How Retail Investors Actually Trade Crypto: 2026 Research

Why do most retail crypto traders underperform – even in a market that has delivered some of the highest returns in modern financial history?

Most retail traders believe they are following a strategy. In reality, their behavior is often reactive: they enter after price spikes, rely on social sentiment, and reduce exposure when markets turn against them. The problem is not only volatility. It is the gap between how traders think they make decisions and how they actually act under pressure.

At Traders Union, we set out to test this gap using real-world data. For this 2026 study, we surveyed 1,200 active retail crypto traders across North America, Europe, and Asia using the CAWI methodology (95% confidence level, ±2.8% margin of error). We compared those results with institutional research from the BIS, JPMorgan, and Coinbase Institutional to identify where retail behavior consistently breaks down and what traders can learn from it.

Findings

Opinions available in open sources

Analysis of institutional, on-chain, and market data reveals a consistent pattern in retail crypto investor behavior, although interpretations vary across major research providers.

According to the Bank for International Settlements (BIS), Working Paper No. 1049 – “Crypto trading and Bitcoin prices” retail participation in crypto markets is largely price-driven. The report states that rising Bitcoin prices are followed by the entry of new users, indicating that most retail investors enter the market after upward price movements rather than before them. This reactive behavior has measurable consequences: the same study estimates that 73–81% of retail investors have likely incurred losses, highlighting a structural disadvantage in timing.

This dynamic becomes even more visible during periods of market stress. The BIS Bulletin No. 69 – “Crypto shocks and retail losses” shows that during major events such as the Terra/Luna collapse and the FTX bankruptcy, large investors were selling while retail investors were buying, reinforcing the conclusion that retail capital tends to enter the market at unfavorable points in the cycle.

A similar pattern is observed in institutional banking data. According to the JPMorgan Institute report – “Crypto investor waves since 2017” retail trading activity is closely linked to Bitcoin price cycles. The report highlights that investor participation increases during periods of strong price growth and elevated volatility, suggesting that retail traders tend to react to market momentum rather than anticipate it.

Platform-level data provides additional context. Insights from Coinbase Institutional – Research & Insights show that while institutional activity is increasingly focused on risk management and structured positioning, retail trading remains concentrated in high-volatility periods and is strongly influenced by sentiment and price trends.

Key takeaways

Across all major sources – BIS, JPMorgan, and Coinbase Institutional – a consistent conclusion emerges:

• retail investors tend to enter late and follow price momentum;

• their decisions are largely reactive and sentiment-driven;

• they often buy during rallies and underperform during downturns.

At the same time, more experienced and institutional participants:

• position earlier in the cycle;

• apply structured strategies;

• and use volatility as an opportunity rather than a trigger.

Theoretical part of the research

From a theoretical perspective, retail trading behavior in crypto markets is shaped by three key factors:

• Behavioral biases. Fear of missing out (FOMO), loss aversion, and recency bias strongly influence decision-making.

• Information asymmetry. Retail traders often rely on simplified or delayed information, including social media signals and influencer opinions.

• Market structure. Unlike traditional markets, crypto operates 24/7, amplifying emotional trading and reducing structured decision-making.

Academic studies (SSRN, NBER) confirm that retail investors tend to:

• buy after price increases;

• sell during downturns;

• and overtrade during volatile periods.

Results of research (survey data)

To evaluate whether widely documented behavioral patterns in cryptocurrency markets are reflected in real-world trading activity, we conducted a proprietary quantitative study among retail investors in 2026.

The research was based on a structured online survey using the CAWI (Computer-Assisted Web Interviewing) methodology, allowing for standardized data collection across a diverse group of market participants.

• Sample size: 1,200 retail crypto traders.

• Geography: global (including North America, Europe, and Asia).

• Experience level: beginner to intermediate traders with at least six months of market activity.

• Confidence level: 95%.

• Margin of error: ±2.8%.

Participants were selected based on active engagement in cryptocurrency trading, ensuring that responses reflect real decision-making rather than theoretical assumptions.

Entry behavior

To better understand how retail traders enter the market, the survey examined the timing of position openings relative to price movements.

The results reveal a strong tendency toward reactive trading behavior, where decisions are driven by recent price action rather than forward-looking analysis.

• Buy after price increase: 64%.

• Buy the dip: 23%.

• Random entry: 13%.

Insight: The majority of traders enter positions after price momentum, confirming a strong tendency toward late entry and reduced ability to capture the full trend.

Trading drivers

To identify what actually influences trading decisions, the survey examined the primary factors retail traders rely on when entering or managing positions.

The data indicates a clear dominance of external and sentiment-driven inputs over structured analytical approaches. The majority of traders rely on easily accessible signals such as social media content and third-party opinions rather than independent research or structured frameworks.

Insight: The distribution highlights a significant imbalance in how retail traders approach decision-making.

• The majority (696 traders) rely on social media, making trading behavior highly dependent on sentiment, trends, and influencer-driven narratives.

• Only 252 traders use technical analysis, suggesting limited adoption of structured, chart-based strategies.

• Even fewer (168 traders) rely on fundamental analysis, which is typically associated with longer-term and more informed decision-making.

• A small group (84 traders) depend on external signals, often without full understanding of the underlying strategy.

From a behavioral perspective, this pattern reflects information asymmetry and herd behavior, where traders prioritize easily accessible inputs over independent analysis.

Risk management

To assess how retail traders manage downside risk, the survey examined the use of one of the most fundamental risk control tools – the stop-loss order.

The findings reveal a significant gap between basic risk management principles and actual trading behavior. Despite widespread awareness of stop-loss mechanisms, the majority of traders do not apply them in practice.

