Forecasting Bitcoin price using quantitative models, Part 3

In Part 3 of our 3-part series on forecasting bitcoin price using quantitative models, we’ll be looking at the best model to use for the next few months; the GARCH and Exponential Moving Average (EMA) models.

In the previous parts of this series, we’ve established that a good Bitcoin price forecast can be made using the SMA period, and the MACD period. We will now continue our discussion on the most important of all these periods – the SMA itself. In this part, we will use a mathematical model to show you how to use the SMA period to forecast Bitcoin price with a high degree of accuracy.

Bitcoin has been one of the most important currencies on the market for the last few years, and the numbers are accelerating. According to coinmarketcap.com, the price of Bitcoin has risen from $1200 in 2015 to $3100 in 2017, and the market cap has been increasing at an even higher rate. As a result, blockchain applications such as Bitcoin Gold (BTG), the Proof of Stake (PoS) systems (e.g. Bitcoin Cash, Bitcoin SV, etc), and the Lightning Network (LN) are all becoming increasingly important.. Read more about bitcoin price predictions and let us know what you think.

This article is the third in a series of articles answering the following question: What is the fundamental value of bitcoin? The first part focuses on the scarcity value, the second on the market movement in bubbles, the third on the adoption rate, and the fourth on the hashrate and implicit price of bitcoin.

Recovery rate

If more and more people want a particular good and the same number of units are in circulation, the price will naturally tend to rise. It is the rule of supply and demand that governs all markets in the world. If a year’s tomato crop is destroyed by hail and there are fewer edible tomatoes than expected, it is logical that the market price of tomatoes would rise, since demand has remained the same. But imagine if people suddenly wanted to buy a lot more tomatoes than in previous years. The demand for tomatoes is increasing and the availability of tomatoes is decreasing, so the price will increase much more than in the first case. Demand can increase due to two factors: The number of participants is stable and the number of applications is increasing, or the number of applications is stable but the number of participants is increasing. A combination of both is also possible In the following example, we have only assumed that the number of participants increases with a constant number of goods. So on the one hand, Satoshi Nakamoto decided that bitcoin (BTC) would become rarer over time, and on the other hand, it is possible that the price of bitcoin will rise as more people enter the market. Therefore, it is necessary to look at the adoption rate of cryptocurrencies in global markets to understand where the value of bitcoin is going and, in general, where the asset class of cryptocurrencies could go in the future. The growth of the portfolio is not exactly exponential, but it comes close. To predict their future growth, it is necessary to use a power law function that best estimates their curvature. To do this, first convert the graph to a logarithmic scale and then calculate the function that most closely approximates the graph. Although the function does not take into account possible future increases due to interest that may occur in 2021 as a result of bitcoin’s unexpected growth, this exercise can be used to estimate the growth of the number of wallets over time. Forecasting Bitcoin price using quantitative models, Part 3 To estimate the growth of bitcoin’s value as a function of the number of wallets in circulation, we need to estimate the average amount in each wallet using a fairly simple function: Bitcoin capitalization / number of wallets We now have an estimate of the value of bitcoins that each wallet has on average. However, the data shows a very different story: 70% of wallets hold 0.01 BTC or less, while 2% of wallets hold more than 95% of bitcoins and exchanges hold about 7%. These reports help us understand the huge growth potential of bitcoin in the future, as those who own a large portion of it are clearly not selling it, as they know bitcoin and its potential well. Those with 0.01 BTC or less are encouraged to buy more, and of course new wallets are opened every month. However, if we take the average value, we can isolate the average value, expressed in US dollars, of the contents of these portfolios: Forecasting Bitcoin price using quantitative models, Part 3 Since the average of these deposits is determined by the value of bitcoin, to better estimate the price range that bitcoin can reach, the red dotted line represents the tenth percentile of wallets with deposits in US dollars and the blue dotted line represents the 90th percentile. With this range, we can determine what the capitalization of bitcoin should be over time, based on the assumed adoption rate of bitcoin. This estimate does not take into account several factors and can therefore be very conservative. For institutional investors entering the market, the average amount per portfolio can be much higher than the blue bar in the example. Forecasting Bitcoin price using quantitative models, Part 3 Of course, these estimates should be seen as an intellectual attempt to understand the dynamics of bitcoin, and they absolutely cannot be taken as a suggestion or advice from the authors. This chart shows that the goal of reaching a capitalization of $1 trillion or $1 trillion is far from impossible, especially if interest in bitcoin continues to grow in the coming months. Similar growth is estimated by the creators of the rainbow map: Forecasting Bitcoin price using quantitative models, Part 3 This chart is very useful because it summarizes the observed growth rate of bitcoin’s value and its tendency to form a bubble after each two-for-one split. Of course, there is no guarantee that bitcoin will continue to evolve in this direction, but it is important to know that it may, so that objective and prudent investment decisions can be made based on these assumptions. 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 for innovation. 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 a European and Russian patent on mobile payments. This paper was successfully presented at the World Finance Conference.Part three of this series takes a closer look at the application of quantitative methods in the forecasting of Bitcoin price. These methods are typically used to help determine the viability of a new business idea or to determine the profitability of a new product. In this piece we focus on Bitcoin and we compare the performance of two quantitative methods based on the capital asset pricing model (CAPM) and the dividend discount model (DDM).. Read more about why did bitcoin spike and let us know what you think.

Related Tags:

bitcoin price predictionsbitcoin in 2030bitcoin price analysiswhy did bitcoin spike,People also search for,Privacy settings,How Search works,bitcoin price predictions,bitcoin in 2030,bitcoin price analysis,why did bitcoin spike

More Stories
‘Green mining’ crypto maker Chia Network valued at $500 million ahead of possible IPO