations and performance in the digital asset market. Hence, this paper allows for additional investigation. This future research agenda will be crucial for developing a comprehensive “Digital Asset Policy Toolkit” for emerging economies, allowing them to transform speculative capital flight risks into structured diversification opportunities.
