Using On-Chain and Market Metrics to Analyze the Value of Crypto Assets

Ethereum and the ERC-20 standard

Ethereum, the second-largest blockchain by market capitalization, was created as an open-source blockchain that allowed developers to deploy and run their own program codes with a Turing-complete computer language on the blockchain to create a platform of decentralized applications (Buterin, 2013, p. 13). These program codes are also called smart contracts.

The importance of analyzing ERC-20 tokens

By examining the blockchain-related academic literature, we see two shortcomings. First, while the analyzed literature mostly studied Bitcoin metrics, hardly any analysis exists on the time series of ERC-20 tokens. The relevance of these tokens is given, as they cover a wide diversity of economic purposes and became an integral part in venture financing (Ante, Sandner, & Fiedler, 2018, p. 15). Second, the examined literature pays little attention to the publicly available transactional data and their relationship to the market price of crypto assets. Considering that blockchain data can be sourced with unquestionable accuracy, this opportunity paves the way to unparalleled insights.

Literature review on on-chain & market metrics

User base. The idea of studying the user base of a crypto asset is based on network effects. The rationale is that as the number of users of a communication service inclines, so will the utility for the individual user (Tucker, 2008, p. 2025). Based on this effect, Metcalfe stated that the utility of a two-way telecommunication network could be expressed by the nodes of the network (Shapiro & Varian, 1999, p. 184). For N linked devices, each device would be connected to N-1 other devices. Hence, the number of unique connections, and according to Metcalfe, also the value of the network V, equals N x (N-1).[ii] Metcalfe’s formulation would later become known as Metcalfe’s Law (Metcalfe, 2013, p. 27).[iii] In the literature, Metcalfe’s Law has been applied to a few crypto assets. Alabi (2017) valued Bitcoin, Ethereum (excluding ERC-20 tokens), and Dash via Metcalfe’s Law. He selected daily unique addresses to approximate the user base and found that the unique addresses fit the price development and that large deviations may imply value bubbles (Alabi, 2017, p. 13). Metcalfe’s Law was also applied to study Bitcoin by Wheatley et al. (2018) and the 50th largest ERC-20 tokens according to their market capitalization by Lehnberg (2018). In both cases, it was found that an exponent smaller than two better approximates the price of the tokens, and in the latter case that using monthly active addresses as a valuation metric performed better than using weekly and daily unique addresses. Thus, we expect a positive relationship between the token price and unique addresses as an approximation for the user base.


Table 1 provides an overview of the variables used to operationalize the factors. To mitigate issues of heteroscedasticity we apply the logarithmic transformation.

Table 1: Definition of variables used in the model

Data acquisition

The ERC-20 tokens to be analyzed are selected based on the following criteria. First, the top tokens according to their market capitalization as of July 15, 2019, have been shortlisted (CoinMarketCap, 2019). Second, we excluded pegged payment tokens, since their value is predefined and recently launched tokens, due to short time-series. Third, we also excluded tokens that migrated to their mainnet, respectively are used on several public blockchains, since not all of their transactions happened on Ethereum’s mainnet.[viii] The on-chain data of the Ethereum blockchain is sourced from a relational database, provided by (2019). We used the contract addresses of the tokens to filter the relevant transactions. The same process was done for the group of stablecoins.


To avoid spurious results when analyzing the time series of each token, it is essential to test the characteristics of time processes which may exhibit interdependency and non-stationarity, meaning that its mean, variance, and covariance are not constant over time. Thus, an OLS regression may not be the appropriate choice.

Regression analysis

First, the augmented Dickey-Fuller (ADF) test needs to be performed.[xi] If the variable of interest is stationary at the level form, the ADF is not performed on the first difference. Among all ERC-20 tokens, the metrics associated with exchange addresses were always stationary at the level form. This was expected as the exchange numbers have been set into relation to the total amount of tokens transferred the same day. Hence, the number ranges between 0 and 1. The results for the other variables differ. In the case that no constant and no trend variables were used, the time series of all variables are non-stationary at level form. For the cases that a constant, or a constant and trend was applied, the examined time series became in some cases stationary at the level form. The outcome of the ADF test unveiled that all variables are either stationary at the level form or the first difference and thus, meet the requirement for the ARDL cointegration test.

Table 2: Bounds test results of the selected ERC-20 tokens
Table 3: Estimation results of tokens exhibiting cointegration

Interpretation of the results

User base. The long-term estimation mostly confirms that there is a positive, statistically significant relationship for LINK, BAT, ZRX, AE, and SNT. There is no case of a significant, negative coefficient. A reason why the coefficients of these assets are not significant for all tokens could be that they are predominantly exchanged on central platforms due to their utility purpose. Trades on these platforms cannot be traced, and the results become distorted. A more detailed elaboration of the on-chain data may give further insights.


We examined the relationship of on-chain and market metrics on the price development of six ERC-20 tokens. This paper contributes by shedding light on the price drivers of the ERC-20 environment as previous literature primarily highlighted metrics that affected the Bitcoin price. The ecosystem of Ethereum is of particular interest due to its ability to represent a variety of tokens with a variety of purposes. Out of this environment, many projects grew and started their own mainnet, emphasizing the importance of comprehending these tokens. Based on the publicly available blockchain transactions and the market data, we tested the metrics that have been derived from our literature review. The metrics are either directly determined from the literature or derived from related investment classes. The examined metrics are monthly active addresses of the token of interest and stablecoins as a whole, the daily transactions from and to exchange addresses, as well as the Bitcoin price as the market factor. We applied the ARDL/EC model to address the issues of non-stationary time series. The examination on the tokens unveiled that out of the analyzed tokens, six exhibited cointegration.


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Alexander Stober studied Business Informatics (Bachelor) and Finance (Master) at the Frankfurt School of Finance and Management. You can contact him via mail (


[i] For more information regarding the required and optional functions an ERC-20 token has to meet, see Bheemaiah & Collomb (2018, p. 58 ff) and Victor & Lüders (2019, p. 3).



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Philipp Sandner

Philipp Sandner

Professor | Lecturer | Author | Investor | Frankfurt School Blockchain Center