Cross-Sectional Momentum in Crypto: Does It Exist?

Introduction: The Enduring Enigma of Momentum Momentum is one of the most widely documented and robust anomalies in traditional finance. From Jegadeesh and Titman's seminal 1993 paper, "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," to countless subsequent studies, the propensity for past winners to continue winning and past losers to continue losing has been a persistent feature of equity, commodity, and currency markets globally. The underlying drivers are often attributed to behavioral biases (underreaction to news, herding) and structural market inefficiencies. However, the nascent, volatile, and often idiosyncratic crypto market presents a unique challenge to established quantitative paradigms. While time-series momentum (trend-following) has shown some efficacy in crypto, the cross-sectional variant – betting on the relative performance of assets within a universe – is less explored and, crucially, less understood. Does the "winner-takes-all" or "strong get stronger" dynamic translate effectively into the digital asset space? Or do the unique market microstructure, high idiosyncratic risk, and rapid narrative shifts in crypto invalidate this powerful factor? At Venym Labs, we are continuously pushing the boundaries of quantitative research in digital assets. Our mission is to identify robust, statistically significant, and economically meaningful alpha sources in this complex domain. This research delves deep into the existence and characteristics of cross-sectional momentum in a diverse universe of cryptocurrencies. We aim to provide a definitive quantitative assessment, leveraging rigorous methodologies and a high-fidelity dataset. Defining Cross-Sectional Momentum in Crypto Before diving into empirical analysis, it's crucial to precisely define what we mean by cross-sectional momentum in the context of cryptocurrencies. Unlike traditional equities where market capitalization often dictates universe selection and liquidity, crypto assets exhibit extreme heterogeneity in these dimensions. Our Working Definition: Cross-sectional momentum in crypto refers to the tendency for assets that have performed well relative to their peers over a lookback period (e.g., 1-month, 3-month) to continue to outperform, and for assets that have performed poorly to continue to underperform, over a subsequent holding period. Key Considerations for Crypto Momentum 1. Universe Construction: The crypto market is vast and constantly evolving. Including illiquid or newly launched tokens can introduce significant noise and survivorship bias. We focus on a dynamically maintained universe of the top N cryptocurrencies by market capitalization, ensuring sufficient liquidity and historical data. Our typical universe comprises the top 100-200 assets, excluding stablecoins and wrapped tokens. 2. Lookback and Holding Periods: Traditional momentum strategies often employ 3-12 month lookbacks and 1-month holding periods. In crypto, due to higher volatility and faster information dissemination, these periods may need adjustment. We explore various combinations, including shorter horizons (e.g., 1-week, 2-week, 1-month lookbacks with 1-week, 2-week holding periods). 3. Return Calculation: Daily log returns are standard. However, the exact time of day for price snapshots (e.g., UTC 00:00 vs. exchange-specific close) can matter due to fragmentation and differing trading hours globally. We use a consistent UTC 00:00 snapshot across major exchanges to mitigate this. 4. Momentum Metric: The simplest is raw historical return. Other variations like risk-adjusted returns (e.g., Sharpe ratio over the lookback) or idiosyncratic momentum (returns orthogonalized to market factors) can also be considered. For this study, we primarily focus on raw historical returns. 5. Portfolio Formation: Typically, assets are ranked by their momentum score, and portfolios are formed by going long the top quintile/decile (winners) and short the bottom quintile/decile (losers). Equal weighting, market-cap weighting, or volume-weighted strategies are all possibilities. We primarily use equal weighting to isolate the momentum effect, free from market-cap biases. Methodology and Data Our analysis spans a period from January 1, 2018, to December 31, 2023, encompassing multiple market cycles, including the 2018 bear market, 2021 bull run, and subsequent corrections. This period is critical for assessing the robustness of any factor. Data Sources High-frequency, aggregated historical price data from major centralized exchanges (Binance, Coinbase, Kraken, etc.) Market capitalization and volume data from reputable data providers (e.g., CoinGecko, CoinMarketCap APIs). Universe Filtering 1. Initial Universe: All cryptocurrencies with a market capitalization exceeding 1 million over the past month. 3. Price Filter: Minimum price of $0.01 to avoid micro-cap artifacts. 4. Exclusions: Stablecoins (USDT, USDC, DAI), wrapped tokens (WBTC, WE