Original article from Presto Research
Compiled by Odaily Planet Daily Golem (@web3_golem)
Key points:
Whale alerts are popular because large on-chain transactions are often seen as a precursor to an impending token sell-off and a sell signal. To evaluate these claims, Presto Research analyzed the price changes of BTC, ETH, and SOL following large deposits to Binance.
According to the regression analysis, the R-squared values between large exchange deposits and subsequent price changes are low (ranging from 0.0017 to 0.0537). Narrowing the data to deposits from VCs and MMs (market makers) slightly improves the R-squared values, but their practical utility as trading signals is still limited. The results strongly suggest that whale deposits to exchanges lack predictive power as reliable trading signals.
On-chain metrics are effective in other ways, such as analyzing blockchain fundamentals, tracking illicit fund flows, or explaining price volatility. They will better serve the industry only when investors have more realistic expectations about the capabilities and limitations of these metrics.
One of the main distinctions between crypto assets and other assets is the public availability of transaction records, which are stored on a distributed ledger. The transparency of this blockchain has led to the emergence of various tools that leverage this unique characteristic, all categorized as 'on-chain data.' One such tool is 'Whale Alerts,' an automated service that notifies about large crypto transactions on-chain. They are popular because large transactions are often seen as precursors to impending sell-off activities, thus regarded by traders as 'sell signals.'
This report assesses the validity of this widely accepted hypothesis. After briefly outlining the popular whale alert services in the market, we will analyze the relationship between large deposit transactions and the prices of BTC, ETH, and SOL. Then, we will present the analysis results and provide key conclusions and recommendations.
Whale Alerts Overview
Whale Alerts refer to services that track and report large crypto transactions. These services emerged with the development of the crypto ecosystem, reflecting market participants' high recognition of the transparency features of blockchain.
History
As early Bitcoin adopters, miners, and investors (such as Satoshi Nakamoto, Winklevoss Twins, F2 Pool, Mt. Gox) accumulated a significant amount of Bitcoin, the term 'whale' began to gain popularity. Initially, blockchain enthusiasts monitored large transactions via blockchain explorers (like Blockchain.info) and shared this information on forums such as Bitcointalk or Reddit. This data was often used to explain significant fluctuations in Bitcoin's price.
During the 2017 bull market, as whale transactions and the number of large trades increased, the market urgently needed automated monitoring solutions. In 2018, a European development team launched a tool called 'Whale Alert,' which tracks large crypto transactions in real-time across multiple blockchains and sends alerts via X, Telegram, and web. The tool quickly gained favor among market participants, becoming the preferred service for those seeking actionable trading signals.
Source: Whale Alert (@whale_alert)
Fundamental Assumptions
Following the success of Whale Alert, many platforms offering similar services emerged over the years, as shown in the figure below. Although many new platforms have added more features to provide context for alerts, the original Whale Alert still focuses on simple, real-time notifications and remains the most popular service, as evidenced by its large following on X. A common feature of all these services is their reliance on the assumption that large on-chain transactions (especially exchange deposits) indicate impending sell-offs.
Mainstream whale alert services, sources: Whale Alert, Lookonchain, Glassnode, Santiment, X, Presto Research
Signal Validity Assessment
Supporters of Whale Alert services believe that on-chain asset transfers to exchanges often precede liquidations, making them effective sell signals. To validate this hypothesis, we analyzed the changes in digital asset prices following large deposits into exchanges; the following figure shows the key parameters of the analysis. The hypothesis is that if large deposit transactions can serve as reliable trading signals, a clear relationship should be observable between deposits and the corresponding asset prices.
Key parameters of the analysis, source: Presto Research
Assets, Exchanges, Analysis Periods, and Deposit Thresholds
Our analysis focuses on three major crypto assets—BTC, ETH, and SOL—and their USDT prices on Binance from January 1, 2021, to December 27, 2024. This time frame was chosen to align with Binance's current operational duration for aggregating deposits from wallet addresses.
