Bitcoin Mining Revenue Analytical Model

The analysis of revenue of public bitcoin mining companies

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Last time we started to talk about how to analyze how the revenue of bitcoin mining companies is affected by:

• changes in bitcoins mined,

• relative shares of global hash rates, and

• market prices.

We talked about:

• the underlying data used to build our model,

• issues observed,

• the way we addressed those issues, and

• whether we were able to resolve these issues properly.

Now we continue to:

• we will consider the local context for observed companies by performing a visual analysis of the consistency of overtime changes in cryptomining revenue against changes in hashrates, the quantity of self-mined Bitcoins, and the average price at which these Bitcoins were recognized as revenue. This will allow us to assess whether the changes in entity hashrate are representative metrics in the context of previously highlighted issues with the quality of this metric,

• we will further consider the global network stats to create a global context for this dynamic,

• finally, we will present the analytical model that we created as a result.

Company (Local) context

First, we want to visually review the local factor dynamics.

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As you can see, in multiple instances (GREE, GRYP, ARBK) the relationship between the hashrate of the entity and changes in its revenue does not appear consistent. This can be attributed to the changes in the global context of cryptocurrency mining operations.

Network (global) context

Now, global factors:

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We reviewed the changes in network revenue along with changes in the network hash rate, as well as changes in market price, level of block subsidies, and transaction fees charged during the period. Note that changes in the market price and level of subsidies have the most prominent and persistent effect on the network revenue.

Factor Analysis

We used the basic revenue equation model in our analysis:

\(\begin{equation} \text{Revenue} = \text{Price} \times \text{Quantity} \end{equation}\)

Revenue is generally proportional to the share of the Company’s share in the number of BTC received by miners as rewards globally. The individual entity’s share is calculated by the division of the miner's own hashrate by the global hashrate of the network. Therefore, we can restructure the equation above as follows:

\(\begin{equation} \text{Cryptomining Revenue} = \frac{\text{Entity Hashrate, EH/s}}{\text{Network Hashrate, EH/s}} \times \text{Global Earnings, BTC} \times \text{BTC Price, USD} \end{equation}\)

We can now analyze the revenue change due to changes in factors: