- Certain investors may be optimistic about Bitcoin’s longer-term potential, but unsure about how to value this novel asset and hoping to mitigate volatility. In these instances, traditional “momentum” signals can be used in a risk management framework to provide guidance on when to add to or subtract from Bitcoin allocations.
- Bitcoin’s price has historically shown pronounced evidence of “momentum”–meaning that gains have tended to follow gains, and losses have tended to follow losses.
- Using a variety of simple trading rules, the Grayscale Research team demonstrates how applying trend-following strategies in the past could have served as one tool to help a portfolio capture Bitcoin’s price appreciation, while simultaneously reducing exposure to large drawdowns.
Evidence of price “momentum”–that gains tend to follow gains, and losses tend to follow losses–can be seen amongst almost all asset classes, either at the level of individual securities or for an aggregate index. In fact, a segment of the alternatives industry, Commodity Trading Advisors (CTAs) or managed futures funds, are designed to generate uncorrelated returns primarily using trend-following strategies. The reason these price patterns exist is debated, but researchers typically link these patterns to behavioral factors, including investors’ underreaction to changes in an asset’s fundamentals, and investors’ “herding” (i.e., the tendency to chase previous winners) that causes prices to eventually overshoot fair value.
Momentum effects are especially pronounced in digital asset markets. As shown in Exhibit 1, buying Bitcoin (BTC) when it increased during the previous month has resulted in high returns, whereas buying when Bitcoin fell during the previous month has not. Other assets with notable momentum patterns at the index level include commodities and baskets of the US Dollar against a variety of currencies.
While we believe the best strategy is for investors to hold Bitcoin over the longer-term—and generally eschew strategies employing technical analysis—we explore momentum signals as one tool for investors to manage volatility. For those inclined to trade more actively in the interest of risk management, this approach may offer guidance on when to add to or subtract from crypto allocations. Using a variety of simple trading rules, we demonstrate how applying trend-following strategies historically would have helped a portfolio capture Bitcoin’s price appreciation while lowering volatility and/or reducing exposure to large drawdowns.
Exhibit 1: Bitcoin’s price shows pronounced evidence of momentum
Trend-following strategies use past price changes to indicate the appropriate points to enter or exit an investment allocation, rather than valuation measures or other fundamentals. Trend-following doesn’t aim to forecast specific price levels; rather, these approaches jump on a trend after it has been established, and stay with the trend until price patterns suggest a trend reversal. The goal is to participate in market upside while preserving capital during extended drawdowns.
The simplest trend or momentum indicator is the moving average: a simple average of an asset’s price over a prior period (e.g. 50 days). The logic behind the moving average (“MAVG”) is simply to create a smoothed line that makes longer-term trends easier to spot. Given that assets often exhibit “noise” in the form of short-term volatility, it can be challenging to discern whether a short term movement in price is part of a larger, meaningful trend or just a random fluctuation. By averaging out price over a longer period, the moving average strategy helps to reduce this noise, producing a smoothed line that hopefully identifies the longer-term trend.
A basic moving average strategy for Bitcoin would involve monitoring Bitcoin’s price relative to its average price for the last 50 days (Exhibit 2). When the price of Bitcoin crosses over the 50-day (50d) average, this is interpreted as a bullish signal, and the point at which a long position is initiated. Conversely, when Bitcoin’s price crosses below the 50d moving average, this is viewed as a bearish signal, and the point at which to return to cash. Although trend-following funds often take both long and short positions, here we only consider strategies which return to cash on bearish signals rather than go short.
Exhibit 2: The 50-day moving average is a common momentum indicator
Although this 50d Moving Average Strategy is simple, the effectiveness is notable. From 2012 to present, this momentum-based strategy not only delivered higher annualized returns, but also reduced volatility when compared to a conventional buy and hold approach (see appendix table for details). The improved performance can largely be attributed to the strategy’s ability to mitigate losses during periods of significant price drawdowns, such as those in Q4 2021 and Q2 2022 (Exhibit 3). The 50d Moving Average Strategy also outperformed in terms of Sharpe Ratio, scoring a 1.9 against the buy and hold 1.3, for the full period from January 2012 through July 2023. It’s worth noting that simple moving average strategies are not particularly sensitive to the choice of the “lookback window,” the period over which the moving average is calculated.
Exhibit 3: Moving to cash when price falls below 50d moving average may reduce drawdowns
Building upon the simple moving average strategy, a moving average crossover strategy employs two moving averages–typically a short-term and a long-term one. The “crossover” refers to the points at which the short-term moving average passes through the long-term moving average. For instance, consider a strategy that tracks two moving averages: a short-term 20-day average and a long-term 100-day average (Exhibit 4). When the short-term (20d) moving average crosses above the long-term (100d) moving average, we identify this event as a “bullish crossover.” This is interpreted as a favorable signal suggesting a long position. Conversely, when the short-term moving average falls below the long-term average, we have a “bearish crossover.” This is generally seen as an unfavorable signal, indicating that it might be time to return to cash.
