BTC price trend has a strong correlation with the number of active addresses, but this is not true for all tokens

You might think that the tokens of the most active blockchain networks would also be the most in demand. But is this really the case? How closely is the price of a token tied to the activity of its network?

To find out, LongHash analyzed daily price data and address data for 18 top tokens (excluding stablecoins) on Coin Metrics. Specifically, we looked at the daily price of each token on Coin Metrics, which tracks the closing price of each asset in USD (i.e., the price at midnight UTC); and the total number of active addresses on Coin Metrics, which represents the total number of unique addresses that participated in at least one transaction on the network that day (but each unique address is only counted once a day).

Then, for each asset, we calculated the Pearson correlation coefficient of price and active addresses across all available data from Coin Metrics. Some coins have more data because the site started tracking each coin at different times, but all but one (BSV) have correlation coefficients based on at least one full year of data, with most having much longer data than a year.

The Pearson correlation coefficient is expressed as a number between -1 and 1, where -1 indicates a perfect negative correlation (i.e., when one value increases, the other decreases) and 1 indicates a perfect positive correlation (i.e., when one value increases, the other also increases). Values ​​above 0.3 or below -0.3 are generally considered to indicate some degree of correlation, while values ​​above 0.7 or below -0.7 indicate a strong correlation.

As can be seen in the chart above, the price action of some tokens seems to have a strong correlation with the number of active addresses. BTC, LTC, LINK, and NEO all meet this requirement, which shows that when more users are transacting on these networks, the price of the corresponding tokens will generally increase.

While no coin has a clear negative correlation between price and active addresses, there are quite a few that do not have a clear correlation between price and active addresses. BSV, XLM, TRON, TEZOS, ETC, MAKER, XEM, and BAT all fall into this category. At least according to Coin Metrics, there does not seem to be a correlation between increased user activity on these networks and increased prices.

It is worth noting that Ethereum falls into this category, with at best a weak correlation between active users and price. This may be due to Ethereum’s positioning as “digital oil” to drive the development of DApps developed on its network, which means that only a small number of Ethereum users will regularly participate in transactions that most directly drive price increases. For example, sending an ETH to a DApp will cause the account to be counted in Coin Metrics’ total number of ETH active users for the day, but it will not affect the price of ETH on exchanges the same way that TUSD trading against BTC affects the price of BTC.

While total active users may not be an effective indicator for predicting intraday trading prices, in the longer term it appears that some tokens like LTC do show a strong correlation between their token price and the number of active users trading on the network on a given day.

From a broader perspective, though, the data suggests that network activity and token prices aren’t always tightly correlated in the short term. This means that a surge in user activity doesn’t necessarily lead to a surge in price, especially when it happens on a token network like MAKER, which has historically had very little correlation between the number of active users and token price.

This article comes from: LongHash