Bitcoin Price Prediction – Reviewing Forecasting Models

Mercuryo

Can the future be predicted through the use of mathematical models, or are price movements “in the hands of Elon Musk”?

Can the future be predicted through the use of mathematical models, or are price movements “in the hands of Elon Musk”?

The price of Bitcoin may seem volatile, but it does obey some rules that are reminiscent of the rules applied to traditional assets traded on exchanges. As such, a number of existing prediction models are applicable to Bitcoin and some new ones were developed to cater to the king of cryptocurrencies. The following is an overview of some of the main forecasting models used for Bitcoin and the results they generate.

Stock-to-Flow

The Stock-to-Flow model is a way of measuring the sufficiency of a particular resource. The stock-to-gain ratio is the amount of a resource stored in reserves divided by its annual production.

Typically, the Stock-to-Flow model is applied to natural resources like gold, for example. For instance, the World Gold Council estimates that around 190,000 tons of the precious metal have been mined thus far. This quantity is called the “stock”. Meanwhile, about 2500-3200 tons of gold are mined annually. This amount is then called the “gain”. Since Bitcoin is mined, such an approach is applicable to it with the input given changing to cater to its dynamics.

It is therefore possible to calculate the Stock-to-Flow ratio, which shows how the annual market supply of a given resource relates to its total supply. The higher the Stock-to-Flow ratio, the less new supply to the market is made relative to the total supply.

In the case of Bitcoin, the Stock-to-Flow model is calculated based on the ratio of the existing volume of the asset and the number of coins, which is added to the market turnover as a result of mining, halving taken into account. With each halving, there are fewer new Bitcoins and at some point, probably around 2140, the last Bitcoin will be mined. However, this does not mean that the network will collapse. In addition to the hash computation rewards, the miners receive transaction processing fees. Currently, these commissions are small, about a fraction of a percent. However, as the reward for calculating new blocks decreases, the transaction processing fees will increase along with the value of Bitcoin. These fees must be maintained at a level that continues to motivate miners. When no new coins are mined, the miners will still continue receiving rewards in the form of commission fees.

There is an assumption that these fees will not be enough to make mining profitable. As a result, the miners will be deprived of all incentives to keep working, which, in turn, will centralize the network and make it insecure.

Some estimates place the price of Bitcoin anywhere between $100,000 to $318,000 in 2021 based on price volatility dynamics and halving effect taken into account.

Elliott Wave Theory

Another popular Bitcoin price prediction tool is the Elliott Wave Theory, which is more of a possible price trajectory than a strict list of target levels that are presented in the Stock-to-Flow model.

Technical analysis on the crypto market not only works, but is also one of the main methods of prediction, because no one can determine the fundamental value of Bitcoin and other coins. The reason why technical analysis is effective is because the principles of decision-making are established by people, the analysis is based on data, the connection with the real economy is a constant, and because the relation between supply / demand still plays a significant role in price formation. In addition, all trading bots operating on indicators and chart models are arranged based on the same analysis models.

The Elliott Wave Theory was developed by Ralph Nelson Elliott to describe price movements in financial markets, in which he observed and identified recurring, fractal wave patterns. Waves can be identified in stock price movements and in consumer behavior. Investors trying to profit from a market trend could be described as “riding a wave”.

The theory identifies waves as impulse waves that set up a pattern and corrective waves that oppose the larger trend. Each set of waves is itself nested within a larger set of waves that adhere to the impulse/corrective pattern, described as a fractal approach to investing. According to Elliott Wave Theory, a trend is a multi-stage market cycle in which extreme highs and lows are reached.

In Elliott’s model, market prices alternate between an impulsive, or motive phase, and a corrective phase on all time scales of trend. Impulses are always subdivided into a set of 5 lower-degree waves, alternating again between motive and corrective character, so that waves 1, 3, and 5 are impulses, and waves 2 and 4 are smaller retraces of waves 1 and 3.

A correct Elliott wave count must observe three rules:

  • Wave 2 never retraces more than 100% of wave 1
  • Wave 3 cannot be the shortest of the three impulse waves, namely waves 1, 3 and 5
  • Wave 4 does not overlap with the price territory of wave 1, except in the rare case of a diagonal triangle formation

A common guideline called “alternation” observes that in a five-wave pattern, waves 2 and 4 often take alternate forms. A simple sharp move in wave 2, for example, suggests a complex mild move in wave 4. Alternation can occur in impulsive and corrective waves. Alternate waves of the same degree must be distinctive and unique in price, time, severity, and construction. All formations can guide influences on market action. The time period covered by each formation, however, is the major deciding factor in the full manifestation of the Rule of Alternation. A sharp counter-trend correction in wave 2 covers a short distance in horizontal units. This should produce a sideways counter-trend correction in wave 4, covering a longer distance in horizontal units, and vice versa. Alternation provides analysts a notice of what not to expect when analyzing wave formations.

The theory can be used on a variety of timeframes, but its non-specific nature makes it difficult to assess the effectiveness of the model.

The Rainbow Chart

The so-called Bitcoin Rainbow Chart is a logarithmic chart of the evolution of the price of Bitcoin using colored bands. This chart was created by Über Holger, CEO of Holger, using a logarithmic regression introduced by the Bitcointalk user trolololo in 2014, with which the colored bands were created. Holger admits that these bands are completely arbitrary and bear no scientific basis, so they are only correct until some point in the future. The theory does not provide any specific timeframes.

Still, the chart does allow users to observe price movements over the long term, ignoring the inevitable disturbances generated by daily volatility, and getting an idea of what were the best times to buy or sell BTC in the past. Holger states that the Rainbow Chart is not investment advice, because past performance is not indicative for future evolution, but divides the price of Bitcoin into eight bands in arbitrary color: bubble, sell, FOMO, bubble formation, HODL, still cheap, accumulate, buy and discounts.

The multi-colored chart reflects the price levels as the purchase of the asset becomes relevant. Depending on the color – from red as the maximum bubble area to dark blue as essentially panic selling – investors receive signals about the possible subsequent movement of Bitcoin.

Despite the renewal of record highs, the exchange rate of BTC / USD is currently directed upwards and is located only in the light green band, which the model defines as “the asset is still cheap”. As in the case of Stock-to-Flow, the reddening of the rainbow chart is predicted only when the rate of BTC breaks into the 6-digit price range within an average 15m timeframe.

Hyperwave Theory

The Hyperwave Theory was popularized on the market by a well-established trader – Tone Vays. The given theory determines the formation of a potential bubble. The chart is broken down into a seven-part market cycle spotting a bearish trend reversal, which is typically revealed at a peak. The Hyperwave structure is similar to the Elliott Wave principle, but pertains only to bearish scenarios.

Hyperwave-based price predictions are said to be controversial, because they assume that the peak of an asset has already been attained. This leads to exaggerated predictions and means that the model is best suited only for use in conjunction with other models for establishing a mean value of the calculated asset.

The Hyperwave theory suggests that any sudden and sharp increase in the value of an asset can be assumed to be a bubble, which will eventually deflate. The theory is controversial, but has been illustrated by the sharp increases and drops of the price of Bitcoin in 2018, and more recently in 2020 and early 2021.

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