What is driving the Crypto bull market?

What is fueling the rise of the Crypto bull market?

In the previous article “Is the Team’s Work Really Related to the Token Price?” we analyzed the correlation between the overall GitHub development activity and the price fluctuation of tokens. We concluded that there is a positive correlation between the six GitHub factors and the price fluctuation of tokens, both in bull and bear markets.

In this article, we further expand on this conclusion and study the causality between the two, that is, “whether the price increase is because of the technological upgrade, or the price increase drives the technological upgrade?” This will help investors and developers better understand the position of “technological development” as a fundamental factor in the price fluctuation of tokens.

The general idea of the article is as follows:

First, we construct the GitHub Development Activity Index (GDAI) to measure the development activity of individual tokens.

• Maven 11 Capital Research Head How does Maven 11 view the future of modularization and blockchain infrastructure?
• FTX transferred approximately $200 million worth of cryptocurrencies to exchanges which tokens were specifically sold off.
• Monero’s Community Crowdfunding Wallet Hacked: A Whirlwind Heist Worth $460,000

Next, based on this, we construct the Industury Github Development Activity Index (IGDAI), which reflects the overall GitHub development activity in the industry, by combining factors such as industry market capitalization ranking and the regularity trend of GitHub project quantity over time.

Then, by comparing the changes in the IGDAI and the price fluctuation over the past six years, we determine the causality between technology and price.

Finally, we apply the GDAI index to tokens that have been developed consistently for the past six years, comparing their development activity index values and price increases with BTC and ETH, to verify the judgment on the causality between technology and price made in the previous text.

Step 1. Use Analytic Hierarchy Process (AHP) to construct the GitHub Development Activity Index (GDAI) for individual projects

The specific formula for GDAI is as follows:

The Analytic Hierarchy Process (AHP) is a comprehensive evaluation method for systematic analysis and decision-making, which decomposes the elements of the decision into objective layer, criterion layer, and scheme layer. Based on the decomposition, further qualitative and quantitative analysis is conducted, resulting in a simple and efficient calculation method.

1. Analyze the relationship between factors in the system and establish a hierarchical structure for the system

Decompose the objective layer GDAI into 5 criterion layers

μStar, μFork, μCommit, μIssues, μPullRequests.

Figure 1 GDAI index decomposition diagram

2. Establish judgment matrices

For each element in the same layer, compare their importance with a specific criterion in the previous layer, and construct pairwise comparison matrices (judgment matrices). In Table 2, we determine the measurements for different levels of importance.

Table 2 Measurements for different levels of importance

Create the following judgment matrices for criterion layer B. Based on experience and the nature of the indicators, the priority of contributions to the GitHub development activity is Commit>Pullrequests>Issues>Fork>Star. Since the indicators Star and Fork do not have a particularly direct connection to development activity, we assign them relatively lower weights.

Table 3 Decision Matrix B

3. Consistency Check (CI)

The characteristic equation of Matrix B:

4.3 Methods for Calculating Weights

Method 1: Arithmetic Mean Method

The derived formula for the weight vector is:

Method 2: Geometric Mean Method

Method 3: First, use the eigenvalue method to determine the maximum eigenvalue and corresponding eigenvector of Matrix A. Then normalize the eigenvector to obtain the desired weights.

Take the average of the weights obtained from the above three methods, which will be the final determined weights. The specific results are shown in Table 4:

Table 4 Specific Weights of the 5 Major Factors

Therefore, the specific GDAI indicator formula can be written as:

Step 2. IGDAI (Industry Github Development Activities Index) Optimized Based on GDAI

In Step 1, we constructed the GDAI indicator for individual tokens on GitHub. Now, based on GDAI, considering all listed and circulating tokens in the cryptocurrency industry that are open source on GitHub, we calculate the IGDAI by aggregating the GDAI of all tokens. The specific formula for calculating IGDAI is as follows:

IGDAI Calculation Formula

Where ‘n’ represents the total number of tokens in a certain interval that circulate in the cryptocurrency market and are open source on GitHub.

There are usually two approaches to building an indicator that reflects the overall industry situation:

1. Selecting representative targets and calculating their performance. 2. Considering the overall industry situation comprehensively.

For approach 1, we first consider that the current cryptocurrency industry ecosystem is not yet complete. Many tokens with good price performance and market value are not open source, and third parties cannot obtain specific development information about them. The selection of “representative” targets is debatable. Secondly, the current cryptocurrency industry is still a blue ocean with vast development space. Each token has the potential to achieve rapid growth in a short period of time. Lastly, the high liquidity of the cryptocurrency industry’s 24-hour trading leads to significant short-term fluctuations in market capitalization. If we reference the A-share market where targets are changed within six months, we may miss out on a large amount of token market value change information.

