Analyze user behavior after the incentives using protocol data
Disclaimer: The content of this post is purely informative and cannot be considered an investment recommendation. The analyzed data may content errors
Since Compound launched its rewards with its COMP governance token, the entire crypto world has been revolutionized and the term “liquidity mining” is increasingly used talking about. This protocol rewards all users who leave liquidity in their protocol, or borrow from it, with governance tokens, dividing the rewards by 50% between them.
In this post, we are going to see how the data has changed since this new reward was included, how users are behaving, and if looking for this reward is making them take more risks when applying for loans.
The data has been obtained from the Compound graphQL api as I commented in this post.
Number of loans
Compound rewards both users who deposit liquidity in the protocol, and those who request loans in it, which has led to the creation of strategies, where a collateral is deposited and, with it, a loan is requested, receiving tokens both per deposit and borrow. Looking at the data on requested loans we see that they have increased significantly since incentives are given
To try to analyze if these loans are riskier and generate an illiquidity problem in the protocol, where the liquidation of one can drag others, we will see different issues about them.
One of the factors that can cause a loan to be liquidated is the volatility of the crypto asset pair involved, as a rapid rise in the price of the principal crypto asset or a drop in the price of the collateral can cause the loan liquidation, therefore users are interested in pairs being less volatile as possible. The strategies that we can find in Internet talk about using stablecoins to mitigate this volatility. If we look at the data, are the most requested borrows in these currencies?
It seems the increase in stablecoins has been great, but the most requested is BAT, in which the volatility against ETH, which is what the collateral is valued at, is higher, with a higher potential risk
Another way in which Loans can be liquidated, is because the interests grow and increase the debt on the loan, until the liquidity threshold falls. In some strategies, it was commented that when receiving COMP, the interests were covered, since they were paid at the price of the COMP. If we look at the data, are the users gradually repaying their debts? Keeping debt in a healthy environment, or waiting to receive more COMP and making your debt rise more and more? If we look at the “Repay” events of loan repayments, they have grown quite a lot,
which could make us think that accumulating a lot of interest is being avoided by repaying debts little by little.
Some users chained several loans, one on top of the other, using the collateral to take another loan, and in turn another… which could lead to a unique user concentrating many loans and in case of non-payment, the chained ones would also be liquidated.
If we group the accounts by requested loans, from the launch of liquidity mining to the day of writing this article, we see
Although some of the accounts are contracts, in which multiple users can operate, there are accounts that accumulate a high number of loans, which, when created through leverage, may involve greater potential risk.
Gas prices in the Ethereum network have been very high lately, and in some cases transaction confirmation times are being extended. This is a point to keep in mind too, since the rise in the transactions prices can make repaying debts, freeing up profits from a contract … entail an extra expense not initially contemplated, which may exceed the benefits in small amounts.
For a loan to be liquidated, the health of that user must be less than 0. To see how exposed users are in general, we are going to extract all the users who have applied for loans and see how it is distributed globally the health.
As we can see, there is a high concentration of accounts with health close to 0, so their liquidation risk is higher, as they are more exposed.
Since the tokens began to be distributed, there have been no significant changes in the valuations of the crypto assets involved, which could have led to more liquidations, if we look at the daily liquidations, we do not see that there have been more than the usual
These are some of the metrics that can be analyzed within the protocols, to try to estimate how their users behave and if this may entail more risk. In some cases, having the incentive to earn more, can make risk control is neglected and can trigger a liquidity crisis.
The fundamental advantage of DeFi with traditional finance is that all this data is accessible by everyone, because of that each participant can access the information and make the decision he considers, without relying solely on price.