Dissecting Bitcoin's “4 Year Cycle”

To investors,

Today is a guest post from Will Clemente, co-founder of Reflexivity Research, on the 4-year cycle of bitcoin. This is an in-depth analysis that should shed light on one of the big questions in the bitcoin community. You can subscribe to Reflexivity’s research by clicking here.

If you’ve been in the digital asset space for any non-trivial period of time you have likely heard many crypto market participants and market commentators refer to the “4 year cycle”. When participants first meet each other one of the first questions that is often proposed is “How many cycles have you been around for?”

In this report we provide an overview of what the 4-year cycle means, but more interestingly the underlying behavioral dynamics that shape this cyclicality and whether the phenomenon roots from the Bitcoin supply halving, macroeconomic factors, or self-reinforced human psychology.

Does the Halving Drive the 4-year Cycle?

The Bitcoin halving refers to when the rate that new Bitcoin issuance comes into circulation cuts in half. Currently there are approximately 900 Bitcoin that are introduced into circulation every day. This number will cut in half when the next upcoming halving takes place in late Q1/early Q2 of next year to 450 Bitcoin introduced into circulation every day. The 3 halvings that have taken place to date in 2012, 2016, and 2020 are shown below by the vertical dotted lines in the chart below as well as the drop in issuance shown in the orange line.

In theory, when the halving takes place - even if the demand for Bitcoin stays the same - the rate of new supply issuance should cause a larg enough supply/demand imbalance that price should have an upwards drift in the period of time following the event, which can provide an initial impulse of momentum that kicks off a multi-year Bitcoin bull market. The chart below shows the decline in the 90-day change of issuance relative to the going rate of issuance.

It is worth noting that even if this is true, the impulse that this supply/demand imbalance creates should diminish over time as the declining rate of issuance relative to overall circulating supply diminishes.

After this initial impulse that sparks momentum in Bitcoin’s price, the momentum of the bull market continues and liquidity within the crypto market disperses from Bitcoin as the largest (arguably most boring) solidified “value” asset into Ethereum and eventually down the risk curve into longer tail assets; a market dynamic referred to as breadth. This liquidity dispersion takes place until there are no longer enough new crypto market inflows to support the amount of assets being lifted by correlation to majors as well as new projects being created that compete with the overall pool of crypto asset supply (which is arguably just beta to Bitcoin).

Eventually this unsustainable behavior implodes in on itself and this entire dynamic reverses; liquidity flows back from the longer tail assets into Bitcoin and recently Ethereum (which is undeniable by seeing how well ETH/BTC held up throughout this bear market) and allows the cycle of liquidity flow to start anew from a place of concentration in value. For individuals coming from traditional finance this is not a new concept by any means, as typically riskier assets underperform in the early stages of a bull market and by the end outperform as active managers rotate down the risk curve to get more beta relative to “value” assets.

Do Behavioral Dynamics Drive the 4-year Cycle?

There is also an argument to be made that Bitcoin-native behavioral dynamics and market psychology may be playing the largest contributing factor to crypto market cyclicality. To break this down we’ll take a look at Bitcoin’s economics measured by blockchain data. Ultimately Bitcoin’s price paired with the profitability of the market participants that are active on the network are arguably the biggest contributing factor to what fuels Bitcoin’s behavioral dynamics.

From a first principles thought process, when market participants are sitting on a large amount of unrealized profit there is a higher likelihood that they will look to sell on any drawdown in fear of losing those unrealized gains. Market participants who buy after an asset is substantially up in price in a short period of time are usually (yes, this is a generalized statement) less sophisticated and/or convinced in the asset’s long term value proposition.

These factors combined make the holder base more fragile than the bedrock holder base during the depths of a bear market. Now that we’ve reasoned through how profitability of market participants should affect market cyclicality, let’s take a look at some real underlying Bitcoin economic data to validate/invalidate this idea.

Profitability

Below we are looking at several metrics that fall under the category of cost basis. We’ve talked about realized price in prior reports and why it serves as a proxy for the aggregated cost basis of the network, but as a refresher; realized price is calculated by taking the price that every coin in circulating supply last moved and multiplying each coin by the price (derived from the timestamp of the coin movement) that the coin last moved. We can then take this value (realized capitalization) and divide it by circulating supply to get realized price.

This realized price value can be applied to the heuristics that the data science team at Glassnode have done to identify long and short-term holders based on their statistical likelihood to spent coins from their wallets and get short and long term holder realized price respectively.

Below you can see aggregated realized price in orange, long term holder realized price in blue, and short-term holder realized price in pink. Whenever the market is below all three of these cost basis’ it indicates that the network, in aggregate, is underwater (in a state of unrealized losses) and is therefore likely to capitulate. Conversely, whenever the current marginal trading price for Bitcoin vastly overshoots these cost basis’ this indicates that the market is in a state of large unrealized gains and therefore is likely to realize some of those gains.

