The original text comes 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 precursors to impending token sell-offs and sell signals. To evaluate these claims, Presto Research analyzed the price changes of BTC, ETH, and SOL after large deposits to Binance.

• According to regression analysis, the R-squared value between large transaction deposits and subsequent price changes is low (ranging from 0.0017 to 0.0537). Narrowing the data to deposits from VCs and MMs (market makers) slightly improved the R-squared value, but the actual utility of using them as trading signals remains limited. The findings strongly suggest that whale deposits to exchanges lack predictive power as reliable trading signals.

• On-chain indicators are effective in other areas, such as analyzing blockchain fundamentals, tracking illicit fund flows, or explaining price fluctuations. They will better serve the industry only when investors have more realistic expectations of these indicators' capabilities and limitations.

One of the main differences between crypto assets and other assets is the public availability of their 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 classified as "on-chain data." One such tool is "whale alerts," an automated service that notifies users of large crypto transactions on-chain. They are popular because large transactions are often seen as precursors to impending sell-offs, 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 transaction deposits and the prices of BTC, ETH, and SOL. We will then 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 as the crypto ecosystem developed, reflecting market participants' high recognition of the transparency characteristics of blockchain.

History

As early Bitcoin adopters, miners, and investors (such as Satoshi Nakamoto, Winklevoss Twins, F2Pool, Mt. Gox) accumulated large amounts of Bitcoin, the term "whale" became popular. Initially, blockchain enthusiasts monitored large transactions through blockchain explorers (like Blockchain.info) and shared this information on forums like Bitcointalk or Reddit. This data was often used to explain significant fluctuations in Bitcoin prices.

During the 2017 bull market, as whale transactions and large transaction volumes increased, there was an urgent need for automated monitoring solutions in the market. In 2018, a European development team launched a tool called "Whale Alert," which could track large crypto transactions across multiple blockchains in real-time and send 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)

Basic Assumptions

Following the success of Whale Alert, many platforms offering similar services have 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 characteristic of all these services is that they rely on the assumption that large on-chain transactions (especially exchange deposits) signal impending sell-offs.

Mainstream whale alert services, source: Whale Alert, Lookonchain, Glassnode, Santiment, X, Presto Research

Signal Effectiveness 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 price changes of digital assets following large deposits to exchanges, with the following figure illustrating the key parameters of the analysis. The hypothesis is that if large transaction deposits 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 Period, 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 the operational duration of the wallet addresses currently used by Binance to aggregate deposits.

The deposit thresholds were set based on an analysis of exchange data. Specifically, using Whale Alert's thresholds of $50 million for BTC and ETH and $20 million for SOL as a baseline, we adjusted the deposit thresholds down to $20 million, $20 million, and $8 million, respectively, which corresponds to 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 whether deposits from specific types of entities exhibited a stronger relationship with price movements. These entities were identified by Arkham Intelligence and supplemented by our own investigations, as shown in the figure below.

Entities with known addresses, source: 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 will manifest within a specific time frame after confirming on-chain deposits exceeding the threshold. We analyzed two time periods: one hour and six hours.

• The maximum drawdown (MDD) within the 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 figures:

• Impact of BTC whale deposits (all):

Source: Binance, Dune Analytics, Presto Research

• Impact of BTC whale deposits (VCs and MMs only):

Source: Binance, Dune Analytics, Presto Research

• Impact of ETH whale deposits (all):

Source: Binance, Dune Analytics, Presto Research

• Impact of ETH whale deposits (VCs and MMs only):

Source: Binance, Dune Analytics, Presto Research

• Impact of SOL whale deposits (all):

Source: Binance, Dune Analytics, Presto Research

• Impact of SOL whale deposits (VCs and MMs only):

Source: Binance, Dune Analytics, Presto Research

Key Points

Data Source: Binance, Dune Analytics, Presto Research

The above figure summarizes the results of the statistics and draws the following three conclusions:

1. The predictive power of large exchange deposits for price declines is weak: The R-squared values for all 12 scenarios show extremely weak predictive power, ranging from 0.0017 to 0.0537.

2. Deposits from VCs and MMs may be slightly better predictive signals: In this subset of data, the R-squared values improved, but this improvement may simply 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.

3. Whale deposits in ETH primarily come from VCs and MMs: They account for 61% of ETH whale deposits (i.e., 538 out of 879 transactions), while BTC accounts for only 13% and SOL for 32%. This reflects the characteristics of different assets: ETH has a higher turnover due to its diverse Web3 uses (e.g., gas fees, staking, DeFi collateral, and swap medium), while BTC is more stable as a store of value asset.

Conclusion

Admittedly, 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, combined with context and individual observations, strongly suggests that whale deposits to exchanges lack sufficient predictive power to serve as reliable trading signals. This also provides us with profound insights into the broader use of on-chain indicators.

On-chain indicators are undoubtedly valuable tools, especially for analyzing blockchain fundamentals or tracking illicit fund flows, and they may also be useful for explaining price movements in hindsight. However, using them to predict short-term price changes is an entirely different 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 drawn to the "magic" of on-chain indicators. When some "overzealous" data providers rush to exaggerate the promises of their platforms, on-chain indicators can better serve the industry only when investors have realistic expectations of these tools' capabilities and limitations.

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