Macro trend analysis using altcoin correlations

How changes in the spread of altcoin correlations with bitcoin and ether can be used to model market sentiment and risk preferences.

Cryptocurrencies are highly correlated risk assets. Each token has a certain strength from which it derives some of its value, but with significant fiat inflows into the asset class, low supply availability, and the problem of not being able to value individual tokens, cash flows are spread across cryptocurrencies. The blind flow of money into valuable and useless protocols amplifies the high correlations in the space, creating noise and weakening the signal. But is there information in the noise itself?

By studying the many correlations across the space, we can filter out the noise of individual tokens to go back in history and see the compression of the industry as a whole (for example: the liquidity crisis caused by the coronavirus outbreak in early 2020, or China’s ban on cryptocurrencies in mid-2021). These squeezes tend to be reactive and unpredictable [1], but one can extend the analysis by looking at the change in the average bitcoin to average ether correlation spread and decipher whether the macroeconomic backdrop is moving in the direction of increasing risk or decreasing it.

The above graph is repeated in the footnotes to demonstrate correlations with ether [2].

We should expect that as highly correlated assets, a pair of parametrically defined correlation time series will closely follow each other (the two time series represent average correlations with equal weighting for bitcoin and ether). In the graph below, we see that they usually do, but the behavior when they don’t is of interest. Instead of looking at the absolute change in correlations, consider the increase and decrease in the spread between the correlation functions.

Comparative correlations of ether and bitcoin with top-25 coins. Even though the correlations closely follow each other, the spread can provide valuable information about the market’s risk appetite.

Now, by taking the spread between the two correlations, we find a profile of how the spread evolves over time. Despite the noisiness, a pattern emerges showing that although the spread between the correlations is widening, the market is generally in a risky environment, putting positive pressure on the price of all cryptocurrencies.

Similarly, as the spread shrinks, we take this as a sign of reduced risk, which usually results in significant corrections of all tokens. It is important to note that these compressions tend to occur at a time when industry-wide correlations to ether are rising, but because they have a higher baseline than bitcoin, we see faster growth in bitcoin and hence spread compression. We need to combine the two correlation profiles to extract predictive information about changes in the asset class risk profile.

The difference between the average correlation of altcoins with ETH and BTC. Bitcoin tends to rise as altcoins’ correlations with ether outperform their correlations with bitcoin.

We also note that because bitcoin and ether remain highly correlated assets (with correlations generally in the 0.6 to 0.9 range), there are some pretty strict spread limits. A spread that widens into the 0.2 range is a sign of extremely risky behavior, while any inversion should be taken as a signal that the market is driven by fear.

If we dig deeper into individual data and look at the top ten non-stablecoin coins by market capitalization, we see that their recent correlations with ether are higher than with bitcoin. This makes sense because they are all closer on the risk curve to ether than to bitcoin.

In contrast, if you go back in time to early May 2021, right before China’s mining ban, but also as many onchain metrics began to show a decline in bullish activity, you can see that as the correlation spread compressed, most of the major coins had inverse correlations.

Comparisons of the 60-day correlation before China’s mining ban

Below is my opinion on why inversions are interesting, but only hypothetically and require further study.

If one considers correlations with bitcoin as a market-wide force or wave that raises the price of all crypto-assets, then after adjusting for cross-correlation, their correlation with Ether properties unrelated to bitcoin becomes negative.

For those who view Etherium as the main driver of technological innovation in blockchain (as opposed to the recent general desire for Bitcoin to never change), these inversions suggest that the parallel innovations underlying the other major cryptocurrencies become short-term and useless. Thereby demonstrating that the value they offer is orthogonal to the value that Etherium brings to the ecosystem.

If the value propositions are largely orthogonal, this reinforces the view that Etherium does not have any significant direct competitors.

I think people who tend to focus solely on bitcoin or solely on Ether are discarding valuable macro information. Even though cryptocurrency price dynamics have corrected since early November, mostly due to risk reduction in the traditional market amid high inflation and the short-term end of quantitative easing, many major trends in the crypto space still seem promising.

I hope that the newly rising spread discussed in the current review remains a reliable indicator [3]. If I had to give a verdict, I would suggest that the current state of correlations looks more like July 2021 than March 2020, suggesting that we are more likely to be on the cusp of an upward move than continuing downward.

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