The deposit threshold was set based on analysis of data from an exchange. Specifically, using Whale Alert's limits for whale deposits of $50 million for BTC, $50 million for ETH, and $20 million for SOL as benchmarks, we adjusted the deposit thresholds down to $20 million, $20 million, and $8 million respectively, aligning with Binance's 40% share of global spot trading volume.
Entity Types
We also specifically analyzed deposits from known entities and conducted the same analysis on a narrower data sample to check if deposits from specific types of entities exhibited a stronger relationship with price movements. These entities were identified through Arkham Intelligence and supplemented by our own investigations, as shown in the figure below.
Entities with known addresses, sources: Arkham Intelligence, Presto Research
Measuring Market Impact
To assess the potential sell-off pressure from whale deposits, we made the following assumptions:
Sell-off pressure manifests within a specific time frame after confirming deposits on-chain that exceed the threshold. We analyzed two time periods: one hour and six hours.
The maximum drawdown (MDD) within a specified interval is used as an indicator to measure the price impact of deposits (if any), effectively filtering out noise during that period.
Results
The analysis results are illustrated in the following charts
Impact of BTC Whale Deposits (All):
Source: Binance, Dune Analytics, Presto Research
Impact of BTC Whale Deposits (VC and MM only):
Source: Binance, Dune Analytics, Presto Research
Impact of ETH Whale Deposits (All):
Source: Binance, Dune Analytics, Presto Research
Impact of ETH Whale Deposits (VC and MM only):
Source: Binance, Dune Analytics, Presto Research
Impact of SOL Whale Deposits (All):
Source: Binance, Dune Analytics, Presto Research
Impact of SOL Whale Deposits (VC and MM only):
Source: Binance, Dune Analytics, Presto Research
Key Points
Source: Binance, Dune Analytics, Presto Research
The above chart summarizes the results of the statistics and draws the following 3 conclusions:
Large exchange deposits have weak predictive abilities for price drops: the R-squared values for all 12 scenarios show extremely weak predictive power, ranging from 0.0017 to 0.0537.
Deposits from VC and MM may serve as slightly better predictive signals: in this data subset, the R-squared values improved somewhat, though this improvement may merely be a result of reduced sample noise rather than a genuinely stronger correlation. Additionally, the absolute values remain low, indicating limited actual effectiveness as trading signals.
Whale deposits of ETH mainly come from VC and MM: they account for 61% of ETH whale deposits (i.e., 879 out of 538 transactions), while BTC accounts for only 13%, and SOL for 32%. This reflects the characteristics of different assets: ETH has a higher turnover rate due to its diverse Web3 uses (e.g., gas fees, staking, DeFi collateral, and swap medium), whereas BTC, as a store of value asset, is more stable.
Conclusion
Indeed, our analytical approach has certain limitations, and regression analysis has its inherent constraints; relying solely on R-squared values to draw conclusions can sometimes be misleading.
That said, the analysis combines context and individual observations, strongly indicating that whale deposits to exchanges lack sufficient predictive power to become reliable trading signals. This also provides us with profound insights into the broader use of on-chain metrics.
On-chain metrics are undoubtedly valuable tools, especially for analyzing blockchain fundamentals or tracking illicit fund flows; they may also be useful for explaining price movements retrospectively. However, using them to predict short-term price changes is entirely another matter. Prices are a function of supply and demand, and exchange deposits are just one of many factors affecting the supply side, even if they are genuinely useful. Price discovery is a complex process, influenced by fundamentals, market structure, behavioral factors (such as sentiment and expectations), and random noise.
In the highly volatile cryptocurrency market, participants are constantly seeking 'foolproof' trading strategies, and there will always be an audience captivated by the 'magic' of on-chain metrics. When some 'overzealous' data providers rush to exaggerate the promises of their platforms, on-chain metrics can better serve the industry only when investors have realistic expectations about their capabilities and limitations.
