Can On-Chain Data Predict Bitcoin Cycles? – Securities.io

Financial markets are moved by the psychology and actions of institutions and retail investors. This means that analyzing transaction volume, patterns and other data can be extremely valuable to traders and investors in predicting future price movements.

Bitcoin has been one of the most successful new asset categories in history, and differs from traditional assets like stocks and gold in a few ways, such as instant transactions and mathematically limited supply. One less discussed difference is how transparent the market and transactions of Bitcoin and cryptocurrencies in general are.

In theory, this could give investors another way to study market cycles, with each transaction registered and “memorized” open for access in the public ledger.

A new economic research paper published by researchers at the University of Vaasa (Finland) and the University of Turin (Italy) explores the potential of this method. It was published in Research In International Business and Finance1, under the title “Using on-chain data to predict Bitcoin cycles”.

Forecasting markets

Because predicting financial market prices can be extremely profitable, a lot of attention has been given to this topic. However, traditional financial models struggle to explain the price movements of crypto-assets.

This is because, unlike stocks, cryptos do not have intrinsic value tied to a company and potential future dividends. Likewise, they are not the same as national currencies, influenced by the central bank’s decision and a national economy’s strength or weakness.

Instead, the price of cryptos is largely sentiment driven, even if the underlying utility for transactions or as a store of value is of course the deeper reason for cryptocurrencies’ value.

In traditional markets, sentiment-driven price movements are typically derived from indirect proxies such as surveys or media-based indicators. But blockchains provide a transparent and tamper-proof ledger of transactions, providing a verifiable record of investor behavior.

To answer the question of whether on-chain data is useful for predicting Bitcoin prices, the researchers used three on-chain, trade-based measures. They measured them over three major market cycles.

Gauge Bitcoin’s sentiment

Metrics Overview

The researchers analyzed Bitcoin prices from December 7, 2013 to April 12, 2025, spanning three complete market cycles: 2015, 2018, and 2022.

The three indicators used in this study are:

Net unrealized profit/loss (NUPL) ratio Market value to realized value Z-score (MVRV Z-score) Cumulative value Days destroyed (CVDD).

The first two measures relate prices to holders’ total cost base (realized value) and can be interpreted by behavioral finance mechanisms.

CVDD reflects the behavior of long-term holders as it captures the spending of long-held coins and thus provides information on capitulation by long-term holders during periods of extreme pessimism.

Generally, the idea is to gauge investors’ sentiments, where over-optimism can cause excessive risk-taking and price run-up that can develop into bubbles, which then burst when investors panic, and prices fall well below intrinsic value.

In cryptos, search engine activity and social media are among the most prominent sources of sentiment analysis. But data across the chain finally contains the proof that such sentiment translates into action.

Net unrealized gain/loss ratio

The NUPL ratio approximates the share of coins currently held at an unrealized profit or loss.

As such, high values ​​(above 0.75) imply a potential market top, with euphoric sentiment leading to significant unrealized gains. Similarly, low values ​​are typically linked to fear and capitulation at a market bottom.

Market Value to Realized Value Z Score

MVRV Z-score determines whether a coin is undervalued or overvalued relative to its “fair value”, and is a widely used on-chain metric.

To do this, it combines 3 measures:

Market Value (MV): Bitcoin price multiplied by the number of coins in circulation. Realized Value (RV): Values ​​each coin at the price at which it was last transferred to the chain and sums over all coins in circulation Z-score: Standardizes the deviation between MV and RV by the standard deviation of market value

This indicator indicates that market participants hold large unrealized profits during bull market phases, when Bitcoin’s market value rises significantly above its realized value.

A score below -0.2 is considered a state of increased fear and uncertainty. An exit threshold of 5-7 indicates that the average participant has large unrealized profits, generating the behavioral pressure to take profits that historically coincides with cycle tops.

Cumulative value days destroyed

CVDD is built on Coin Days Destroyed (CDD), a metric that weights transactions by both the amount of coins moved and how long they were held.

More precisely, it measures the number of coins transferred multiplied by the number of days since those coins were last moved. CVDD collects this activity over time

This can be especially useful for measuring the market bottom, as it determines when long-term holders capitulate.

Can on-chain data predict Bitcoin price?

Published results

Several tested NULP strategies all outperformed a buy-and-hold strategy. In addition to higher yields, they all also showed smaller withdrawals. The most aggressive NULP strategy turned out to be the most profitable.

MVRV Z-score also demonstrated superior and robust risk-adjusted performance relative to the buy-and-hold benchmark. They outperformed the NUPL-based strategies across all metrics, albeit with a little extra volatility in some cases.

The CVDD strategies are proven capable of identifying cycle bottoms across all trades and window ranges, beating most random entries.

With a p-value of 99%, this suggests that although CVDD typically comes in very close to the bottom, its holding periods are sometimes longer than ideal, reducing annualized performance.

These results indicate that all three measures contain predictive value, with MVRV Z-score providing the strongest overall risk-adjusted performance and CVDD appearing particularly informative for identifying market bottoms.

Overall, the study indicates that yes, on-chain data contains economically meaningful information about Bitcoin market behavior.

Restrictions

It should not come as a surprise that market indicators of an overbought or oversold situation of Bitcoin markets help to trade better than a buy and hold strategy. After all, if such indicators offered no extra benefit, traders would have stopped using them long ago.

However, they are not a crystal ball, and most likely a more sophisticated approach combining multiple indicators will have superior performance, including types other than on-chain indicators.

The research paper also acknowledges that more work is needed to analyze the link between on-chain data and prices for other assets, such as Ethereum, Solana and XRP.

Similarly, other on-chain statistics have yet to be scientifically evaluated.

AI disruption?

Finally, the rise of LLMs (Large Language Model) and AI in general may disrupt the pattern tested from 2013.

LLMs are increasingly used by retail and institutional investors to interpret market conditions and process information, with the potential to reinforce behavioral biases. This can radically change the dynamics of the sentiment signals on the chain investigated here.

So, crypto investors should be careful not to get too confident in the reliability of indicators that have worked in the past, as markets are constantly evolving, even more so today, as new analytical tools such as AI can also change the structure of markets.

So, as always in investing, diversification and remembering that “past performance is not proof of future results” will matter.

Study Reference

1. Klaus Grobys, Sebastian Näsman and Davide Sandretto. Using on-chain data to predict Bitcoin cycles. Research in International Business and Finance. September 2026. Article: 103486. Volume: Volume 89. 10.1016/j.ribaf.2026.103486.