This paper explores Bitcoin price volatility prediction by combining blockchain on-chain data with Whale Alert’s Twitter data. The authors employ a reinforcement learning approach (Q-learning) to classify and leverage Whale Alert tweets about large transactions. They report that merging Whale Alert tweet signals with on-chain metrics enhances the model’s ability to predict Bitcoin price trends and volatility. The results indicate that large-transfer alerts (like Whale Alert’s notifications) provide useful information for anticipating market movements, thereby confirming the practical value of Whale Alert data in volatility forecasting.

An academic study focusing on extreme volatility in Bitcoin, which integrates on-chain metrics with Whale Alert’s large-transaction tweets. The authors use a deep learning Transformer model and show that incorporating Whale Alert tweet data alongside on-chain analytics improves the prediction of next-day Bitcoin volatility spikes. The authors conclude that Whale Alert’s feed of big crypto movements serves as a valuable signal, helping the model outperform baselines in forecasting volatility.

This study leverages Whale Alert’s blockchain data to help identify and label major crypto addresses and uses wallets and entities identified by Whale Alert alongside other sources to link Bitcoin addresses to known entities, aiding analysis of the Bitcoin network’s structure and participants.

This paper uses Whale Alert as a data source for documenting hacking events and examines market effects of cryptocurrency cybercrime. It relies on Whale Alert’s public alerts to identify major hacking incidents and measure their impact on crypto prices.

Cites Whale Alert in analyzing the FTX collapse. The authors reference a Whale Alert report of 22,999,999 FTT (around US$585 million) transferred from an unknown wallet to Binance, an event first flagged by Whale Alert that signaled trouble at FTX. Whale Alert’s detection of this large transfer (later confirmed by Binance’s CEO) is used as a key timestamp in their study of market behavior during the FTX bankruptcy.

This paper investigates how Bitcoin’s price reacts when Tether (USDT) is minted or burned, and specifically examines the impact of Whale Alert Twitter announcements. The author finds that Bitcoin’s price response to Tether minting is significantly stronger when the event is publicly announced by Whale Alert on Twitter. In fact, investors do not react much to the minting itself until Whale Alert tweets about it, at which point Bitcoin sees a short-term price increase, especially under bullish market sentiment. This study is the first to demonstrate that Whale Alert’s real-time large-transfer alerts can influence cryptocurrency markets.

An industry whitepaper that evaluates whether alerts of large crypto transactions (such as those from Whale Alert) can be used as reliable trading signals. The report provides an overview of Whale Alert services – noting that Whale Alert (launched in 2018) became the pioneering tool for real-time tracking of large on-chain transactions across multiple blockchains. The authors analyze a dataset of big Bitcoin, Ether, and Solana transfers into exchanges (e.g. Binance) and measure subsequent price changes. The paper also discusses Whale Alert’s history and how its success spurred many copycat, underscoring Whale Alert’s prominence as the go-to crypto whale tracker.

This study examines how Twitter post sentiment and user engagement affect daily price moves of top cryptocurrencies (BTC, ETH, DOGE, ADA, XRP) during 2021–2022. While the paper’s main focus is on overall sentiment metrics, it mentions Whale Alert in a broader context as an example of influential crypto-related Twitter activity. Whale Alert is brought up as part of the discussion on market-moving news on Twitter, highlighting that high-impact alerts (e.g. large transaction announcements) are a notable component of crypto Twitter sentiment.