When cryptocurrency prices swing wildly or slump for months, it is easy to assume that masses of everyday traders are blindly copying one another in a wave of panic or greed. Yet when financial economists have tried to measure this crowd mentality in digital asset markets, their studies have often come up empty or produced conflicting results.
A new study published in the Journal of Behavioral and Experimental Finance resolves this puzzle by analyzing both daily price trends and millions of split-second transactions. The research reveals that crowd-following behavior is widespread in crypto, peaks during calm market slumps rather than chaotic crashes, and is frequently steered by sophisticated traders rather than just confused amateurs.
The puzzle of the missing crypto herd
Karthik Natashekara and Aravind Sampath of the Indian Institute of Management Kozhikode set out to understand why previous academic studies struggled to pin down “herding” in cryptocurrencies. In financial markets, herding happens when investors set aside their own research or beliefs and simply mimic what they see others doing.
Cryptocurrency markets have all the textbook ingredients that normally encourage investors to follow the crowd. Digital tokens lack universally agreed-upon fundamental values, attract heavy speculation, suffer from uneven access to information, and trade around the clock with relatively little institutional oversight.
Natashekara and Sampath identified two main reasons why earlier studies missed the herd. First, the standard mathematical formula used to detect market-wide herding relies on traditional stock-pricing theories that do not hold up well in the real world, creating a statistical blind spot that hides herding even when it is happening. Second, most researchers only looked at daily closing prices, missing the fast-paced, minute-by-minute trading where modern algorithmic and professional players operate.
Why calm markets breed stronger crowds
To test their ideas at the broad market level, the researchers collected daily and weekly price data for 62 major cryptocurrencies from CoinMarketCap, covering 2,291 trading days from January 1, 2018, to April 9, 2024. Each digital currency in the sample had a market value of at least $100 million, and together they represented the vast majority of the total crypto market.
In daily price data, researchers detect herding by measuring how closely individual assets move together. Under normal conditions, different coins move at different speeds based on their own unique news, widening the gap between individual coin returns and the overall market average; when investors herd, every coin moves in lockstep and that gap shrinks.
When Natashekara and Sampath applied the traditional, flawed formula to their dataset, it showed zero evidence of herding. When they switched to an updated statistical method that corrects for the pricing-model blind spot, they found strong, consistent herding across the entire six-year timeline, across weekly data, and during major market shocks such as China’s 2021 crypto mining ban, the 2022 collapse of the FTX exchange, and the 2024 approval of spot Bitcoin exchange-traded funds (ETFs).
Next, the authors divided the timeline into periods of low, medium, and high market volatility to see when the herd mentality was strongest. Conventional wisdom suggests that investors flock together during wild, high-volatility panics, but the data showed the exact opposite.
Herding was strongest during periods of low volatility, such as the prolonged, sluggish “crypto winter” of 2022 and 2023, and grew weaker as market turbulence increased. The researchers interpret this to mean that herding in cryptocurrency is primarily a response to market stagnation and lingering uncertainty, where investors cluster around a gloomy consensus during quiet downturns rather than during sudden, chaotic crashes.
Following the biggest names
To see which specific digital assets attract the biggest crowds, the researchers sorted the 62 cryptocurrencies into three groups based on their “idiosyncratic volatility.” This term refers to price swings driven by factors unique to a single coin, as opposed to movements driven by the broader crypto market.
Their analysis revealed that herding was concentrated in the group of coins with the lowest coin-specific volatility, while the most erratic, high-volatility coins showed no statistically significant herding. Because the low-volatility group contained the largest and most famous tokens, the researchers ran the test a second time after removing five industry giants: Bitcoin, Ether, XRP, Litecoin, and Bitcoin Cash.
Once those five household names were stripped out of the low-volatility group, the herding effect disappeared entirely. Natashekara and Sampath argue that this points to a two-step behavioral process during quiet market periods.
First, sophisticated investors consolidate their portfolios into the largest, most liquid cryptocurrencies, establishing those tokens as dominant market benchmarks. Everyday retail traders, who face high search costs and lack the time to research hundreds of obscure tokens, then pile into those same high-profile market leaders because they dominate online discussions and news coverage.
How sophisticated traders steer the crowd
Daily prices can show whether the whole market is moving together, but they cannot reveal who is copying whom during the trading day. To observe trader behavior up close, the researchers analyzed more than 112.26 million individual, tick-by-tick transactions on the Bitstamp exchange between February 2018 and March 2024, focusing on Bitcoin, Ether, XRP, Litecoin, and Bitcoin Cash.
They measured intraday herding by counting “runs,” which are unbroken streaks of trades where the price moves in the same direction, whether upward, downward, or flat with no price change. When these streaks last much longer than random chance would predict, it signals an “informational cascade,” a chain reaction where less-informed traders abandon their own views and copy the trades unfolding in front of them.
Across all five cryptocurrencies, these trading streaks were far longer than random chance would allow, confirming intense intraday herding, with Bitcoin showing the strongest crowd behavior and Litecoin the weakest. The researchers also tracked “informed trading” by measuring imbalances between buying and selling volume, a standard microstructure technique used to estimate when traders with superior information are active in the market.
Comparing these informed trading signals against the transaction streaks revealed that herding is self-reinforcing, meaning that a strong herd on one day is linked to more herding the next day. High overall trading turnover was also associated with stronger herding, whereas coins with higher network value relative to their daily transaction volume experienced less herding.
When the researchers examined how informed traders interact with existing herds, they found that the outcome depended on what the broader market was doing. When a cryptocurrency was already experiencing a herding streak and prices were actively trending upward or downward, a rise in informed trading was linked to even stronger herding.
Conversely, when prices were flat and the market lacked a clear direction, an influx of informed trading was associated with a drop in herding behavior. The authors verified these intraday patterns using alternative measures of informed trading and a separate dataset of 22 cryptocurrencies traded on the Gemini exchange from May 2021 to March 2024.
What this means for investors and regulators
Based on these transaction patterns, the researchers argue that behavioral finance needs to rethink the conventional view that crypto herding is purely the result of irrational retail traders chasing hype. Instead, they describe informed traders as “strategic chameleons” who intentionally amplify price momentum when a trend is underway to profit from predictable crowd behavior, only to step in and stabilize the market when prices stall.
For everyday retail investors, the authors note that the practical challenge lies in distinguishing between blind crowd noise and momentum driven by informed players. They suggest that individual traders can benefit by riding market momentum when sophisticated traders are actively fueling a trend, while anticipating price reversals when informed traders step into stagnant markets.
For institutional investors and regulators, the findings indicate that market risk shifts depending on whether prices are trending or flat, requiring dynamic hedging strategies rather than static risk models. Regulators can also use these transaction-level tools to monitor whether sudden price swings reflect natural crowd enthusiasm or coordinated manipulation and pump-and-dump schemes orchestrated by big players.
At the same time, the study highlights a major caveat for analysts who rely on only one type of data to judge market behavior. During the 2022 and 2023 crypto winter, daily return gaps shrank, indicating strong herding, while intraday transaction streaks pointed to weaker herding on low-volume days.
Natashekara and Sampath explain that when liquidity dries up during a prolonged slump, prices across different coins can drift downward together mechanically in reaction to shared bad news, even without active buying and selling. Relying solely on daily price charts during a liquidity crisis can lead observers to mistake a state of market paralysis for an active stampede of traders.
