What Is Driving the Bull Market in Crypto? Is It Technological Upgrades?

In the previous article, “Does Team Activity Really Affect Coin Prices?” we examined the correlation between GitHub development activity in the industry as a whole and the price fluctuations of tokens. We concluded that the GitHub Six Factors are positively correlated with token price fluctuations in both bull and bear markets.

In this article, we further explore the causality behind this correlation, asking whether “price increases are driven by technological upgrades or if price increases stimulate technological upgrades.” This analysis aims to help investors and developers better understand the role of “technical development” as a fundamental factor in the fluctuations of token prices.

The article follows this general outline:

First, we construct a GitHub Development Activity Index (GDAI) for individual tokens to measure their development activity.

Next, we build an Industry GitHub Development Activity Index (IGDAI) that reflects the overall GitHub development activity in the industry. This index takes into account factors such as industry market capitalization rankings and the historical trends in the number of GitHub projects over time.

Then, by comparing the changes in the IGDAI and token price fluctuations over the past six years, we aim to determine the causal relationship between technology and price.

Finally, we apply the GDAI index to tokens that have been under continuous development for the past six years. By comparing their development activity index values and price increases with those of BTC and ETH, we aim to validate our earlier assessment of the causal relationship between technology and price.

Step 1: Constructing the GitHub Development Activity Index (GDAI) for individual projects using the Analytic Hierarchy Process.

Table 1: Interpretation of the Relationship Between GitHub Five Factors and Project Development \

The specific formula for GDAI is as follows:

**The Analytic Hierarchy Process (AHP) is a comprehensive evaluation method for systematic analysis and decision-making. It decomposes the elements required for decision-making into three levels: the objective level, the criterion level, and the scheme level. Based on this decomposition, qualitative and quantitative analyses are conducted, making the calculation process simple and efficient.

(1) Analyze the relationships between various factors in the system and establish a hierarchical structure for the system.

Decompose the GDAI at the objective level into five criterion levels:

μStar, μFork, μCommit, μIssues, μPullRequests.

Figure 1: Decomposition of the GDAI Index

(2) Establishing Judgment Matrices

For pairwise comparisons of the importance of elements within the same level concerning a criterion from the previous level, we construct pairwise comparison matrices (judgment matrices). We have determined different levels of importance as shown in Table 2.

Table 2: Different Levels of Importance

For the criterion layer B, the following judgment matrices are created. Based on experience and the nature of the indicators, the priority contribution to GitHub development activity is as follows: Commit > Pull Requests > Issues > Fork > Star. Since the Star and Fork indicators do not have a particularly direct relationship with development activity, we assign relatively lower scores to them.

Table 3: Judgment Matrix B

(3) Consistency Check (CI)

Characteristic equation of matrix B:

(4) Calculation of Weights by Three Methods

Method 1: Arithmetic Mean Method

The formula for the derived weight vector is:

Method 2: Geometric Mean Method

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

Take the average of the weights obtained from the above three methods, which is the final determined weight value. The specific results are as shown in Table 4.

Table 4: Specific Weights of the Five Major Factors

Therefore, the specific formula for the GDAI index can be expressed as follows:

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

In Step 1, we constructed the GitHub development activity index, GDAI, for individual tokens. Now, building upon GDAI, we comprehensively assess the entire cryptocurrency industry by considering all tokens listed and openly sourced on GitHub. This results in the calculation of the Industry Github Development Activities Index (IGDAI). The specific formula for calculating IGDAI is as follows:

IGDAI Calculation Formula:

Where ‘n’ represents the total number of tokens circulating in the cryptocurrency market and openly sourced on GitHub within a specific interval.

When constructing an indicator reflecting the overall industry situation, there are typically two approaches:

1. Select representative assets and evaluate their performance.

2. Consider the entire industry comprehensively.

For approach 1, we first consider that the current cryptocurrency industry ecosystem is not yet fully mature, and many tokens with good price performance and market capitalization are not open-sourced. Third parties cannot access specific development information, making the selection of “representative” assets subject to debate. Secondly, the cryptocurrency industry is still a rapidly evolving field with ample growth potential, and every token has the potential for rapid development. Lastly, the high liquidity characteristic of the cryptocurrency industry with 24-hour trading leads to significant short-term market capitalization fluctuations. If we were to change the selected assets every six months, as is done in traditional stock markets, we might miss out on important information about token market value changes.

Therefore, in this paper, we consider the development information of all tokens in the entire industry to calculate IGDAI.

Step 3: The Relationship Between “Technological Revolution” and “Price Increase” - Unidirectional Influence of Price Change on GitHub Development

We used the Granger causality test to analyze the causal relationship between industry development activity IGDAI and BTC price changes, two time series data sets. The time range considered is from 2015 to 2023-10-31, with a daily index dimension. Initially, we determined a lag order of 4, and through unit root tests, we confirmed that both data sets are stationary (a prerequisite for the Granger causality test). The following results were obtained:

Table 5: Granger Causality Test Results

In the table above:

• A p-value of 0.000, which is less than 0.05, indicates that the F-test rejects the null hypothesis (H0: There is no Granger causality relationship between the two). This implies that BTC_price is a cause of IGDAI, meaning that the industry’s GitHub development activity, IGDAI, is influenced by lagged changes in cryptocurrency prices.

