In recent months, a burgeoning phenomenon has emerged around the intricate world of prediction markets, particularly the practice of “whale-watching” on platforms such as Kalshi and Polymarket. This practice involves individuals paying to track the trades of more experienced, well-resourced investors—commonly referred to as “whales”—in the hopes of replicating their successes.
Whale-watching has become a cornerstone of the prediction market ecosystem, allowing traders to tap into insights from those who possess a greater understanding of market dynamics. While not all whales are leading insiders, many are self-described “sharps”—discipline-driven traders with significant resources at their disposal. This created an environment where followers analyze large trades for potential signals, often leveraging automated bots to assist in their trading decisions.
Edward Ridgely, CEO and co-founder of Stand—a prediction-market aggregator—states that the activity is very noticeable on platforms like Polymarket. He notes that many of the top performers on their builders leaderboard are Telegram copy-trading bots, which have gained traction in engaging real-time trade alerts. Ridgely’s platform tracks whale activity, categorizing them based on the size of their trades: whales (over $5,000), dolphins ($1,000 to $5,000), and shrimp ($500 to $1,000).
A noteworthy example of whale activity came last August, when Ridgely observed a surge of bets on the question, “Will Taylor Swift be engaged?” Sharing this information with his fiancée led her to dismiss the notion as mere speculation. However, just thirty minutes later, Swift confirmed her engagement on Instagram, prompting a shocked response from Ridgely’s fiancée.
Yet, as the number of copy traders increases, well-informed whales adopt tactics to safeguard their positions. To counteract the risks associated with being shadowed, they might establish multiple wallets or manipulate trades by executing iceberg orders—buying positions while gradually accumulating the other side. As Ridgely puts it, while one can mimic trading actions, discerning the intent behind them remains elusive.
Despite the allure of copy-trading, some individuals prefer more traditional decision-making methods. One trader opted for a manually researched approach during Oscar night, cross-referencing insights from Oscars expert Matt Neglia with the odds presented on Kalshi. Choosing a balanced mix of favorites and underdogs, the trader injected a sentimentality into their picks, reflecting personal connections to nominees.
As the Oscar announcements began, an intriguing pattern emerged: about 20 seconds before each award was revealed, the odds for one nominee would mysteriously spike, foreshadowing their win. This observation pointed to potential latency arbitrage, where individuals receiving insider information or having direct access to the award results would capitalize on the delay in public knowledge.
By the end of the evening, the trader had achieved mixed results—winning two bets and losing two—but found the experience exhilarating and educational. It highlighted the engaging complexities of prediction markets and the gradual evolution of a landscape where information and intuition collide, shaping the way individuals strategies are formulated and decisions made in the world of trading.
