Original title: “Depth丨According to the model prediction, ETH will rise to $8880 in the future?”
Written by: Christopher White
Translation: Li Hanbo
Inspired by PlanB’s inventory and traffic regression model, I used the total daily cost of the network to develop a regression model for Ethereum.
The basic argument of Bitcoin is that it is a store of value, similar to gold, so the stock to flow ratio is a useful basis for analyzing the value of Bitcoin. I think that the basic argument of Ethereum is that it is a settlement layer, a transaction network that can carry economic and non-economic decentralized applications. Since Ethereum is a settlement layer, it is reasonable to use the total daily fees processed on the network to analyze the value of the Ethereum network.
Daily cost regression model
The figure below is a regression model based on the daily fees processed on the Ethereum network and the Ethereum price since August 2015.
Data sources for charts made with Excel and CoinMetrics:
It can be seen that the R-squared value of this model is 94% in a time frame of approximately five years (August 2015 to December 2020), which represents a statistical correlation.
I noticed that there are three main stages, which are related to the three price ranges of Ethereum. Similar to PlanB’s stage model, I summarize these three periods into stages and their daily transaction fees and the average value of Ethereum price as follows.
In the first phase (August 2015 to September 2015), the daily cost is about US$20 and the average price is about US$1.
In the second stage (May 2016 to February 2017), the daily cost is about US$500 and the average price is about US$11.
In the third phase (December 2017 to December 2020), the daily transaction fee is approximately US$500,000, with an average price of approximately US$33.
The daily fee consists of two parts: the number of transactions per day and the average fee per transaction. Multiplying these two parts equals the total daily cost. I decided to disassemble the two parts and analyze them separately to evaluate whether they can be used to build a model.
Daily transactions
In order to count the number of daily transactions, I evaluated transactions at the level of transactions per second (TPS). The following is a time series chart of the transaction volume per second on the Ethereum network.
Data sources for charts made with Excel and CoinMetrics:
The three stages of transaction volume per second and the corresponding average TPS are summarized below.
- The first stage: 0.05 TPS
- The second stage: 0.5 TPS
- The third stage: 8.9 TPS
Average cost per transaction
When calculating the average cost of each transaction, I used the average cost of each transaction in the same time period as the TPS chart.
Data sources for charts made with Excel and CoinMetrics:
The three stages of average cost per transaction are summarized below
- The first stage: USD 0.004 per transaction
- The second stage: 0.01 USD per transaction
- Stage 3: USD 0.7 per transaction
Simulated prices and daily transaction fees
I have determined the maximum, minimum, and average transaction volume per second for the three stages, and determined the corresponding transaction fees for these specific dates. In the following table, there are three main parts: “maximum”, “minimum” and “average” . In these three parts, I have determined the TPS and transaction fees for each stage. For example: 0.81 is the maximum TPS determined on the Ethereum network during the second phase, and the corresponding transaction fee is 0.014 USD on that day. The total transaction fee on that day was approximately US$962, and the price predicted by the regression model was US$15.6 .
I outlined the fourth stage and I believe that once ETH 2.0 is deployed, the maximum TPS capacity will increase. For the sake of simplicity, I have outlined the three scenarios of the fourth stage; from the third stage TPS and transaction fees increased by 5 times, 10 times and 15 times .
Data source for charts made with Excel and CoinMetrics: The model price is calculated using the regression formula Y=0.4018X^.5324 outlined at the beginning
In terms of “maximum”, “minimum” and “average”, the model seems to have a high correlation with the actual price observed in each instance.
For example: the lowest TPS observed in the third stage is 4.41, and the transaction fee is $0.1. This makes the model calculate that the lowest price in the third stage is 99.8 USD, and the lowest price in this stage is 82.8 USD.
Taking the fourth stage (5 times) estimate seems conservative, because it will mean that the highest TPS of the fourth stage is 81.4 and the transaction fee is 20.6 USD, resulting in the highest model price of about 8,880 USD .
The following table lists the historical growth percentages of TPS and transaction fees for the second and third periods.
Data sources for charts made with Excel and CoinMetrics:
Use a four-stage price model
Apply the highest TPS and transaction fees of the four stages (5 times the fourth stage) to the following chart.
Inspired by Plan B’s stage model
Modeling the four stages (5x scheme as a conservative measure) with actual prices produces a model similar to PlanB’s Bitcoin stock-to-flow model, but uses the daily transaction fees on the Ethereum network.
Data sources for charts made with Excel and CoinMetrics:
The fourth phase (5 times) plan means that the highest price is approximately US$8,880, the lowest price is approximately US$554, and the average price is approximately US$2,515 .
Track model price and actual price
Using the regression formula to compare the total daily cost and the actual price, we can see that the current model indicates that the price of Ethereum is 1200 USD (December 30), and the actual price is about 750 USD.
Red indicates that the actual price is much lower than the model price, and blue indicates that the actual price is higher than the model price. The ETH price is the Y value, and the model price is a 30-day moving average.
Sources of chart data made with Python and CoinMetrics:
To evaluate whether the actual price is lower or higher than the model price by percentage, we can get the following chart. Interestingly, we noticed the symmetry of the “too high/(too low)” difference.
Sources of chart data made with Python and CoinMetrics:
in conclusion
Ethereum has a high value proposition and has become the settlement layer for innovative economic and non-economic decentralized applications. As the settlement layer, historical daily transaction fees provide us with an analysis of the historical price of Ethereum.
The model presents a high correlation between daily transaction fees and the price of ETH. Therefore, any increase in the network’s transaction volume per second and the average fee per transaction in the future seems to be related to the increase in the valuation of the Ethereum network .
The model predicts that the conservative maximum price for the next stage is approximately $8,880, which will occur when the transaction volume per second increases significantly from the current average of 8.9 TPS in the third stage. The launch of ETH 2.0, which provides higher transaction volume per second, may be a catalyst for the increase in network utilization and the increase in the price of each Ethereum.