• A p-value of 0.135, which is greater than 0.05, implies that the F-test accepts the null hypothesis, suggesting that IGDAI is not a cause of BTC_price. In summary, price changes unidirectionally influence industry development activity.

Additionally, we present a more intuitive analysis using charts. Considering the significant fluctuations in the development activity index on a daily basis, which may be influenced by numerous random factors and can be less visually informative, we have applied exponential smoothing and expanded the time interval to “weekly.” Figure 2 illustrates the IGDAI index and BTC price changes from 2015 to the present, with a monthly time frame.

Figure 2: IGDAI Index and BTC Price Changes from 2015 to October 2023

This graph provides a clear visual representation of how changes in industry development ecology lag behind BTC price fluctuations during different periods. Additionally, both exhibit similar levels of volatility, confirming the conclusion that IGDAI is unidirectionally influenced by price changes.

Furthermore, from the graph, we can observe that in the past few months, the Industry Development Activity Index (IGDAI) has experienced a sharp decline of 31.7%, marking the largest drop in nearly a decade!

Step 4: As Long as Development Teams Stay Active, Does the Price Performance Remain Stable During Bear Markets? Not Necessarily!

In Step 3, we established the conclusion that price unidirectionally influences technical development through Granger causality testing. However, we also want to explore whether there exists a special relationship: even if the extent of GitHub development is not a leading factor in price fluctuations, does consistent development activity, especially during bear markets, lead to more stable price performance? Considering variations in the maturity of token development ecosystems and the diversity of token types, we decided to identify tokens that have been continuously developed since 2018 and compare their GitHub development activity (GDAI) with their price fluctuations relative to BTC.

In this context, we define “continuous development” as having non-zero core GitHub development activity, including commits, issues, and pull requests, in each week from 2018 to October 2023. Price fluctuations are defined as the (highest price - lowest price) / lowest price during that period. Through extensive data collection and analysis, we initially identified approximately 1,400 tokens that have been simultaneously open-sourced and listed since 2018. Among these, 38 tokens meet the aforementioned criteria (including BTC and ETH, which are highly mature in terms of development ecology and market capitalization). Given the length of this article, we will focus on discussing the results of the remaining 36 tokens in comparison to BTC. The specific list of tokens is provided in Table 6:

Table 6: Tokens that have been continuously developed since 2018

Regarding the GitHub Development Activity Index (GDAI), the statistics of 38 tokens have been compiled to produce Figure 3:

Figure 3: GDAI of Tokens with Continuous Development on GitHub from 2018 to 2023

In this figure, tokens with IGDAI surpassing BTC are depicted in red, while those that do not are shown in blue. Among the tokens with continuous development, 9 tokens exhibit higher development activity than BTC.

Regarding price fluctuations, we present the findings in Figure 4:

Figure 4: Price Fluctuations of Tokens with Continuous Development on GitHub from 2018 to 2023

In this figure, tokens with price fluctuations exceeding BTC are represented in red, while those that do not are shown in blue. Among the tokens with continuous development, 31 tokens have experienced price increases surpassing BTC.

Summarizing the findings from both figures, there is an overlap of 8 tokens represented in red. This means that from 2018 to the present, 8 tokens have simultaneously exhibited superior performance in GitHub development activity (GDAI) and price fluctuations compared to BTC (an industry benchmark). These 8 tokens account for 22% of all tokens with continuous development activity within this timeframe. The specific tokens are listed in Table 7:

Table 7: Tokens from 2018 to 2023 with Simultaneously Superior GDAI and Price Performance Compared to BTC

When considering continuous development, the 22% overlap rate suggests that while continuous development does have some influence on price, it cannot conclusively demonstrate a highly positive driving effect on price. This observation aligns with the results of the Granger causality test in Step 3, confirming the notion that price unidirectionally affects GitHub development activity.

Conclusion

In summary, Falcon presents the following conclusions in this article:

1. Utilizing the Analytic Hierarchy Process (AHP), this article established the development activity index, GDAI, for individual tokens and the Industry GitHub Development Activities Index, IGDAI, for the entire industry.

2. By analyzing the “Industry GitHub Development Activities Index IGDAI” and “BTC price data” from 2015 to October 2023, it was found that price only unidirectionally influences GitHub development activity. Moreover, in the past few months, the Industry Development Activity Index has experienced a sharp decline of 31.7%, marking the largest drop in nearly a decade.

3. “Continuous development by teams” is not the core driving factor for price increases after bear markets. When making investments, it is essential to consider other factors that impact price comprehensively.

Disclaimer：

1. This article is reprinted from [mirror]. All copyrights belong to the original author [LUCIDA & FALCON]. If there are objections to this reprint, please contact the Gate Learn team, and they will handle it promptly.
2. Liability Disclaimer: The views and opinions expressed in this article are solely those of the author and do not constitute any investment advice.
3. Translations of the article into other languages are done by the Gate Learn team. Unless mentioned, copying, distributing, or plagiarizing the translated articles is prohibited.