• Use stop-loss: 37%.

• No stop-loss: 63%.

Insight: The results highlight a critical weakness in retail trading discipline.

• A majority (756 traders) operate without predefined exit levels, exposing their capital to uncontrolled losses during adverse market movements.

• Only 444 traders consistently use stop-loss orders, indicating that structured risk management is not yet widely adopted.

From a behavioral perspective, this pattern is closely linked to loss aversion and emotional bias:

• traders avoid setting stop-losses to prevent realizing losses;

• but this often leads to larger drawdowns and delayed decision-making;

• especially in highly volatile crypto markets.

The absence of stop-loss usage also amplifies the impact of other behaviors identified in the study, including momentum chasing and short-term trading, creating a compounding effect on overall performance.

Trading style

To understand how retail traders approach the market over time, the survey examined the preferred trading horizon.

The analysis confirms a clear bias toward short-term strategies, with the majority of participants focusing on rapid trade execution rather than long-term positioning.

• Short-term trading: 61%.

• Long-term investing: 39%.

Insight: Retail traders predominantly favor short-term speculation over long-term positioning, increasing exposure to volatility and emotional decision-making.

The dominance reflects several structural and behavioral factors:

• Higher exposure to noise. Short-term traders are more sensitive to market fluctuations and intraday volatility.

• Increased emotional pressure. Frequent decision-making amplifies the impact of fear and greed.

• Lower consistency. Short-term strategies often lead to overtrading and unstable performance.

At the same time, only 468 traders in the sample follow a long-term approach, which is typically associated with:

• better alignment with fundamental trends;

• reduced transaction costs;

• and more stable performance over time.

This imbalance suggests that retail traders prioritize immediate opportunities over strategic positioning, which may contribute to underperformance in the long run.

Behavior during market declines

To understand how retail traders respond to adverse market conditions, the survey examined behavior during periods of price decline.

The results reveal a predominantly defensive and reactive approach, with most traders prioritizing capital preservation over strategic positioning.

• Sell during decline: 49%.

• Hold position: 34%.

• Buy the dip: 17%.

Insight: Nearly half of retail traders exit positions during downturns, indicating strong loss-aversion behavior and reinforcing procyclical trading patterns.

The distribution highlights a key structural weakness in retail trading behavior:

• A large group (588 traders) reacts to declines by closing positions, often locking in losses.

• A significant portion (408 traders) chooses to hold, which may reflect indecision rather than strategy.

• Only a minority (204 traders) actively buy the dip, a behavior typically associated with more experienced or systematic investors.

From a behavioral finance perspective, this aligns with the concept of loss aversion, where traders:

• prefer avoiding losses over maximizing gains;

• react emotionally to negative price movements;

• and abandon positions under pressure.

This behavior reinforces a classic procyclical pattern:

• entering markets during growth (as shown in Chart 1);

• and exiting during declines – often at unfavorable prices.

As a result, decision-making becomes inherently reactive. Traders respond to visible momentum, while more informed participants position themselves earlier – before price movements become obvious.

Incorporating capital flow analysis can partially address this gap. Monitoring large transactions, exchange inflows and outflows, and broader on-chain activity provides additional context about how major market participants behave across different phases of the cycle. Tools such as MTracer, which aggregate and visualize this type of data, illustrate how tracking “whale” activity can help identify accumulation or distribution before it is reflected in price.

At the same time, access to such data and overall trading conditions largely depends on the exchange a trader uses. Execution quality, liquidity, available tools, and analytics features can significantly influence how effectively traders interpret market signals and manage risk.

The table below highlights several major crypto exchanges and their key characteristics relevant for retail traders.

Simple ways to trade more effectively

To avoid the common mistakes highlighted in this research, traders should focus on a few practical principles:

• Define entry rules in advance — Avoid entering trades based on recent price spikes or emotions.

• Use stop-loss consistently — Set your risk before entering a position, not after.

• Reduce reliance on social media — Sentiment can provide context, but it shouldn’t replace analysis.

• Focus on quality over quantity — Fewer trades with clear setups tend to perform better.

• Look beyond price action — Understanding market structure and capital flow improves timing.

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Consistency in trading comes from process, not prediction

Andrey Mastykin Head of Company Reviews and Ratings

From my experience analyzing retail trading behavior, the most consistent issue is not access to tools, but the lack of structured decision-making. Many traders believe they are following a strategy, but when you analyze their actual trades, the patterns are clearly reactive. Entries are often driven by recent price movements or external noise rather than predefined criteria.

What separates more consistent traders is not necessarily better predictions, but better process. This includes defining entry and exit rules, managing risk per trade, and avoiding impulsive decisions based on short-term market sentiment. In crypto markets especially, where volatility is high and information flow is constant, discipline becomes the primary edge.

Conclusion

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Team that worked on the article

Anastasiia Chabaniuk

Educational Content Editor

Anastasiia has 17 years of experience in finance and content marketing. She believes that the support of information and expert opinion is very important for the success of investors and new traders.

Learn about our editorial policies

Dan Blystone

Senior English Editor

Dan Blystone began his trading career in 1998 as an arbitrage clerk on the floor of the Chicago Mercantile Exchange (CME). He later traded bond and Eurex futures at proprietary firms such as Altea Trading, gaining valuable experience in high-frequency trading and risk management.

Chinmay Soni

Head of Fact-Checking Department

Chinmay Soni is a financial analyst with more than 5 years of experience in working with stocks, Forex, derivatives, and other assets. As a founder of a boutique research firm and an active researcher, he covers various industries and fields, providing insights backed by statistical data.