Will the growth of the NFT market in 2023 benefit from the entry of new funds or the involution of old funds?

作者：Helena L. ，Huang Z.， Eocene Research

** Through on-chain data analysis and address clustering algorithms, we have studied the transaction volume and proportion of investment funds of new and old address entities in the entire NFT market in 2023, as well as the sources of transaction and capital increments, thereby revealing the activity of the NFT market And the new momentum mainly comes from old players or new entrants outside the field. **

Since the end of June 2022, the NFT market has entered a bear market, but after Blur launched the Airdrop incentive mechanism at the end of 2022, the trading volume of the NFT market has improved (Figure 1). During Blur’s airdrop campaign, it can be seen that its contribution to the transaction volume of the entire NFT market has been increasing (Figure 1).

Figure 1 NFT daily trading volume

The reward mechanism of Blur airdrops and the successful issuance of tokens are undoubtedly the biggest factors for the surge in NFT trading volume in 2023. But behind the skyrocketing trading volume, what is the real situation of the entire NFT market; in other words, is there an actual increase in the funds on the NFT market, and whether there is new capital flowing into the market, or is it mainly the involution of old funds?

We 1) explored the new transaction volume and sources of funds in the entire NFT market in the second quarter compared with the first quarter; 2) compared the transaction volume and investment funds of different address entities in the entire NFT market during Blur’s airdrop activities in the first quarter and the second quarter proportion.

research process

1. Obtain the transaction volume data of each address

1. First, the time intervals we selected for the study are time period A (October 19, 2022-February 14, 2023) and time period B (February 15, 2023-May 31, 2023);
2. Secondly, we conduct research on transaction volume and capital volume from the perspective of buyers, because the research focuses on "NFT market participants' investment in NFT";
3. Furthermore, there are many participants in the NFT market during the research period, but the data shows that the number of Top trading volume addresses that contribute 90% of the entire NFT market trading volume/funding volume accounts for only 8% of the total number of addresses. Therefore, for the convenience of analysis, we define the scope of the research object as "Top transaction volume addresses that contribute 90% to the total transaction volume/capital volume in each time period", and thus screen out a total of more than 7w addresses;
4. Finally, we eliminated wash trades to obtain the real transaction volume and number of transactions of each address.

Time period A corresponds to Blur’s airdrop in the first quarter, and time period B corresponds to Blur’s airdrop in the second quarter. The day of BLUR token issuance on February 14 is used as the dividing line. This is because the issuance of BLUR has significantly and rapidly stimulated the activity of the NFT market.

Wash trades exclusion rule: buyer=seller, buyer and seller have a common source of EOA funds.

2. Based on the transaction volume and the number of transactions, the investment funds are obtained

1. Based on the previously obtained transaction volume and transaction number of each address, calculate the amount invested by each buyer for each token_id in different NFT collections;
2. Sum up all the investment amounts of each buyer to get the amount of funds invested in NFT by each address.

When token_standard=erc 721, each token_id corresponds to the same token, so the amount invested for each token_id is the average price paid for buying the token_id (average price=sum of payment amount/number of purchases ); when token_standard=erc 1155, each token_id can correspond to multiple tokens, so we assume that the investment amount of each token_id is the total amount spent to buy the token_id.

3. Divide different entities based on address clustering algorithm

Based on the logic of capital association, addresses that are highly likely to be controlled by the same entity are divided into a group, so as to study the sources of transaction volume and capital volume in two time periods according to the entity dimension.

1. We stipulate that the clustering is based on the following criteria: 1) There have been transfers of eth or stable coins between addresses; 2) There must be transfers between two addresses, the number of times is: one direction >= 3 pens, and the other direction > = 1 transaction; 3) only limited to transactions between addresses in 2023.
2. Use the algorithm to cluster addresses according to the above criteria to obtain different address groups, and use s 1 _ind and s 2 _ind to identify whether the address has participated in NFT transactions in time period A and time period B. Wherein, if the address group contains at least one address with s 1 _ind= 1, the address group is the old entity; if the addresses s 1 _ind in the address group are all 0, the address group is the new entity.

Our algorithms can identify direct or indirect links between wallets. "Direct" means that the interaction between two NFT players meets the criteria. When multiple NFT players have interacted with the same address (whether the address is in the scope of analysis or not) and the interaction meets the above criteria, an "indirect" link will be formed between these NFT players.

s 1 _ind= 1 and s 2 _ind= 1 means that the address participated in NFT transactions in both time periods; s 1 _ind= 1 and s 2 _ind= 0 means that the address only participated in time period A NFT transaction; s 1 _ind= 0 and s 2 _ind= 1 indicates that the address only participated in the NFT transaction in time period B.

Research Results and Analysis

1. Data results (buy volume is "trading volume", capital is "capital volume"; Season 1 corresponds to time period A, Season 2 corresponds to time period B)

"Transaction volume and capital amount in time period B" respectively output address and entity angle data to make up for possible defects in address clustering (such as misclassifying the addresses of some new entities into old entities, resulting in transactions of old entities and the amount of capital is inflated), so as to obtain the benchmark of the data.

1.1 Transaction volume and fund volume in two time periods:

Trading volume and capital volume in time period A and time period B

1.2 New and old addresses** Transaction volume and capital amount in time period B (the left is in ETH; the right is the percentage):**

The transaction volume and fund volume of the old and new addresses in time period B

s 1 _ind= 1 is the old address, s 1 _ind= 0 is the new address.

1.3 New and Old Entities** Transaction volume and capital amount in time period B (the left is in ETH; the right is the percentage):**

Transaction volume and funding volume of old and new entities in time period B

on_ind=old for old entities and on_ind=new for new entities.

2. Result analysis

2.1 The growth of funds on the NFT market

The total trading volume and capital volume in time period B are greater than those in time period A, and the absolute values of the incremental transaction volume and capital volume are 906,857 E and 661,159 E respectively. Both the trading volume and the amount of funds are showing an upward trend, indicating that the NFT market is generally growing.

2.2 Sources of new funds

The increase in the amount of funds is smaller than the amount of funds of the new entity in time period B (661, 159 E vs 851, 181 E), so the amount of new funds mainly comes from the new entity, and at least part of the amount of funds invested by the old entity in the NFT market is in shrinking.

2.3 The proportion of transaction volume and capital volume of new and old entities

Combining the transaction volume and capital volume data from the perspective of addresses and entities in time period B, the proportion of transaction volume and capital volume of old entities is roughly at 55% -70%.

• The transaction volume and capital volume of the old entity in time period B accounted for more than 50%, indicating that the old entity is the main contributor to the NFT market activity;
• But at the same time, it should be noted that there is no great difference in the proportions of old and new entities, so we believe that the contribution of new entities to the NFT market cannot be ignored.

in conclusion

By studying the transaction volume and proportion of investment funds of new and old entities in the NFT market (old entities account for about 60%), as well as the sources of transactions and capital increments (mainly from new entities), we believe that most old players (old entities) ) is the main contributor to the activity of the NFT market, while new entrants (new entities) outside the market are the source of new momentum in the NFT market. **

**It should be noted that the increase in capital and the entry of new players does not entirely mean that the NFT market is booming. **This is due to the fact that most of the increment is concentrated on Blur, most likely attracted by the token rewards rather than the value of the NFT itself. As for how to maintain the long-term prosperity of the NFT market after the airdrop, it is still a major challenge faced by the market.