The world of cryptocurrency has evolved rapidly over the past decade, drawing increasing attention from both retail and institutional investors. As digital assets gain legitimacy, researchers are applying traditional financial frameworks to understand what drives their returns. One landmark study published in The Journal of Finance in April 2022—"Common Risk Factors in Cryptocurrency" by Yukun Liu and colleagues—offers a groundbreaking analysis of systematic risk factors in the crypto market.
This research reveals that market, size (scale), and momentum are the three dominant factors explaining cross-sectional cryptocurrency returns. By adapting established asset pricing models from equity markets, the authors construct a cryptocurrency three-factor model that successfully explains the performance of various trading strategies.
In this article, we’ll break down the core findings of the study, explore its implications for investors and researchers, and examine how early-stage asset classes like crypto exhibit unique yet analyzable patterns.
Understanding Cryptocurrency Risk Factors
Traditional finance relies on multi-factor models—like the Fama-French three-factor model—to explain stock returns. This study asks: Can similar tools be applied to cryptocurrencies?
The answer is yes.
Using a comprehensive dataset of crypto assets, the researchers evaluate well-known return predictors from equity markets—including size, momentum, trading volume, and volatility—and test their effectiveness in the context of digital currencies.
Key Cryptocurrency Risk Factors Identified
- Market risk: Exposure to overall market movements.
- Size (scale) factor: Smaller-cap cryptos tend to outperform larger ones.
- Momentum factor: Assets with recent price gains continue to perform well in the short term.
These three factors form the backbone of a new cryptocurrency pricing model, capable of explaining the returns of multiple long-short strategies.
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Size Matters: The Scale Premium in Crypto
One of the most compelling findings is the existence of a size effect in cryptocurrency markets. Much like small-cap stocks historically outperforming large-cap stocks, smaller cryptocurrencies generate higher average returns.
The study constructs portfolios sorted by:
- Market capitalization
- Last traded price
- Highest daily price
Across all measures, lower-tier (smaller) cryptos delivered significantly higher returns than their larger peers. A long-short strategy going long on small cryptos and short on large ones produced statistically significant excess returns.
This scale premium may stem from two key mechanisms:
- Liquidity risk: Smaller cryptos are less liquid, meaning investors demand higher returns as compensation for trading difficulty.
- Convenience yield vs. capital gains trade-off: Investors may favor major coins like Bitcoin not for return potential but for their utility and acceptance—creating a return gap favoring lesser-known tokens.
While these explanations are still theoretical, they align with emerging models of crypto asset valuation.
Momentum Effect: Riding the Crypto Wave
Momentum—the tendency for assets that have performed well recently to keep rising—is one of the strongest anomalies in financial markets. In crypto, it’s even more pronounced.
The study finds that 1-week to 4-week momentum strategies yield strong, statistically significant returns. Specifically:
- Buying past winners and selling past losers generates consistent weekly alpha.
- The effect persists across multiple time horizons and portfolio constructions.
This suggests investor overreaction plays a crucial role in crypto pricing. Unlike mature markets where information diffuses quickly, crypto markets often experience delayed or exaggerated reactions to news, leading to sustained price trends.
Such momentum can be exploited through systematic trading strategies—but also increases volatility and risk for passive holders.
Trading Volume and Volatility Signals
Beyond size and momentum, the paper investigates other potential predictors:
Trading Volume Strategies
Strategies based on price-to-volume ratios show promise:
- Going long on low price-volume cryptos and short on high price-volume ones yields 3.3% weekly excess return.
- This suggests inefficient pricing: high trading volume relative to price may indicate overhyped or speculative assets.
Volatility Anomaly
A counterintuitive pattern emerges with volatility:
- Low-volatility cryptos outperform high-volatility ones.
- A zero-cost strategy (long low-vol, short high-vol) earns 3.2% weekly excess return.
