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Latest cryptocurrency news > BITCOIN (BTC) > Crypto Influencers’ Signals Lag Behind Bitcoin Buys, Study Finds
BITCOIN (BTC)

Crypto Influencers’ Signals Lag Behind Bitcoin Buys, Study Finds

BH NEWS
Last updated: 9 November 2026 09:41
BH NEWS 12 seconds ago
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A recent study by Freekonomist Research reveals that tracking the trading signals of popular crypto influencers yields lower returns than simply buying Bitcoin on the same days. Analyzing 17,820 trade predictions shared across Telegram, YouTube, and X from 2017 to 2026, the study concluded that only 51.9% of the predictions were accurate. Among the 63 accounts evaluated, just 24 outperformed Bitcoin in the research simulation.

Contents
Bitcoin Buys Outpace Signal-Based TradingPublic Signal Analysis and Market Implications

Bitcoin Buys Outpace Signal-Based Trading

The researchers postulated a scenario where an investor starts with $1,000 and allocates $100 to every trade signal, without leverage. The simulation was compared to buying the same amount of Bitcoin on the same days. The investor following the signals ended up with a median balance of $1,180, whereas the Bitcoin buyer reached $1,272.

Investors who tracked 49 accounts saw their capital grow, but 23 out of 39 accounts with over 50% accuracy were still outperformed by Bitcoin. For instance, Il Capo of Crypto had a prediction accuracy of 62.3%; however, while his followers achieved a balance of $1,632, those buying Bitcoin hit $2,330.

Among the Telegram channels analyzed, only 14 out of 46 showed positive results. Similarly, on YouTube, 9 out of 16 channels were successful. English-speaking accounts had a better track record, with 19 out of 36 performing well, compared to only 5 out of 27 Russian-speaking accounts.

Public Signal Analysis and Market Implications

The study meticulously compared public trade signals with daily market prices, examining whether trades hit their target or stop-loss levels first. The sample comprised accounts with substantial market influence. Simulations were based on shared signals, excluding variables such as commissions, price slippage, funding costs, and taxes.

One key takeaway was the discrepancy between hit rates and returns. As the Il Capo of Crypto case illustrates, even with a high prediction accuracy, buying Bitcoin on the same days still outperformed the trade signals. This discrepancy highlights the challenges of relying solely on influencer signals for navigating the crypto market.

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