Analysis of Coinbase's "pull effect"

Original source: Messari

Original translation: free and easy, the way of DeFi< /p>

On Thursday night, Coinbase, the world's leading cryptocurrency exchange, announced that it will be listed directly on Nasdaq on April 14 under the stock code "COIN". According to previous private market transactions, Coinbase's valuation It is possible to exceed 100 billion US dollars, which will naturally cause a new market storm.

In addition to the battlefield of the stock market, the Coinbase listing event is also expected to To promote the influence of the exchange itself, messari researcher Roberto Talamas analyzed the "Coinbase effect".

The following is the translation:

When Coinbase Exchange lists a new asset, this It has become a well-known phenomenon that an event will have a very positive impact on the asset, which we call the "Coinbase Effect". The economics behind this effect are simple, when a crypto asset is listed on a popular exchange, it immediately reaches a new set of market participants, and when we examine this effect, Coinbase is the main In addition, in this report, we also expanded the scope of research to five other well-known exchanges to compare the effect of listing encrypted assets on different exchanges.

Furthermore, considering that Coinbase is the largest retail portal for cryptoassets, the study included a Event study using a sample of Coinbase listings in the Messari Enterprise API. Event analysis provides a better understanding of how listing events affect token returns, taking into account the impact of the market environment.

Coin listing effects on different exchanges

In the first part of the analysis, we use Messari's intelligence database collected listing information from six different centralized cryptoasset exchanges:

1. Coinbase (online 28 assets)
2. Binance (listed 22 assets)
3. FTX (listed 19 assets)
4. Gemini (listed 19 assets)
5. Kraken (11 assets listed)
6. OKEx (14 assets listed)
   
   

For each exchange, we will The cumulative return distribution of the 5 days after the listing of the token on the exchange is used as a rough proxy data to observe its effect. Not surprisingly, listing Coinbase has the highest average return at 91%, but it also has the widest spread of returns, from -32% to 645%.

In view of the above results, we can easily conclude that listing on Coinbase will bring higher returns than other exchanges. However, there are a few outliers in Coinbase’s sample that have seen huge returns following their listing announcements, skewing the mean significantly to the right of the return distribution.

Among these anomalies, District0x (platform for creating decentralized marketplaces and communities) and Civic (identity verification solution) token prices They rose 645% and 493%, respectively. By controlling for outliers in the Coinbase sample, we can more clearly compare the distribution of returns across exchanges.

On the face of it, these results suggest that listings on exchanges (especially Coinbase) tend to lead to increased returns on assets.

However, when analyzing the impact of events on asset prices, we need to consider another important The factor is the market environment in which these events occur. To address this question, we conduct an event study on a sample of Coinbase listings to explore the market conditions at the time of the announcement, in an attempt to decompose the attributable market reaction from the impact of the listing event.

Introduction to Event Study Methods

The Efficient Market Hypothesis (EMH) states that all publicly available information is immediately incorporated into the price of the asset. While a full explanation of the validity (or lack thereof) of this theory is beyond the scope of this report, we can build on this premise by examining how a particular event changes an asset by quantifying its impact on asset returns. market perception. Several academic researchers have proposed event study methodologies to address this type of analysis, focusing on understanding how events affect the statistical properties of asset returns.

The purpose of an event study is to quantify the economic impact of an event on "abnormal returns." Abnormal returns are calculated as the difference between the rate of return that would have been realized (called the normal rate of return) and the rate of return that would have been realized had the event not occurred.

While actual returns are easy to observe, normal returns require us to estimate. For this part of the analysis, the expected return model is usually used, which is also a commonly used valuation tool in other fields of financial research.

Market models are the most commonly used method of estimating normal returns in event studies. It estimates normal returns using actual returns in a reference market and the asset's correlation to the reference market. Formally, the abnormal return on a given day, expressed as the difference between the asset's actual return and the normal return, based on the typical relationship and correlation between the asset and a reference market (denoted α as β) and the reference asset estimated real rate of return.

So what's the point of doing all these extra steps if we're only looking at asset returns before and after an exchange listing to assess the impact of an event? A look at the intuition behind the model can help clarify this question.

Suppose Coinbase announces a new token listing on the exchange, as we would expect , tokens experience dramatic price increases after being listed on an exchange, so far, it’s not surprising. Let's also assume that on the same day, Bitcoin (a currency representing the entire market) realizes a 10% gain on some macro news. In this case, how can we be sure that the increase in the price of the token is due to the listing of the coin on the exchange and not due to the overall trend of the market? This is where the market model comes into play.

Using historical price data, we can study how their returns move together potential relationship between. In other words, if Bitcoin goes up 5% on a given day, what returns should we generally expect the coins to earn given their historical relationship? This relationship can be better visualized by plotting the daily returns of the two assets.

In this case, return the daily return on Ethereum (the token we are interested in) to the return on Bitcoin (our reference market), resulting in the fitted line shown above, which can then be used as a simple linear model to estimate the normal return event study required to execute that return. Following this approach, we can isolate the reaction to an exchange listing event by subtracting the market-wide effect from the asset return on the day the exchange was listed.

Experimental methods and results

For exchange trading To conduct research on coin listing events, we used Messari's intelligence database to collect data on tokens listed on Coinbase from May 14, 2020 to March 23, 2021, with a total of 28 encrypted assets. Since the research results rely heavily on accurate estimates of normal returns, we only list assets with at least one hundred days of data prior to listing to avoid introducing bias into the model. Additionally, the study focuses on analyzing abnormal returns of assets using the 15-day window before and after listing (known as the event window), so assets listed on Coinbase after March 8, 2021 were excluded from the study. After filtering by these criteria, only 14 assets met the research requirements.

To measure the overall impact of a go-live event, we sum all the individual abnormal returns to create Cumulative Abnormal Return Chart. As an example, the graph below plots Maker’s abnormal and cumulative abnormal return rates during the listing event window.

In the graph, the vertical line represents the abnormal return experienced on each day of the event window, the solid line represents the cumulative abnormal return, and the gray area represents the 99% confidence interval for . On the very day of the Coinbase listing, Maker experienced significant anomalous returns associated with the event, suggesting that the Coinbase listing had a strong economic impact on asset returns.

In an observational study of 14 assets, we found that 9 assets had The period showed statistically significant anomalous returns. Among these 9 assets, the accumulated abnormal return rate of 4 assets continued to grow positively after listing, which indicates that the listing of Coinbase has a continuous impact on the return rate of this asset.

Two of the assets had abnormal returns on the day Coinbase announced their listing. In this case, the price of the asset contains all the information about the listing, without seeing any additional reactions on the day of the listing.

The remaining three assets showed high positive abnormal returns before and after the event date, but quickly returned to normal, which shows that the listing No material impact on earnings.

Conclusion

Our analysis shows that the In other words, Coinbase’s listing announcement has the greatest positive impact on the asset’s average return. This result can be attributed to the popularity of Coinbase among retail investors, which makes it the most famous cryptocurrency trading platform.

In addition, the event research on the Coinbase listing shows that the listing market has an impact on the return on assets Strong effect, but not all observations agree. Of the 14 assets in the initial sample, only 9 assets showed statistically significant returns during the event window, and only 6 assets achieved higher positive returns.

Given these results, we can conclude that while a Coinbase listing has the potential to have a positive impact on asset returns, it does not Will affect all tokens in the same way.

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