Using quantitative models to predict bitcoin prices, part 4

This is the fourth part of a multi-part series designed to answer the following questions: What is the “fundamental value” of Bitcoin?The first part is about The value of scarcity, the second part – The market moves in a bubble,the third part– Adoption rate, And the fourth part-Bitcoin’s hash rate and estimated price.

Hash rate and estimated price of Bitcoin

In data mining, the term “hash rate” is a security indicator. The greater the computing power, the greater its security and ability to resist external attacks. Hacking your home computer is one thing, but when a hacker tries to attack tens of thousands of computers around the world at the same time, it is another.

The increase in the hash rate is due to the continuous increase in the computing power of the mining server, which also means the increase in the cost of Bitcoin mining (Bitcoin). A simple rule tells us that a given activity must be economically convenient in order to continue over time. Those who extract oil from the ground must sell oil at a cost higher than the cost of extraction, those who produce electricity must sell oil at a cost higher than the cost of production, and so on.

The same rules apply to Bitcoin mining, the cost of electricity, the amortization of increasingly powerful servers, etc. must be lower than the revenue generated by receiving Bitcoin for activities.

related: Is Bitcoin a waste of energy?The pros and cons of Bitcoin mining

Therefore, the increase in the difficulty of Bitcoin mining must match the economic convenience.

In the first few months of 2010, Bitcoin paid approximately $10,000 to miners every month. Today, due to the rise in the price of Bitcoin, the global network of miners distributes more than $500 million in wealth every month-and this value is destined to grow.

This number is huge, even if partly commensurate with electricity consumption, it allows us to understand that this “social experiment” can create wealth. From the figure, we can see that the increase in computing power is higher than the increase in monthly salary. Therefore, in order to estimate the correct price of Bitcoin based on the hash rate, it is first necessary to understand the trend of the reward per unit of hash over time.

It can be seen that the dollar remuneration of computing power is falling sharply. This means that over time, security has grown almost exponentially, but over time, the cost of security has dropped significantly.

For a better understanding, although the reward for each block has increased—although the halving has increased scarcity—the difficulty of destroying new blocks has increased even faster, at least for now. Therefore, the price/hash rate ratio decreases because the denominator rises more significantly than the numerator.

Therefore, in order to estimate the (non-linear) trend of the decline in computing power returns, the function that best represents this trend is, as always, the power law function, as shown in the figure below.

Once we obtain this function by multiplying the two functions of hash rate growth and payment by a single hash rate, it is possible to obtain a function that approximates a monthly dollar return.

This result is not approximate to the price of a single bitcoin, but the value of the monthly reward that grows over time, as shown in the figure above.

To estimate the price of bitcoins corrected based on this hash rate metric, this value must be divided by the average number of bitcoins mined in a given month. By doing this, we have obtained the typical ladder trend of the inventory-to-flow model described earlier.

in conclusion

We can conclude that even in the face of strong volatility and obviously incomprehensible price changes, the three main factors affecting the price of Bitcoin-scarcity, demand and production costs-are very useful for understanding the dynamics of Bitcoin price .

We can argue that long-term fundamental value trends help to treat Bitcoin as a “strategic asset class” for investment.

This article is created by Ruggiero Bertelli with Daniel Bernardi.

This article does not contain investment advice or recommendations. Every investment and trading action involves risks, and readers should research on their own when making a decision. The views, thoughts and opinions expressed here are only those of the author, and do not necessarily reflect or represent the views and opinions of Cointelegraph.

Ruggiero Bertelli Professor of Financial Intermediary Economics at the University of Siena. He teaches bank management, credit risk management and financial risk management. Bertelli is a member of the board of directors of Euregio Minibond, an Italian fund that specializes in regional SME bonds, and a member of the board and vice president of Italian bank Prader Bank. He is also an asset management, risk management and asset allocation consultant for institutional investors. As a behavioral finance scholar, Bertelli participated in the National Financial Education Project. In December 2020, he published Cherry tree hill, A book on behavioral finance and financial market crises.

Daniel Bernardi Is a serial entrepreneur who is constantly seeking innovation. He is the founder of Diaman, a group dedicated to developing profitable investment strategies. The group recently successfully issued PHI tokens, a digital currency designed to combine traditional finance with encrypted assets. Bernardi’s work is geared towards mathematical model development, which simplifies the risk reduction decision-making process for investors and family offices. Bernardi is also the Chairman of the investor magazines Italia SRL and Diaman Tech SRL, and the CEO of the asset management company Diaman Partners. In addition, he is also the manager of a crypto hedge fund.He is the author The origin of crypto assets, A book about crypto assets. He was recognized as an “inventor” by the European Patent Office for his European and Russian patents related to the field of mobile payment.

This article has been successfully submitted to the World Finance Conference.