# A New Way To Find Discount Rates in Crypto Models

If you have been following along with crypto valuation models, you would know that they are very new and based on a few key assumptions. (If you haven’t been following along start with the article “Cryptoasset Valuations” by Chris Burniske) One of the most apparent assumptions is finding a discount rate for any given crypto asset. A discount rate is the rate at which you discount future cash flows or utility value back to its present value. However, the issue with crypto assets is there are no requirements to release any 10ks or any sort of financial statements that would allow investors to calculate the Weighted Average Cost of Capital(WACC) of a project. Because of this, there is no way to quantify crypto discount rates, thus analysts are currently having to pick arbitrary rates that they believe are suitable based off of the risk of a project. In this piece, I look at current issues with discount rates in crypto valuation models and introduce a first iteration model to quantify discount rates for crypto assets.

You can download the model and follow along here

As I mentioned above, Discount Rates have been an arbitrary number used to estimate risk by an analyst. Instead, I offer up the Discount Rate Matrix: a way to both quantify a discount rate and better understand the risk of a crypto asset. Often used in statistics, matrix’s are a rectangular array of numbers, symbols, or expressions, arranged in rows and columns, that compare two different attributes. In this case, the Discount Rate Matrix compares the Beta of a crypto asset on the X axis and the Price to Estimated Assets on the Y. More on these below.

First, why I chose these two metrics before diving deeper into both of them. “In a broad sense, a company finances its assets either through debt or with equity. WACC is the average of the costs of these types of financing.” It is this idea of financing that I draw from when creating this matrix. One criticism of this model that I foresee is the argument that these decentralized projects and protocols are not corporations. And though I agree, I would push back by saying that early stage decentralized projects still suffer from similar issues to early stage centralized companies, raising capital to operate at capacity for example.

On the X axis of the Matrix is BETA. “Beta is a measure of the volatility, or systematic risk, of a security or a portfolio in comparison to the entire market or a benchmark.” (Investopedia) This stems from the cost of equity in the WACC equation. To find the cost of Equity, you use the CAPM formula. Though CAPM’s validity in Crypto is definitely questionable, does the risk free rate apply to crypto and what is the accepted way to measure market returns; I think taking BETA from the equation is an excellent way of assessing risk. I currently use BTC as my benchmark for my BETA analysis due to it making up a significant portion of the market as well as its ability to drive the market. If crypto assets were to become more decoupled from Bitcoin or it’s market dominance were to fall, I would switch over to a weighted benchmark. I also use daily returns since the crypto asset became tradeable on exchanges.

In the Matrix model you find the BETA calculation in the 4th sheet titled BETA (imagine that). To use it, all you have to do is insert the date and price in the yellow square. Note: make sure the dates match.

Now onto the Y axis: Price to Estimated Assets per share

This model incorporates the idea of financing from the WACC equation, and comes from a estimated net assets>total market cap crypto screener I created a few months ago. The model has been changed to calculate the price to the estimated assets per share to create a ratio similar to the P/E or P/B ratio.

You can read about it in this twitter thread here

With no mandatory financial statements and no 10Ks it is incredibly difficult to estimate whether a project has enough money to keep the lights on, let alone enough capital to create world changing decentralized solutions. Though projects themselves are not transparent, the Ethereum blockchain that they raise capital on is.

From here we make a few key assumptions that drive this model.

- All ETH sent from a project’s wallet is either covering an expense, a payment of some sort or being sold for cash to pay for future business operations.
- A portion of the ETH sold is simply to hedge against Ethereum price volatility and is still an asset on a project’s balance sheet.
- All other ETH sold is sold to cover business operations and is no longer an asset on a project’s balance sheet.

With that in mind, we can find the total amount of ETH sold, subtract it from the total amount of ETH raised, and multiply it by the current price of ETH to find the total dollar amount of ETH still on a company’s balance sheet. This, and the hedging amounts all happen in the third row of tables on the third tab of the model titled analysis. The percent hedged is the hardest part to quantify. I have found the best way to get this information is simply to email developers and advisors of the project and ask how much of the ICO raise did they sell to hedge.

ETH isn’t the only measurable asset on a project’s balance sheet. We must also look at the supply and vesting schedule of the project’s native token.

Here we break down the different supply side outputs of the native token. We add any token that is held specifically for development as an asset on the balance sheet. Development Fund and Foundation are usually the main two supply side outputs in your typical token release. Token releases often include a vesting and lock up schedule, that is why there is a start period and the current period in the outputs side of the model. As you can see with 0x project the total number of native tokens on their balance sheet shrank by 45m from 300m to 255m but because the price has appreciated so much the total value of their native tokens on their balance sheet has grown from 21m to 138m.

Now we can sum the three assets on the balance sheet. As you can see in the 4th row of tables, 0x has an estimated assets of 166m. Dividing that by the total supply, we get $0.65 of assets per share. We then divide the current price by the assets per share to get the Price to Estimated Assets Ratio. 0x has a P/EA of 0.835. Similarly to a P/E ratio, the lower the P/EA ratio is, the better.

Now that we have both the BETA and the P/EA of 0x we can go back to the discount Matrix and find the discount rate to use for 0x’s velocity model.

And from there we can plug the 21% discount rate into our Velocity Model. To get the discounted utility value. Dividing the total of all the discounted utility value by the circulating supply leaves us with the fair value per share.

Like I said in the Introduction, I see this as a first iteration model to help solve the problem of calculating discount rates in crypto valuation models. Lets go over some of the strengths and weaknesses of this model.

Weaknesses

- There is no set calculation similar to calculating WACC. Because of this, and because the model uses a matrix, it is still not as accurate as traditional finance models. You are still rounding values to use the matrix instead of an exact number.
- We are replacing assumptions with other assumptions. Though I would argue that the matrix is much more accurate than arbitrarily guessing the discount rate based on your opinion of the crypto asset’s risk. But the Price to Estimated Assets is just that, an estimate. Furthermore it does not take into consideration property, plant, and equipment since they are off-chain. I look forward to disclosures from projects being normal practice.
- Finally this model as a whole does not paint the full picture. A hypothetical project that sold all of it’s raised ETH in November would have a really good P/EA ratio, and thus have a ton of capital to fund development and operate their project. Or they could be a fraudulent team that took all the capital and ran, not building anything other than a tradable ERC-20 token. Additional research is always required.

Strengths

This model helps better quantify risk and provides a way to calculate a discount rate based on the specific project you are modeling. It has also helped standardize my analysis which will only be beneficial in the long run.

Overall this model is not without its flaws, and it will not be the last version that I make on the topic of Discount Rates. However, it does remove large assumptions from current Crypto Valuation Models. It is my hope that this model sparks a discussion on the subject of calculating discount rates and leads to better models than this one. I look forward to reading about them.

*If you have any comments, thoughts, or ideas feel free to reach me on **twitter**!*