The cryptocurrency market has undergone rapid evolution since Bitcoin's inception in 2009, growing into a digital asset class exceeding $1 trillion by 2023. With increasing investor interest due to price volatility, speculative opportunities, and potential diversification benefits, understanding the market efficiency of cryptocurrencies like Bitcoin and Ethereum has become crucial for both traders and policymakers. While previous studies have explored time-varying efficiency, few have systematically analyzed the underlying drivers influencing this dynamic behavior.
This article investigates the time-varying inefficiency levels in Bitcoin and Ethereum using daily data from August 7, 2016, to February 15, 2023. We employ the Adjusted Market Inefficiency Magnitudes (AMIMs) measure and quantile regression to uncover not only how efficiency fluctuates over time but also what factors—ranging from global financial stress to liquidity and pandemic impacts—drive these changes.
Understanding Cryptocurrency Market Efficiency
Market efficiency, rooted in Eugene Fama’s Efficient Market Hypothesis (EMH), suggests that asset prices fully reflect all available information. In an efficient market, it is impossible to consistently achieve returns above the market average through speculation or analysis because any new information is immediately priced in.
However, the cryptocurrency market presents unique challenges to traditional EMH assumptions. Its high volatility, emotional trading behaviors, and susceptibility to herd mentality suggest deviations from efficiency. The Adaptive Market Hypothesis (AMH) offers a more fitting framework: markets evolve over time, adapting to changing conditions, regulations, investor behavior, and external shocks.
Bitcoin and Ethereum dominate the crypto landscape, collectively representing over 65% of total market capitalization. Their high trading volumes, widespread adoption, and role as market bellwethers make them ideal subjects for studying broader market dynamics. Unlike smaller altcoins, they benefit from mature ecosystems, institutional interest, and deeper liquidity—factors that can influence efficiency.
Methodology: Measuring Time-Varying Inefficiency
To assess market efficiency dynamically, this study uses the Adjusted Market Inefficiency Magnitudes (AMIMs) methodology proposed by Le Tran and Leirvik (2019). This approach captures inefficiency as a continuous, time-varying process rather than a static binary state.
The model begins with an autoregressive (AR) representation of cryptocurrency returns:
$$ {R}_{t}={\alpha }_{0}+\sum_{i=1}^{p}{\alpha }_{1}{R}_{t-1}+{\epsilon }_{t} $$
If the coefficients $ \alpha_1 $ to $ \alpha_p $ are statistically significant, the market is considered inefficient—indicating that past prices influence future movements, enabling predictability.
From this foundation, the Magnitude of Market Inefficiency (MIMt) is calculated based on standardized regression coefficients. However, MIMt can be biased by lag selection. To correct this, Monte Carlo simulations determine a critical threshold under the null hypothesis of efficiency. Subtracting this threshold yields the Adjusted Market Inefficiency Magnitude (AMIMt):
$$ {AMIM}_{t}=\frac{{MIM}_{t}-{R}_{CI}}{1-{R}_{CI}} $$
- AMIMt > 0 → Market is inefficient
- AMIMt ≤ 0 → Market is efficient
Using overlapping one-year rolling windows ensures a smooth transition across periods and allows detection of short-term shifts in efficiency linked to specific events.
Key Findings: Time-Varying Efficiency Patterns
Analysis reveals that both Bitcoin and Ethereum exhibit time-varying efficiency, alternating between efficient and inefficient states throughout the sample period.
Bitcoin vs. Ethereum: A Comparison of Efficiency
- Bitcoin shows higher overall efficiency compared to Ethereum.
- On average, Bitcoin was efficient in approximately 40% of observations.
- Ethereum was efficient in only about 20% of cases.
- This suggests that Bitcoin’s longer history, greater adoption, and perceived stability contribute to more predictable pricing behavior.
Periods of sustained efficiency in Bitcoin occurred during:
- Mid-2018 (April–July)
- Late December 2021 to early October 2022
- December 2022 through February 2023
Ethereum experienced notable efficient phases during:
- May–September 2018
- May–October 2022
- November 2022 onward
These findings support the Adaptive Market Hypothesis, where efficiency evolves in response to market maturity, regulation, investor sentiment, and macroeconomic events.
What Drives Cryptocurrency Market (In)Efficiency?
Using quantile regression (QR), we examine how various factors impact AMIMs across different levels of market efficiency—from highly efficient (low quantiles) to deeply inefficient (high quantiles). This method provides deeper insights than traditional OLS regression by revealing heterogeneous effects across the distribution.
Core Drivers Identified
1. Global Financial Stress Negatively Affects Inefficiency
Contrary to expectations, higher global financial stress correlates with lower AMIM values, meaning markets become more efficient during turbulent times.
Interpretation: During crises, investors may rely more on fundamentals or automated trading algorithms, reducing emotional distortions and arbitrage opportunities. This aligns with Zhang and Wang (2021), who found crypto markets relatively insulated from traditional financial stress.
2. Liquidity Boosts Efficiency Across All States
Cryptocurrency liquidity—measured as trading volume relative to market cap—has a positive and significant effect on AMIMs regardless of whether the market is efficient or inefficient.
Why it matters: High liquidity reduces bid-ask spreads, minimizes price slippage, and enhances price discovery—hallmarks of an efficient market. Low liquidity often reflects information asymmetry and structural frictions.
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3. Money Flow Enhances Efficiency Only When Markets Are Stable
The money flow into cryptocurrencies improves efficiency—but only when markets are already relatively stable and efficient.
Implication: New capital inflows during calm periods likely come from informed investors or institutions that stabilize prices. During chaotic periods, inflows may amplify speculation rather than improve pricing accuracy.
4. The COVID-19 Pandemic Increased Market Inefficiency
The pandemic had a positive and significant impact on AMIMs across most quantiles—meaning it worsened inefficiency.
Possible causes:
- Panic-driven trading
- Herding behavior (Yousaf et al., 2021; Maouchi et al., 2021)
- Increased retail participation during lockdowns (Guzmán et al., 2021)
- Amplified sentiment swings due to uncertainty
Robustness checks splitting the sample pre- and post-pandemic confirm that the relationship between external factors and efficiency shifted significantly after early 2020.
Other Factors: Limited or No Impact
- Investment substitutes (gold, energy): No significant effect on efficiency.
- Uncertainty indices (EPU, VIX): Minimal influence on Bitcoin or Ethereum.
- World stock markets (MSCI): Positive effect only when Bitcoin is efficient.
- Federal Funds Rate: Negatively affects Ethereum AMIMs.
These results suggest that while crypto markets are increasingly integrated with traditional finance, their internal dynamics—especially liquidity and investor behavior—remain dominant forces shaping efficiency.
Frequently Asked Questions (FAQ)
What does "market inefficiency" mean in crypto?
Market inefficiency means that cryptocurrency prices do not fully reflect all available information. As a result, historical price patterns can predict future movements, creating opportunities for arbitrage or excess returns. Inefficient markets often exhibit volatility clustering, herding behavior, and delayed reactions to news.
Why is Bitcoin more efficient than Ethereum?
Bitcoin’s greater age, larger user base, higher liquidity, and status as a digital gold standard contribute to its relatively higher efficiency. Ethereum, while innovative with smart contracts and DeFi applications, experiences more speculative activity and development-driven price swings, which can impair pricing efficiency.
How did the pandemic affect crypto market efficiency?
The COVID-19 pandemic increased market inefficiency in both Bitcoin and Ethereum. Lockdowns led to surges in retail trading, emotional decision-making, and herding behavior—all of which disrupted rational price formation. The crisis also intensified correlations between crypto and traditional assets, undermining claims of decoupling.
Can we predict when crypto markets will be efficient?
While exact timing remains challenging, certain conditions correlate with higher efficiency:
- High liquidity environments
- Periods of low global financial stress
- Stable regulatory climates
- Absence of major geopolitical shocks
Tools like AMIMs can help monitor real-time shifts and inform trading strategies accordingly.
Does high volatility always mean inefficiency?
Not necessarily. While high volatility often accompanies inefficiency—especially when driven by speculation or panic—it can also occur in efficient markets reacting swiftly to new information. The key distinction lies in whether price movements are predictable based on past data. If yes, the market is inefficient.
How can traders use efficiency insights?
Traders can adapt their strategies based on prevailing market conditions:
- In inefficient markets, technical analysis and momentum strategies may outperform.
- In efficient markets, passive holding or algorithmic execution may yield better risk-adjusted returns.
Monitoring AMIM trends helps identify regime shifts before they become obvious in price action alone.
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Conclusion
This study confirms that Bitcoin and Ethereum markets are not statically efficient or inefficient, but instead follow a dynamic pattern shaped by evolving conditions. Using AMIMs and quantile regression over a comprehensive period—including bull runs, crashes, and the pandemic—we identify key drivers of inefficiency:
- ✅ Liquidity consistently enhances market efficiency.
- ✅ Financial stress paradoxically improves efficiency.
- ✅ Money flow supports efficiency—but only in stable times.
- ❌ The pandemic significantly increased inefficiency, likely due to behavioral biases.
These insights offer valuable guidance for investors seeking to optimize entry/exit points and portfolio allocations based on underlying market structure—not just price trends.
While limitations exist—such as non-normal return distributions in crypto—the AMIM framework provides a robust tool for monitoring real-time market health. Future research could extend this approach to include sentiment data from social media or blockchain analytics for even deeper insight.
For now, understanding what drives cryptocurrency market efficiency isn’t just academic—it’s a strategic advantage in one of the world’s most volatile yet promising asset classes.