To measure the delta between the current marginal trading price and the aggregated cost basis of the network we can look at the chart below which is market price divided by realized price. This shows the phenomenon we just described in a very simple oscillator.

Another way to visualize this same dynamic is by looking at an on-chain metric called net unrealized profit/loss, which is calculated as (market cap – realized cap / market cap). This is done because by subtracting realized cap from market cap we can gauge whether the market is in a state of unrealized profit or less and then divide this value by market cap to see the value on a relative basis to historical readings. Very similar to MVRV, as you can see whenever the metric reaches high readings, identified as euphoria-greed in blue, this has marked the top of Bitcoin multi-year cycles. This is because market participants are sitting on a large amount of unrealized profit. (You can also look at realized profit & loss, as opposed to unrealized, as a gauge for capitulatory periods)

With this data backing up our hypothesis we can conclude that tops are marked by high amounts of unrealized profits paired with lack of new buyers paired with market dispersion from Bitcoin to ETH to alts very similar to traditional market cycles where capital flows from deep value down the risk curve until new inflows can no longer support dispersion and the entire market implodes in on itself.

Holder Base

Another dynamic to keep an eye on is the quality of the holder base of Bitcoin. One way to do this is by looking at on-chain entities by the amount of supply that they hold. Below we contrast the amount of supply held by on-chain entities with over 1,000 BTC and on-chain entities with between 0.1-1 BTC.

In 2017 there was clear distribution from “whales” to “fish” but over the last few years we have seen what appears to be accumulation from smaller entities while whales have been offloading inventory. It is worth noting that the signal of this data appears to have diminished with the rise of custodial solutions that hold assets on behalf of funds and high-net-worth individuals. There is not much to actionably conclude from this data outside of the fact that smaller entities continue to accumulate BTC throughout its entire lifespan which makes Bitcoin’s supply distribution more favorable as a byproduct.

We can also look at the “quality” of the market participants holding Bitcoin based on their statistical likelihood to spend their supply. This data science was done by the Glassnode team, who realized that the likelihood for BTC to be spent dropped off dramatically after being held in a wallet for 155 days (5 months). This threshold is used to deem an on-chain entity a short- or long-term holder. Below we can see supply held by long term holders in blue and supply held by short term holders in red.

In bear markets we see a rotation of supply from short term holders to long term holders, which reflects short term holders capitulating their supply as well as aging past the 155-day threshold into long term holders. Heading into the bear market we see long-term holders distributing their holdings back to short-term holders (new market participants). This relationship between short- and longterm holders is a simplified visual of the underlying current that drives Bitcoin’s cyclicality.

Miners impact on the market

Another important factor to consider is the role of Bitcoin miners on the overall market. Historically miners have been pro-cyclical forces for BTC as they accumulate BTC when profitable in the bull and become forced sellers in the bear market. However, miners’ impact on the Bitcoin market overall has diminished over time. Below we can see thermocapitalization, created as a proxy for mining resources spent; which is calculated by taking aggregated coinbase transactions multiplied by the USD value of each transaction at the time they were mined, compared to realized capitalization. This metric shows a decline in overall market impact from miners.

Do Macroeconomic Factors Drive the 4-year Cycle?

There is another argument to be made that Bitcoin simply follows the broader cyclicality of global macroeconomic dynamics. While the intent of this report is to focus on crypto native data, we’ll look at two macro indicators to make a general point. Below we can have overlay of Bitcoin’s price with PMI, which is a survey-based indicator of business conditions, which includes individual measures of business output, new orders, employment, costs, selling prices, exports, purchasing activity, and more. As you can see Bitcoin moves with a high correlation to PMI but shows divergences during major declines (like the current one) which can likely be attributed to shifts in liquidity conditions to combat the declining economic conditions reflected by the declining PMI readings.

Bitcoin also has shown a clear inverse correlation to the year over year change in the US dollar index DXY, which measures the US dollar relative to other fiat currencies. Declining dollar strength has historically been bullish for Bitcoin and increasing dollar strength has historically been bearish for Bitcoin.

These factors surely play a large role in Bitcoin’s price action and have been eerily closely tied into the timing of the Bitcoin 4-year cycle.

Conclusion

In conclusion, the factors that drive Bitcoin’s cyclicality are far more nuanced than one may think at first glance. The underlying shifts in the macroeconomic landscape that have coincided with Bitcoin’s halving make it difficult to differentiate which of the two is the larger driver and compartmentalize correlation from causation. However, with the halvings having a diminishing effect it is likely that the shifts in the global macroeconomic landscape play the largest role and will continue to play an increasingly large role in Bitcoin’s price action as it becomes a more solidified asset and the set of market participants shift.

Today is a guest post from Will Clemente, co-founder of Reflexivity Research, on the 4-year cycle of bitcoin. This is an in-depth analysis that should shed light on one of the big questions in the bitcoin community.

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