Therefore, this article considers the overall development information of all tokens in the industry to calculate the IGDAI.

Step 3. “Technological Revolution” or “Price Increase”: Which one causes the other? Price changes in cryptocurrency only affect the development level on GitHub

We used the Granger causality test to analyze the causal relationship between the development activity of the industry (IGDAI) and the price changes of BTC, using time series data from 2015 to 2023.10.31, with a time interval of “day”. Firstly, we determined a lag order of 4, and through the unit root test, we confirmed that both types of data are stationary series (a prerequisite for the Granger causality test). The following results were obtained:

Where 0.000<0.05, it indicates that the F-test rejects the null hypothesis (null hypothesis H0: there is no Granger causality relationship between the two), and that the BTC price is the cause of IGDAI. In other words, the industry’s GitHub development activity (IGDAI) is influenced by the lagged term of price changes.

0.135>0.05, it indicates that the F-test accepts the null hypothesis, and that IGDAI is not the cause of BTC_price. In conclusion, the price changes only affect the industry’s development activity in one direction.

Additionally, we analyze the data using charts for a more intuitive presentation. Considering that the fluctuation of development activity indicators in daily intervals has a large amplitude and is influenced by various random factors, and the view is not intuitive, we applied exponential smoothing and extended the time interval to “week”. Figure 2 shows the changes in IGDAI index and BTC price from 2015 until now, with a time interval of “month”:

This chart clearly demonstrates that the changes in the industry’s development ecosystem lag behind the changes in BTC price at different periods, and both show similar fluctuations, confirming the conclusion that IGDAI is only influenced by price changes in one direction.

We also observed from the chart that in the past few months, the industry’s development activity index plummeted by 31.7%, marking the largest decline in nearly a decade!

Step 4. As long as the development team keeps working, enduring the bear market, the price won’t plummet, right? Wrong!

In Step 3, we established the conclusion that price influences technological development unilaterally through the Granger causality test. **But we also want to explore if there is a special relationship: even if the level of GitHub development is not the cause of price fluctuations, as long as the team keeps working and endures the bear market, will the price performance not be particularly disappointing?** Considering the maturity stage of token development ecosystem and the changes in the variety of tokens, we decided to identify tokens that have been actively developed since 2018 and compare the relationship between their GitHub development activity (GDAI) and the price changes compared to BTC.

Here, we define **”continuous development”** as commit, issues, and pull requests, which are the core factors of GitHub development, not being 0 in each week from 2018 to October 2023. Price changes are defined as (highest price – lowest price) / lowest price for that period. Through the collection and analysis of massive data, we first determined that there are approximately 1400 tokens that have been simultaneously open-sourced and listed since 2018. Among these 1400 tokens, we found 38 tokens that meet the aforementioned criteria (including BTC and ETH). Considering that the development ecosystems and market values of BTC and ETH are already very mature and representative, and considering the length of this article, we will focus on discussing the remaining 36 tokens compared to BTC. The specific list of tokens is shown in Table 6:

Table 6: Tokens under continuous development from 2018 to present

About GitHub development activity GDAI, statistics show the situation of 38 tokens, as shown in Figure 3:

Red represents tokens where IGDAI is higher than BTC, and blue represents tokens where it is not. Among the tokens under continuous development, 9 tokens have a higher development activity than BTC.

Regarding price fluctuations, Figure 4 shows:

Red represents tokens with price fluctuations higher than BTC, and blue represents tokens where it is not. Among the tokens under continuous development, 31 tokens have a higher price increase than BTC.

Summing up the situations depicted in the two charts, there are 8 tokens that overlap in the red zone. This means that from 2018 to present, 8 tokens have both higher GitHub development activity (GDAI) and price fluctuations compared to BTC (industry benchmark), accounting for 22% of all tokens under continuous development in this period. The specific tokens are shown in Table 7:

Considering the perspective of continuous development, the 22% overlap is relatively low. Therefore, we can only conclude that continuous development has a certain degree of influence on price, but we cannot definitively state that continuous development has a significantly positive impact on price movement. This viewpoint is also corroborated by the results of the Granger causality test in step 3.

Conclusion

Based on the content above, Falcon summarizes the conclusions of this article:

By using the Analytic Hierarchy Process, this article establishes a development activity index GDAI for individual tokens and a GitHub development activity index IGDAI for the entire industry.

By analyzing the “GitHub development activity index IGDAI” and “BTC price data” from 2015 to 2023.10, it is found that price only influences GitHub development activity in one direction. In the past few months, the industry’s development activity index has plummeted by 31.7%, marking the largest decline in nearly ten years.

“Continuous development by the team” is not the core driving factor for price increases after the bear market. When investing, it is necessary to consider other factors that may influence the price.

We will continue to update Blocking; if you have any questions or suggestions, please contact us!