This contradicts traditional finance theory, which assumes higher risk (volatility) should be rewarded with higher return. Instead, it echoes the "low-volatility anomaly" seen in stocks—where safer assets beat riskier ones over time.
In crypto, this could reflect investor behavior: highly volatile tokens attract speculative attention but often fail to deliver sustainable growth.
Building a Cryptocurrency Three-Factor Model
Armed with these insights, the authors develop a cryptocurrency-specific factor model analogous to Fama-French.
The model includes:
- Crypto Market Factor (Rm − Rf): Excess return of the crypto market over risk-free rate.
- Crypto Size Factor (SMB): Small-minus-big portfolio returns.
- Crypto Momentum Factor (MOM): Winners-minus-losers portfolio returns.
When tested against 10 successful long-short strategies, this three-factor model explains nearly all observed return variation. No additional factors were needed to account for volume or volatility effects—suggesting size and momentum dominate.
Principal component analysis further confirms that the first two components closely match market and size/momentum factors, reinforcing their centrality.
Why This Research Matters Beyond Crypto
While the data covers a relatively short and volatile period—reflecting crypto’s early-stage nature—the implications extend far beyond digital assets.
As the authors note:
"Studying early cryptocurrency helps us understand the dynamics of new asset classes—not just crypto."
New markets often exhibit exaggerated versions of familiar phenomena:
- High speculation
- Information asymmetry
- Behavioral biases
By analyzing crypto now, we gain insight into how financial systems evolve—from inefficiency toward efficiency, from emotion-driven swings toward rational pricing.
Moreover, as blockchain-based assets (e.g., tokenized real estate, NFTs, DeFi instruments) proliferate, understanding foundational risk factors becomes essential for portfolio construction and risk management.
Limitations and Future Outlook
Despite its rigor, the study acknowledges several caveats:
- Sample period bias: Returns are exceptionally high compared to traditional markets—likely unsustainable long-term.
- Market immaturity: High levels of speculation, manipulation, and fraud may distort results.
- Factor stability: As markets mature, size and momentum effects may weaken or disappear.
Nonetheless, the core methodology stands: standard asset pricing tools can be meaningfully applied to crypto.
Future research should explore:
- Integration with macroeconomic factors
- On-chain data as predictive signals
- Cross-market spillovers between crypto and equities
Frequently Asked Questions (FAQ)
Q: What are the main risk factors in cryptocurrency returns?
A: The study identifies three primary drivers: market exposure, size (small-cap outperformance), and momentum (recent winners continue winning).
Q: Is the crypto three-factor model similar to Fama-French?
A: Yes—it mirrors the structure of equity models but is calibrated specifically for cryptocurrency dynamics using market, size, and momentum factors.
Q: Why do low-volatility cryptos outperform high-volatility ones?
A: This reflects a behavioral anomaly where extremely volatile assets attract speculative interest but fail to deliver consistent returns, while more stable cryptos build sustainable value.
Q: Can these strategies be implemented profitably today?
A: While historical returns were high, current market conditions may reduce profitability due to increased competition and market efficiency.
Q: Does this research apply to other digital assets like NFTs or tokenized securities?
A: The principles may extend to other nascent asset classes, especially those with speculative trading patterns and limited liquidity.
Q: How reliable are these findings given crypto’s short history?
A: The results are statistically robust within the sample period, but long-term validity depends on market evolution. They serve as a foundational framework rather than a final word.
Final Thoughts
The Journal of Finance paper on common risk factors in cryptocurrency marks a turning point in how we perceive digital assets—not as speculative novelties, but as analyzable financial instruments subject to measurable forces.
By identifying market, size, and momentum as key drivers of returns, the study paves the way for more sophisticated investment strategies, better risk modeling, and deeper academic inquiry.
For investors, this means opportunities lie not just in picking coins—but in understanding structural market dynamics. For researchers, it offers a template for studying future asset classes at their infancy.
As crypto continues maturing, models like this will become increasingly vital—not only for making money but for building a more resilient and rational financial ecosystem.