Original author: Helena L., Zixu H., Eocene Research
What is the driving force behind the growth of the NFT market in 2023, the internal circulation of funds or the support of new capital? We used on-chain data analysis and address clustering algorithms to reveal the answer to this question.
By analyzing the transaction volume and investment proportion of new and old address entities within the entire NFT market in 2023 using on-chain data analysis and address clustering algorithms, we can determine whether the activity and new momentum in the NFT market mainly come from existing players or new entrants outside the market.
Since the end of June 2022, the NFT market has entered a bear market. However, after Blur launched the Airdrop incentive mechanism at the end of 2022, the transaction volume of the NFT market has improved (Figure 1). During Blur's airdrop event, its contribution to the overall NFT market transaction volume has been increasing (Figure 1).
Figure 1: NFT daily transaction volume
The reward mechanism of the Blur airdrop and the successful issuance of tokens undoubtedly are the main factors contributing to the surge in NFT transaction volume in 2023. However, behind the surge in transaction volume, what is the true situation of the entire NFT market? In other words, is there an actual increase in on-chain funds of NFTs, and is there new capital entering the market, or is it mainly the internal circulation of existing funds?
We 1) investigated the source of the new transaction volume and funds in the entire NFT market in the second quarter compared to the first quarter; 2) compared the transaction volume and investment proportion of different address entities in the entire NFT market during the first and second quarters of the Blur airdrop event.
Research Process
1. Obtained transaction volume data for each address
Firstly, we selected the time intervals for research as period A (October 19, 2022 - February 14, 2023) and period B (February 15, 2023 - May 31, 2023)¹;
Secondly, we studied the transaction volume and funding from the perspective of buyers because the focus of the study is on "the investment of NFT market participants in NFTs";
Finally, we filtered out wash trades^2 and obtained the actual trading volume and number of transactions for each address.
Furthermore, there are many participants in the NFT market during the research period. However, the data shows that the top 8% of addresses contribute to 90% of the total trading volume/funds in the NFT market^ . Therefore, for ease of analysis, we define the scope of our research as the "top trading volume addresses that contribute to 90% of the total trading volume/funds for each time period," resulting in a total of over 7w addresses^ ;
[^1^] Time period A corresponds to the Blur first quarter airdrop, and time period B corresponds to the Blur second quarter airdrop. The division is based on the issuance of the BLUR token on February 14, which significantly boosted the activity in the NFT market.
[^2^] Wash trades exclusion rules: buyer=seller; buyer and seller have a common EOA fund source.
2. Based on trading volume and number of transactions, we analyze the investment situation.
Based on the previously obtained trading volume and number of transactions for each address, we calculate the amount of money each buyer invested in different NFT collections' token_ids^3.
We sum up the total investment amount for each buyer to determine the amount of funds invested in NFTs across different addresses.
[^3^] When token_standard=erc 721, each token_id corresponds to the same token. Therefore, the amount of money invested in each token_id by a buyer is the average price paid for purchasing that token_id (average price = total amount paid / number of purchases). When token_standard=erc 1155, each token_id can correspond to multiple tokens. In this case, we assume that the amount of money invested in each token_id is the total amount spent on purchasing that token_id.
3. Based on address clustering algorithm, different entities are identified.
Based on the logic of fund association, addresses that are highly likely to be controlled by the same entity are grouped together, allowing us to study the sources of trading volume and funds in the two time periods from an entity perspective.
We define clustering based on the following criteria⁴: 1) there has been a transfer of eth or stablecoin between addresses; 2) the two addresses must have transferred to each other with at least 3 transactions in one direction and at least 1 transaction in the other direction; 3) the transactions are limited to the year 2023 between addresses.
Using the algorithm, we cluster addresses according to the above criteria and obtain different address groups. We use s 1 _ind and s 2 _ind to indicate whether an address has participated in NFT transactions during time period A and time period B respectively⁵. If at least one address in an address group has s 1 _ind= 1, then the address group is classified as an old entity. If all addresses in an address group have s 1 _ind= 0, then the address group is classified as a new entity.
[ 4 ] Our algorithm can identify direct or indirect associations between wallets. "Direct" refers to interactions between two NFT players that meet the criteria. When multiple NFT players have interacted with the same address (regardless of whether the address is within the analysis scope) and the interactions meet the above criteria, there will be an "indirect" link between these NFT players.
[ 5 ] s 1 _ind= 1 and s 2 _ind= 1 indicate that the address has participated in NFT transactions during both time periods; s 1 _ind= 1 and s 2 _ind= 0 indicate that the address has only participated in NFT transactions during time period A; s 1 _ind= 0 and s 2 _ind= 1 indicate that the address has only participated in NFT transactions during time period B.
Research Results and Analysis
1. Data Results⁶ (buy volume refers to "transaction volume" and capital refers to "funds volume"; Season 1 corresponds to time period A, Season 2 corresponds to time period B)
1.1) Transaction volume and funds volume for each time period:
Transaction volume and funds volume for time period A and time period B
1.2) Volume and capital in time period B for new and old addresses^7 (left in ETH; right as percentage):
Volume and capital in time period B for new and old addresses
1.3) Volume and capital in time period B for new and old entities^8 (left in ETH; right as percentage):
Volume and capital in time period B for new and old entities
[ 6 ] "Volume and capital in time period B" output data from the perspective of addresses and entities to compensate for potential flaws in address clustering (such as wrongly attributing the addresses of some new entities to old entities, resulting in inflated transaction and capital volume of old entities), thus obtaining a benchmark for the data.
[ 7 ] s 1 _ind= 1 indicates old addresses, s 1 _ind= 0 indicates new addresses.
[ 8 ] on_ind=old indicates old entities, on_ind=new indicates new entities.
2. Result analysis
2.1) Growth of on-chain capital in NFT
Total transaction volume and capital in time period B are both greater than in time period A, with absolute increments of 906,857 E and 661,159 E, respectively. The transaction volume and capital both show an upward trend, indicating overall growth in the NFT market.
2.2) Source of new capital
The incremental capital is smaller than the capital of new entities in time period B (661,159 E vs 851,181 E), indicating that the main source of new capital comes from new entities, while at least some of the capital invested by old entities in the NFT market is shrinking.
2.3) Percentage of transaction volume and capital for new and old entities
The transaction volume and funding volume data for time period B from a comprehensive address and entity perspective roughly account for 55%-70%.
The transaction volume and funding volume of old entities in time period B both account for more than half, indicating that old entities are the main contributors to the activity in the NFT market;
However, it should also be noted that there is no significant difference in the proportion between new and old entities, therefore we believe that the contribution of new entities to the NFT market should not be overlooked.
Conclusion
By studying the proportion of transaction volume and investment funds between new and old entities in the NFT market (with old entities accounting for about sixty percent), as well as the source of transaction and funding increment (mainly from new entities), we conclude that old players (old entities) are the main contributors to the activity in the NFT market, while new entrants from outside the market (new entities) are the source of new momentum in the NFT market.
It should be noted that the increment in funding and the entry of new players do not necessarily mean that the NFT market is booming. This is because most of the increment is concentrated on Blur, which is highly likely to be attracted by token rewards rather than the value of NFT itself. As for how to sustain long-term prosperity in the NFT market after the airdrop, it remains a major challenge faced by the market as a whole.
