In the previous article “Development as the Driving Force: Understanding the Impact on Token Price Performance? ” we meticulously examined the intricate relationship between industry-wide GitHub development and the fluctuation of token prices. Our analysis indicates a positive correlation between the six GitHub factors and both increases and decreases in token prices, regardless of market conditions in both bull and bear markets.
In our latest research paper, we delve deeper into the correlation we previously identified and explore the cause-and-effect relationship between technical development and the rise in token prices. We seek to answer an important question: Does development advancement lead to price increases, or do rising token prices drive development progress? This investigation aims to provide valuable insights for investors and developers, offering a clearer understanding of the crucial role played by "development" in shaping the movement of cryptocurrency prices.
In this news article, we present an overview of the key findings from our research study:
To begin, we developed the GitHub Development Activity Index (GDAI) to measure the level of GitHub development activity for individual tokens.
Building upon this, we integrated various factors, including industry market capitalization ranking and the ongoing trend of GitHub project numbers, to create the Industry GitHub Development Activity Indicator (IGDAI). This indicator provides a comprehensive representation of the overall GitHub development activity across the industry.
Next, we explored the causal relationship between development and price by examining the correlation between the IGDAI trend and the fluctuations in token prices over the past six years. This analysis aimed to determine whether changes in the industry's development activity corresponded to movements in token prices.
Lastly, we applied the GDAI indicator to tokens developed within the past six years, comparing the differences between the values of the development activity indicator and the increases in coin prices, as well as the performance of leading cryptocurrencies like BTC and ETH. This step aimed to validate our initial observations regarding the causal relationship between development and price.
Step1. Utilize the hierarchical analysis method to construct the GitHub Development Activity Index (GDAI) for individual projects.
Aiming to accurately evaluate a token's development activity and showcase its technological advancement within a defined timeframe, we are in search of a reliable metric. Recognizing the significance of GitHub as a leading open-source code platform, we have chosen to utilize its wealth of token development data. Consequently, we are introducing the GitHub Development Activity Index (GDAI) as a comprehensive measure for individual tokens, which takes into account five crucial factors: Stars, Forks, Commits, Issues, and Pull Requests on GitHub.
GDAI formula:
The Analytic Hierarchy Process (AHP) is a robust methodology utilized for system analysis and decision-making. This approach breaks down the decision into distinct components, namely objectives, criteria, and scheme. By conducting qualitative and quantitative analyses on these components, AHP simplifies and streamlines the calculation process, ensuring efficiency and accuracy.
(1) Analyze the relationship between various factors in the system and establish a systematic hierarchical structure.
Decompose the target layer GDAI into five criteria layers: μStar, μFork, μCommit, μIssues, μPullRequests。
(2) Construction of the Judgment Matrix
To compare the relative importance of each element at the same level in relation to a specific criterion from the previous level, we create a two-by-two comparison matrix known as the judgment matrix. In Table 2, we establish the metrics for varying levels of importance.
To evaluate criterion layer B, Based on experience and the nature of the metrics, the prioritization of contributions to GitHub development activity is Commit>Pullrequests>Issues>Fork>Star. Since Star and Fork metrics are not directly indicative of development activity, we will assign relatively lower weights to them.
(3) Consistency Inspection (CI)
The characteristic equation of matrix B:
(4) Calculation of Weights using 3 Methods
Method 1: Arithmetic Mean Method
The formula for deriving the weight vector using the arithmetic mean method is as follows:
Method 2: Geometric Mean Method
Method 3: The eigenvalue method is employed as the initial step to calculate the maximum eigenvalue and its corresponding eigenvector for matrix A. Subsequently, these eigenvectors are normalized to obtain the desired weights.
The weights obtained from the above 3 methods are averaged, which is the finalized weight value. The specific results are shown in Table 4:
Therefore, the specific formula for the GDAI is as following:
This index can calculate the GitHub development activity of each token during any time period
Step2 The industry-wide GitHub Development Activity Index (IGDAI) optimized based on GDAI.
In Step 1, we developed GDAIi, an indicator that measures the GitHub development activity of individual tokens. Building upon GDAIi, we then consider all open-source tokens in the cryptocurrency industry listed and in circulation on GitHub. By aggregating the GDAIi values of these tokens, we derive the industry-wide GitHub development activity indicator, IGDAI. The specific formula for calculating IGDAI is as follows:
When constructing an indicator to represent the overall state of an industry, there are typically two approaches:
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Selecting a representative marker and evaluating its performance.
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Taking a comprehensive view of the entire industry as a whole.
In the first approach, we must consider that the current cryptocurrency industry ecosystem is not yet mature, and many tokens with impressive performance in terms of coin price and market capitalization are not open-source. This means that specific development information for these tokens is inaccessible to third parties, raising concerns about the "representativeness" of selected targets. Additionally, the cryptocurrency industry is still in its early stages, offering vast potential for growth. Each token has the possibility of achieving rapid development within a short period of time. Furthermore, the high liquidity nature of the cryptocurrency industry, with 24-hour trading, leads to significant fluctuations in market capitalization within a short timeframe. Drawing a parallel to the A-share market, changing selected targets within a six-month period might cause us to miss out on crucial information regarding token market values.
Therefore, this article takes into account the development information of tokens across the entire industry to calculate IGDAI.
Step 3 What is the cause and effect of the "development" and the "rise in the price of cryptocurrency"? The change in the price of cryptocurrency unilaterally affects the level of development on GitHub.
We conducted a Granger causality test to examine the causal relationship between industry development activity (IGDAI) and changes in BTC price using two-time series datasets spanning the period from 2015 to October 1, 2023. The data is measured on a daily basis.
