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Bitcoin Price Forecasting Using Quantitative Models, Part 4

This is part four of a multipart series that aims to answer the following question: What is the “fundamental value” of bitcoin? part one is about depreciation value, part Two – market moves in bubble, part three – adoption rate, and part four — the hash rate and 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 hashing power, the higher its security and resistance to external attacks. It’s one thing for a hacker to attack your home computer, but it’s another when a hacker tries to attack thousands of computers around the world at the same time.

The increase in hash rate is due to the increasing computing power of mining servers, which means an increase in the cost of bitcoin (B T c) A simple rule tells us that a given activity must have economic convenience in order to be sustainable over time. Those who extract oil from the ground must sell it at a price higher than the cost of extraction, those who produce electricity must sell it at a price higher than the cost of production, and so on.

The same rule applies to bitcoin mining, whereby the cost of electricity, amortization of increasingly powerful servers, etc., must be less than the revenue generated for the activity generated by acquiring bitcoins.

related: Is bitcoin a waste of energy? Advantages and disadvantages of bitcoin mining

Therefore, the increasing difficulty of bitcoin mining must be matched by economic convenience.

In the first months of 2010, bitcoin paid miners about $10,000 per month. Today, thanks to the rise in the price of bitcoin, more than $500 million in assets are distributed per month to networks of miners around the world – and that value is set to increase.

This figure is huge, albeit partly in line with electricity consumption, but it allows us to understand the generation of wealth that this “social experiment” is capable of creating. As we can see from the graph, the increase in hash rate exceeds the increase in monthly remuneration. Therefore, in order to estimate the true price of bitcoin based on hash rate, it is necessary to understand the trend of remuneration for each unit of hash over time.

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

For a better understanding, while the remuneration for each block increases – regardless of or thanks to the halving increasing the reduction – the difficulty of reducing a new block increases very rapidly, at least for now. Therefore, the price/hash rate ratio goes down as the denominator increases by more than the numerator.

Therefore, to estimate the (non-linear) trend of declining remuneration for the hash rate, the function that best represents this trend is, as always, the power law function, as shown in the following figure.

Once we get to this function by multiplying the two functions of hash rate increment and payout by the same hash rate, it is possible to derive a function that estimates monthly remuneration in US dollars over time.

This result does not predict the value of the price of one bitcoin, but the monthly remuneration that has been increasing over time, as can be seen on 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 bitcoins that are mined in a given month. By doing this, we obtain the stepwise trend typical of the stock-to-flow model described earlier.


We can conclude that despite strong volatility and clearly incomprehensible price movements, the major three factors that drive the price of bitcoin – scarcity, demand and cost of production – are important for understanding bitcoin price dynamics. can be really useful. movements.

We could argue that there are long-term fundamental price trends that can help Bitcoin to be considered an investment “strategic asset class”.

This article was co-authored Ruggero Bertelli and Daniel Bernardik.

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

Ruggero Bertelli Professor of Financial Intermediary Economics at the University of Siena. He teaches Banking Management, Credit Risk Management and Financial Risk Management. Bertelli is a board member of Euregio Minibonds, an Italian fund specializing in regional SME bonds, and a board member and vice president of Italian bank Prader Bank. He is also an asset management, risk management and asset allocation consultant to institutional investors. As an applied finance scholar, Bertelli is involved in national financial education programs. In December 2020, he published La Colina de Siligi, a book about behavioral finance and the woes of the financial markets.

Daniel Bernardik Is a serial entrepreneur who is constantly on the lookout for innovation. He is the founder of Dymon, a group dedicated to the development of profitable investment strategies, which recently successfully issued a digital currency, the PHI token, with the goal of merging traditional finance with crypto assets. Bernardi’s work is oriented toward mathematical model development, which simplifies the decision-making processes of investors and family offices for risk reduction. Bernardi is also the chairman of investors’ magazines Italia Srl and Dymon Tech Srl, and CEO of asset management firm Dymon Partners. In addition, he is the manager of a crypto hedge fund. She . is the author of 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 mobile payments sector.

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