Bitcoin Price Forecast Using Quantitative Models, Part 4

This is the fourth part of a multi-part series that aims to answer the following question: What is the “fundamental value” of Bitcoin? The first part deals with the value of scarcity, The second part – the market moves in bubbles, Part three – the adoption rateand the fourth part: the hash rate and the estimated price of Bitcoin.

Hash rate and estimated price of Bitcoin

In data mining, the term “hash rate” is a security metric. The higher the hash power, the greater its security and resistance to external attacks. It is one thing for a hacker to attack your home computer, but another thing is when a hacker tries to attack tens of thousands of computers around the world at the same time.

The growth of the hash rate is due to the increasing computing power of mining servers, which also means an increase in the costs of exploitation of Bitcoin (BTC). A simple rule tells us that a certain activity must have economic convenience to be sustainable over time. Those who extract oil from the ground must sell it at a cost greater than the cost of extraction, those who produce electricity must sell it at a cost greater than the cost of production, etc.

The same rule applies to Bitcoin mining, so the cost of electricity, the payback of increasingly powerful servers, etc., must be less than the income generated by receiving Bitcoin for the activity performed.

Related: Is Bitcoin a waste of energy? Pros and cons of Bitcoin mining

Therefore, the increasing difficulty of mining Bitcoin must be accompanied by economic convenience.

In the first months of 2010, Bitcoin paid miners around $ 10,000 per month. Today, thanks to the growth in the price of Bitcoin, the network of miners in the world distributes a wealth of more than $ 500 million per month, and this value is destined to grow.

The figure is enormous, although in part proportional to electricity consumption, but it allows us to understand the generation of wealth that this “social experiment” is capable of generating. As we can see in the graph, the growth of the hash rate is higher than the growth of the monthly remuneration. Therefore, to estimate the correct price of Bitcoin based on the hash rate, it is first necessary to understand the trend of the remuneration of each hash unit over time.

As we can see, the dollar remuneration of the hash rate is in sharp decline. This means that security increases almost exponentially over time, but the cost of security drops considerably during that time.

For a better understanding, while the remuneration for each block grows, despite or thanks to the halving that increases the scarcity, the difficulty of undermining a new block increases much faster, at least for now. Therefore, the price / hash rate relationship decreases because the denominator increases more substantially than the numerator.

So, to estimate the (non-linear) trend of decrease in pay by hash rate, the function that best represents this trend is, as always, the power law function, as shown in the following figure.

Once we obtain this function by multiplying the two growth functions of the hash rate and payment by a single hash rate, it is possible to obtain the function that approximates the monthly remuneration in US dollars over time.

This result is not close to the value of the price of a single Bitcoin, but rather to the monthly remuneration that grows over time, as can be seen in the previous graph.

To estimate the price of Bitcoin, corrected according to this hash rate metric, it is necessary to divide this value by the average number of Bitcoin that is mined in a given month. In doing so, we obtain the typical step trend of the flow-to-stock model described above.


We can conclude that even in the face of strong volatility and seemingly incomprehensible price movements, the three main factors that drive the price of Bitcoin – scarcity, demand, and cost of production – can be really helpful in understanding Bitcoin price dynamics. movements.

We can argue that there are long-term fundamental value trends that can help consider Bitcoin as a “strategic asset class” of investment.

This article was a co-author of this article. Ruggero Bertelli Y Daniele bernardi.

This article does not contain investment advice or recommendations. Every trade and investment move involves risk, and readers should do their own research when making a decision. The views, thoughts and opinions expressed here belong solely to the authors and do not necessarily reflect or represent the views and opinions of Cointelegraph.

Ruggero Bertelli is professor of financial intermediary economics at the University of Siena. He teaches classes on banking management, credit risk management and financial risk management. Bertelli is a member of the board of Euregio Minibond, an Italian fund specializing in regional bonds for SMEs, and a member of the board and vice president of the Italian bank Prader Bank. He is also an asset management, risk management and asset allocation advisor for institutional investors. As a behavioral finance specialist, Bertelli participates in national financial education programs. In December 2020, he published Cherry hill, a book on behavioral finance and the crisis in financial markets.

Daniele bernardi is a serial entrepreneur constantly seeking innovation. He is the founder of Diaman, a group dedicated to the development of profitable investment strategies that recently successfully issued the PHI Token, a digital currency with the aim of merging traditional finance with crypto assets. Bernardi’s work is oriented to the development of mathematical models, which simplify the decision-making processes of investors and family offices to reduce risks. Bernardi is also President of Investor Magazine Italia SRL and Diaman Tech SRL, and is the CEO of asset management firm Diaman Partners. Additionally, he is the manager of a crypto hedge fund. He is the author of The genesis of crypto assets, a book on 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 payments.

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