To begin the analysis, we determined a lag order of 4 and conducted a unit root test to ensure the data's smoothness (a prerequisite for the Granger causality test). The obtained results are as follows:
Where 0.000<0.05, indicating that the F-test rejects the original hypothesis (original hypothesis H0: there is no Granger causality between the two) and BTC_price is the cause of IGDAI, i.e., the industry GitHub development activity IGDAI is affected by the lagged term of the change in the price of coins. 0.135>0.05, indicating that the F-test accepts the original hypothesis and IGDAI is not the cause of BTC _price cause. In summary, the change in coin price unidirectionally affects the degree of industry development activity.
To provide a clearer and more intuitive analysis, we have utilized charts. Given the significant daily fluctuations in the development activity index and the presence of numerous random factors, it can be challenging to grasp the overall trend. To address this, we have employed index smoothing techniques and expanded the time period to a weekly interval. Figure 2 illustrates the variations in the IGDAI index and BTC price from 2015 to the present, using a monthly time frame.
The chart reveals a noticeable pattern: the industry development ecosystem has shown a lag compared to the BTC price at various points in time. Both indicators have exhibited similar fluctuations, further supporting the conclusion that changes in cryptocurrency prices unidirectionally impact the IGDAI.
Moreover, our analysis has uncovered a concerning trend in recent months. The industry development activity index has experienced a significant decline of 37%, marking the largest drop observed in nearly a decade.
Step 4 Even if the development team remains committed and continues their efforts during the bear market, there is no guarantee that the coin price will experience a significant surge.
In the Step 3 section, we reached the conclusion that there is a unidirectional relationship between coin price and technology development based on the results of Granger causality tests. However, we also wanted to investigate if there is a distinctive relationship: even if the level of GitHub development does not directly influence the rise or fall of coin prices, whether the coin price performance remains stable as long as the development team maintains focus, continues to develop, and survives the bear market. Given the evolving maturity of the token development ecosystem and the changing landscape of token types, we decided to identify tokens that have demonstrated consistent development throughout 2018. Our aim was to compare the relationship between their GitHub development activity (GDAI) and coin price fluctuations with that of BTC.
Among them, we define "continuous development" as the GitHub development core's commit, issues, and pull requests factors being zero for each week of the time period from 2018 to October 2023, and the coin price increase or decrease is defined as the period's (highest price - lowest price) / (lowest price). Minimum price. Through massive data crawling and analysis, we first determined that there are about 1,400 tokens open-sourced and listed at the same time from 2018 to the present, and found 38 out of 1,400 tokens that meet the above conditions (which includes BTC and ETH, considering that the development ecology and market value of BTC and ETH are already very mature and representative, and considering the length of the article, we focus on the elaboration of the paper). (the results of the remaining 36 tokens compared with BTC). The list of specific tokens is shown in Table 6:
Regarding the GitHub development activity GDAI, 38 token cases were counted to obtain Figure 3:
Red color indicates tokens with IGDAI exceeding BTC, and blue color indicates those that have not. Among the continuously developed tokens, there are 9 tokens whose development activity exceeds that of BTC.
Regarding the token price rise and fall, Figure 4 is obtained:
The tokens have been color-coded to provide a clear distinction: red indicates tokens with coin price changes surpassing BTC, while blue indicates tokens that do not. Out of the consistently developed tokens, 31 tokens have experienced a coin price increase that surpasses BTC.
Analyzing the two charts, we find that there are 8 tokens in red that overlap with each other. This means that from 2018 until now, these 8 tokens have demonstrated superior performance in both GitHub development activity (GDAI) and coin price changes compared to BTC, which is considered an industry benchmark. These 8 tokens account for 22% of all the tokens that have been continuously developed within this timeframe. For a detailed list of these tokens, please refer to Table 7.
Considering the aspect of continuous development, the overlap rate of 22% indicates a relatively low correlation. Therefore, we can conclude that continuous development does have some impact on coin prices, but we cannot definitively state that it has a significantly positive pulling effect on coin prices. This perspective aligns with the findings of the Granger causality test conducted in Step 3.
In Conclusion
Based on the findings presented in this paper, Falcon summarizes the conclusions as follows:
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Through hierarchical analysis, this study introduces the development activity index (GDAI) for individual tokens and establishes the industry-wide GitHub development activity index (IGDAI) for the entire industry.
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Upon analyzing the IGDAI and BTC price data from 2015 to October 2023, it was observed that the coin price only influences GitHub development activity in a unidirectional manner. Additionally, in recent months, the industry's development activity index has experienced a significant decline of 31.7%, marking the largest drop in nearly a decade.
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It is important to note that the "teams continuing development without swinging for the fences" alone is not a primary driver of a rising coin price following a bear market. When making investment decisions, it is crucial to consider other factors and their potential impact on the price
About LUCIDA & FALCON
Lucida （https://www.lucida.fund/ ） is an industry-leading quantitative hedge fund. Officially entered the Crypto Industry in April 2018, Lucida develops CTA Strategy / Statistical Arbitrage Strategy / the Arbitrage of Option Implied Volatility Strategy, at present, the team manages 30 million dollars, including 14 million dollars of proprietary assets.
Falcon （https://falcon.lucida.fund /）is a Web3 investment infrastructure driven by multi-factor models and assists users in “selecting,” “buying,” “managing,” and “selling” crypto assets. Falcon is a product incubated within LUCIDA in June 2022.
More Information https://linktr.ee/lucida_and_falcon