In the previous post, we saw how we can forecast Bitcoin price using a linear regression model, one of the simplest and most common approaches. In this post, we will take a look at a few other models that are used by quantitative analysts to forecast Bitcoin price.

Forecasting Bitcoin price using quantitative models, Part 4 Bitcoin has been seen as the new gold for many in the Bitcoin community. It has become a hot topic of discussion, and one that can’t be ignored easily. The price of Bitcoin has been changing pretty rapidly within the past few weeks. As the current Bitcoin price is growing, more and more people are becoming interested in the Bitcoin and Blockchain technology. In this post, we would like to discuss a few techniques that can help us predict the Bitcoin price accurately.

The fourth and final part of this article looks at Bitcoin price prediction. In part 3, I discussed the role of the fundamental, technical and volatility metrics with respect to the prediction of Bitcoin price, and here I am going to discuss their role in the prediction of Bitcoin price in the near-term.. Read more about bitcoin price predictions and let us know what you think.

This is part four of a multi-part series to answer the next question: What is the fundamental value of bitcoin? The first part is about the value of scarcity, the second about market movements in bubbles, the third about the speed of adoption and the fourth about the hashrate and implied price of bitcoin.

Hashrateand estimated price of bitcoin

In data mining, the term hash rate is a security measure. The higher the hash performance, the higher the security and resistance to outside attacks. It’s one thing for a hacker to attack your personal computer, but it’s quite another for him to try to attack tens of thousands of computers around the world at once. The growth of the hash rate is due to the ever-increasing computing power of the mining servers, which also means that the cost of mining bitcoin (BTC) is increasing. A simple rule is that for an activity to be sustainable in the long term it must be economically viable. Those who extract oil from the earth must sell it at a price higher than the cost of production, 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, where the cost of electricity, depreciation of increasingly powerful servers, etc. must be less than the revenue generated by receiving bitcoins for the activity performed. Related: Is bitcoin a waste of energy? Pros and Cons of Bitcoin Mining Therefore, the increasing complexity of bitcoin mining must be accompanied by economic convenience. Forecasting Bitcoin price using quantitative models, Part 4 In the first few months of 2010, bitcoin miners were paying about $10,000 a month. Thanks to the rising price of bitcoin, more than $500 million in assets are distributed to the global network of miners every month – and that value will only increase. This is a huge number, even if it is partially equal to the consumption of electricity, but it gives a glimpse of the wealth this social experiment creates. As you can see in the graph, the growth of hash rate is higher than the growth of monthly earnings. Thus, to properly price bitcoin based on the hash rate, one must first understand the trend of rewards for each hash unit over time. Forecasting Bitcoin price using quantitative models, Part 4 As you can see, the dollar reward of the hash rate is falling. This means that security increases almost exponentially over time, but the cost of security decreases significantly over time. For better understanding : If the reward for each block increases – despite or because of the halving of the deficit – the difficulty of undermining a new block increases much faster, at least for now. So the ratio of price to royalty rate decreases because the denominator increases more than the numerator. So, as always, to estimate the (non-linear) downward trend in hashrate rewards, the function that best represents this trend is a power law function, as shown in the figure below. Forecasting Bitcoin price using quantitative models, Part 4 By obtaining this function by multiplying the two hash rate growth and payoff functions by one hash rate, we can obtain a function that approximates the monthly reward in US dollars over a given period. Forecasting Bitcoin price using quantitative models, Part 4 This result does not roughly reflect the value per bitcoin, but rather the monthly rewards that increase over time, as seen in the previous chart. Forecasting Bitcoin price using quantitative models, Part 4 To estimate the bitcoin price corrected for this hashrate metric, divide this value by the average number of bitcoins mined in a given month. This gives us the typical pattern of the stock-stock-stream model described above.


We can conclude that even in an environment of high volatility and seemingly incomprehensible price movements, the three main factors that determine the price of bitcoin – scarcity, demand and mining costs – can be really useful to understand the dynamics of bitcoin’s price movement. We could argue that there are fundamental long-term value trends that can help to view bitcoin as a strategic asset class to invest in. This article was co-authored by Ruggero Bertelli and Daniele Bernardi. This article contains no investment advice or recommendations. Any investment or business transaction involves risk, and readers should do their own research before making a decision. The views, thoughts and opinions expressed in this document are those of the authors and do not necessarily reflect the views and opinions of Cointelegraph. Ruggiero Bertelli is Professor of Economics of Financial Intermediaries at the University of Siena. He teaches banking, credit risk management and financial risk management. Mr. Bertelli is a member of the Board of Directors of Euregio Minibond, an Italian fund specializing in regional bonds for SMEs, and a member of the Board of Directors and Vice President of Prader Bank in Italy. He also advises institutional investors on asset management, risk management and asset allocation. A researcher in the field of behavioral finance, Bertelli is involved in national financial education programs. In December 2020, he published La Collina dei Ciliegi, a book about behavioral finance and the crisis in the financial markets. Daniele Bernardi is a serial entrepreneur who is constantly looking to innovate. He is the founder of Diaman, a group dedicated to developing profitable investment strategies and recently successfully launched the PHI Token, a digital currency that seeks to combine traditional finance and crypto assets. Dr. Bernardi’s work focuses on developing mathematical models that simplify the decision-making process of investors and family offices, thereby reducing risk. Mr. Bernardi is also chairman of Investors Journal Italia SRL and Diaman Tech SRL, and CEO of asset management firm Diaman Partners. He is also a crypto-currency hedge fund manager. He is the author of Genesis of Cryptoassets, a book on cryptocurrencies. The European Patent Office has recognized him as an inventor for European and Russian patents related to mobile payments. This paper was successfully presented at the World Finance Conference.Here, I’ll describe how I made a prediction using a Nasdaq model to predict the BTC price over the next 24 hours. This model is based on the assumption that the price of each Bitcoin increases in proportion to the value of the USD price of each Bitcoin. This assumption has been verified by other researchers in the past, and so far, it has shown to be reliable. (See updated blog post here: Read more about why did bitcoin spike and let us know what you think.

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