Prediction markets hit record weekly trading volumes last week as traders increasingly move to bet on major political events, crypto-related outcomes, and sports markets.

The growing activity reflects rising interest in event-based trading across multiple sectors. However, this rapid expansion has also brought renewed concerns over market fragmentation and insider trading.

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Prediction Markets Break Volume Records While Trader Gains and Losses Mount

According to Dune data, prediction markets posted a record $3.7 billion in weekly trading volume last week, marking an all-time high. Weekly notional volume also surged to a new record of $5.57 billion.

The acceleration extends a trend that began in 2025, when prediction market activity started to outpace trading volumes in meme coins and non-fungible tokens (NFTs).

Prediction Market Weekly Notional Volume. Source: Dune

User engagement has also risen. Weekly active users peaked at 335,583 in the first week of January, while transaction counts followed a similar upward trajectory.

Data shows that activity remains quite concentrated, with three categories accounting for the majority of weekly notional volume. On Polymarket, trading is primarily driven by political events, sports, and crypto-related markets. Kalshi has exhibited a comparable pattern.

This concentration is also reflected at the individual trader level. Lookonchain reported that a Polymarket trader “beachboy4” staged a dramatic turnaround, moving from more than $6.8 million in losses to approximately $395,000 in profit after betting on sports outcomes.

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Over the past two days alone, the trader reportedly profited more than $10.5 million across five successful predictions, fully recovering earlier losses.

“That said, his bet sizes have surged — from a few hundred thousand per bet before to over $3 million on a single bet now” the post added.

However, not all traders have seen similar results. On Polymarket, 2 users lost nearly $10 million in less than a month, highlighting the risks associated with event-based markets.

“2 Polymarket traders bet heavily on sports markets at 48¢–57¢, and lost nearly $10M in less than a month. 0x4924: 346 predictions, 46.24% win rate, -$5.96M in 24 days. bossoskil1: 65 predictions, 41.54% win rate, -$4.04M in 11 days At ~50¢ odds, you’re basically flipping a coin. Betting big just means losing faster,” Lookonchain noted.

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Beyond retail users, major industry players are also moving to capitalize on the trend. Coinbase is reportedly preparing to launch its own prediction markets. Moreover, a Gemini affiliate has secured regulatory approval to begin offering prediction markets to US customers.

Trump Media & Technology Group has likewise signaled intentions to enter the space. In December, Fanatics, a sports platform, announced the launch of a fan-led prediction market platform through a strategic partnership with Crypto.com.

Growing Concerns Surround Prediction Markets

However, some experts have raised concerns regarding the surge in markets, calling it the “endgame” for the sector.

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Others argue that the number of markets is not the core issue. Instead, liquidity remains the biggest challenge facing prediction markets.

“All this will incentivize is just a ton of 0 liquidity markets people are spamming to make 5 cents of creator fees,” Alex Finn stated.

Beyond fragmentation, insider trading has emerged as another pressing concern for prediction markets. A series of recent episodes has raised questions about whether non-public information is shaping market outcomes.

In one case, three wallets collectively recorded profits of more than $630,000 on Polymarket after wagering on the removal of Nicolás Maduro before his arrest was announced. Elsewhere, a trader reportedly generated close to $1 million from bets tied to Google’s 2025 “Year in Search” results.

Similar patterns were observed around entertainment events. On Polymarket, users placed 27 wagers on Golden Globe Award outcomes, with 26 settling in the money. These unusually accurate results have fueled growing concerns that insider knowledge may be influencing activity on prediction platforms.

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Author: Kamina Bashir

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