- Bitcoin has returned 1,013% (10-fold increase) over the past 12 months and 1,000,000% since 2012
- Our model forecasts US$100,000 per Bitcoin within four months, but expect sizeable pullbacks
- The decentralised nature of crypto skews it towards higher volatility because liquidity is so fragmented
The fact that the only logical method of plotting the Bitcoin price over a multi-year horizon is with a log scale speaks volumes about the magnitude of its price movements, both on the upside and the downside.
Our machine-learning model forecasts a break of US$100,000 within the next four months, followed by a 65% drop to US39,000 going into the end of 2021. A move down to this area should once again attract enough buying to catapult prices back to all-time highs at the beginning of 2022, with the 12-month projection being US$120,000 – around a 100% increase from recent levels.
The eight-year logarithmic channel depicted on the price chart above has been highly influential, with it accurately pinpointing the 2020 turning point of US$4,001, which ultimately led to where we sit today, at cUS$56,000. We can expect this channel to continue to influence price action, with the top of the channel sitting at close to US$500,000 at the end of the 12-month projection.
This sort of valuation may reek of over-exuberance and perhaps even be labelled as a fantasy; however, taking a look at the historical returns and volatility highlights its plausibility.
If you were lucky enough to have invested in Bitcoin in March 2012, exactly nine years ago, you would now have attained a 1 million percent return on your investment. For every US$1 invested you would now have US$10,000.
Bitcoin booms and busts
To gain a better perspective of the wild swings in Bitcoin, let's take a look at the Bitcoin performance over each year since 2011. Years 2014 and 2018 experienced drops of 58% and 70%, respectively, but the good years have more than made up for it, with the compound annual growth rate (CAGR) being 189%.
A plot of the interquartile ranges of returns since 2012 shows that 75% of 12-month periods have yielded over 17% for Bitcoin, while the top-performing 25% of periods have produced a whopping 477% return or more.
Looking at the 12-month period returns for every day since 2012 highlights the importance of market timing. 12% of days would have yielded between a 50% and 75% loss in the ensuing 12 months. 1% (18) unlucky days would have seen those losses extend to between 75% and 90%; however, most of the periods would have yielded between a 100% and 500% positive return on your Bitcoin holdings over the 12-months.
The risk-adjusted return of an asset is likely the single most important performance metric for most investors; however, it doesn't seem to be a priority for the traditionally undercapitalised crypto crowd. They tend to seek absolute returns over risk-adjusted returns because riskier assets essentially offer them leverage. With this said, as long as they are prepared to stomach up to 90% drop over the short run, they can still be rewarded with above-market risk-adjusted returns during many periods. The below chart highlights the swings in the annualised Sharpe ratio of Bitcoin versus the S&P 500.
Correlation to traditional assets
March 2020 saw a spike in the 12-month correlation between Bitcoin and the S&P 500 as both reacted negatively to news of the pandemic. This correlation continued to strengthen as they both began to recover, with it reaching a high of 0.35 in November 2020. The correlation has since begun to turn back down as Bitcoin relentlessly drives higher.
The 12-month rolling volatility of 30-day returns shows that Bitcoin volatility is on the rise once again, despite being appreciably lower than previous peaks in 2014 and 2018. As it stands today, compared with the S&P 500, Bitcoin is approximately four times as volatile.
The decentralisation effect
Heavily decentralised markets have a skew towards higher volatility because the liquidity is fragmented across many exchanges. Large market orders on one exchange can rip through the order book at a much faster rate, which in turn triggers arbitragers to adjust the price on other exchanges to the same level – all without having to fight against a centralised single wall of liquidity.
Read my primer on Quantitative Technical Analysis here (complete with a video on my methods and the benefits of combining traditional technical analysis techniques with cutting-edge machine learning).
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This report is independent investment research as contemplated by COBS 12.2 of the FCA Handbook and is a research recommendation under COBS 12.4 of the FCA Handbook. Where it is not technically a res...