Exhibit 4: The crossover strategy tracks two moving averages
The 20d/100d moving average crossover strategy also would have outperformed a buy-and-hold Bitcoin allocation from 2012 through the present. It boasts an annualized return of 116% and a Sharpe ratio of 1.7 when compared to a buy-and-hold strategy, which returned an annualized 110% and had a Sharpe Ratio of 1.3. Results for the backtested crossover strategies differed somewhat over various periods. For example, during the 2020-2023 period, these strategies produced better risk-adjusted returns but lower total returns compared to a buy-and-hold Bitcoin position (Exhibit 5). The reduction in risk in some periods came at the expense of lower returns.
Exhibit 5: Cross strategy delivered lower total returns but higher risk-adjusted returns during last crypto market cycle
In contrast to the basic moving average strategy, the results from the moving average crossover strategy are more sensitive to the choice of lookback window. To illustrate, we run backtests for the moving average crossover strategy using combinations of lookback windows (Exhibit 6). There's a clear variance in the outcomes depending on the selected lookback periods; certain combinations yield superior Sharpe ratios, indicative of better risk-adjusted performance, while others deliver less satisfactory results. The highest Sharpe ratios occur when the short-term moving average is set to roughly 10-30 days. It is important to stress that the results are based on historical data, and Bitcoin’s price patterns may change over time. Moreover, strategies with relatively short moving averages will produce more trading signals and therefore subject investors to more transaction costs.
Exhibit 6: Crossover strategy risk-adjusted returns maximized when short-term moving average is around 10-30 days
Lastly, we test a strategy based on an exponential moving average. This approach is similar to the basic moving average strategy above, but recent price points are given a higher weight in the trailing average. For the analysis here we use an exponential moving average based on the last 150 days of price data (Exhibit 7).
Exhibit 7: Exponential moving average puts more weight on recent values
As with the basic moving average strategy and the crossover strategy, the exponential moving average approach produces favorable returns in back testing. For the full 2012-2023 sample period, the strategy (which alternates between Bitcoin and cash) produces annualized total returns of 126% and a Sharpe Ratio of 1.9.
Exhibit 8: Hypothetical exponential MAVG strategy would have captured Bitcoin’s upside while reducing drawdowns
Our analysis is subject to a number of caveats. Most importantly, the performance of backtests depends on historical price patterns, which may change in the future. Moreover, all of the hypothetical returns reported here do not take trading fees into account, which means strategies involving a higher number of trades might have their returns overstated.
It bears repeating: this analysis is based purely on price movements, ignoring fundamental factors that can significantly influence asset prices. Ultimately, fundamentals are paramount for the determination of long-term value. Following mechanical trading rules based solely on historical price data can expose an investor to other risks.
Tools for risk management
Bitcoin has delivered exceptionally strong total returns during its short history, albeit with a number of large drawdowns along the way. We believe Bitcoin and the crypto asset class as a whole will continue to offer attractive returns over the coming years, and the best way to ensure that investors capture its upside potential is simply to buy and hold the coins. At the same time, certain investors may be uncertain how to value the asset and cautious about its volatility. Investors hoping to capture Bitcoin’s price appreciation while managing volatility and/or drawdown risks could consider applying momentum signals and trend-following. We've demonstrated how these tools and strategies can provide guidance for when to add to or subtract from Bitcoin allocations. When correctly applied, they would have historically resulted in improved risk-adjusted returns–in both long-only and long/short portfolios. Therefore, applying momentum signals as part of a crypto allocation risk management framework could potentially improve a portfolio’s return profile over time.
Appendix: Strategy Returns
 Exponential moving average places more weight on recent price observations; the weight on earlier observations decays exponentially over the lookback window.
 Sharpe Ratio equals an asset’s annualized excess return (relative to cash) divided by its annualized volatility, and is a common measure of risk-adjusted performance.
 All strategy results refer to the period from January 1, 2012 through July 31, 2023.
 For a cash return proxy we use the Bloomberg 1-3 Month Treasury Bill Index. This index represents the return an investor would receive from holding short-term government securities.
 See, for example, Asness, Moskowitz, and Pedersen, “Value and Momentum Everywhere.” Journal of Finance, 2013; and Moskowitz, Ooi, and Pedersen, “Time Series Momentum.” Journal of Financial Economics, 2012.
 See, for example, Liu and Tsyvinski, “Risks and Returns of Cryptocurrency.” The Review of Financial Studies, 2021; and Harvey et al, “An Investor's Guide to Crypto.” The Journal of Portfolio Management, 2022.
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Investors are not to construe this content as legal, tax or investment advice, and should consult their own advisors concerning an investment in digital assets. The price and value of assets referred to in this content and the income from them may fluctuate. Past performance is not indicative of the future performance of any assets referred to herein. Fluctuations in exchange rates could have adverse effects on the value or price of, or income derived from, certain investments.
Certain of the statements contained herein may be statements of future expectations and other forward-looking statements that are based on Grayscale’s views and assumptions and involve known and unknown risks and uncertainties that could cause actual results, performance, or events to differ materially from those expressed or implied in such statements. In addition to statements that are forward-looking by reason of context, the words “may, will, should, could, can, expects, plans, intends, anticipates, believes, estimates, predicts, potential, projected, or continue” and similar expressions identify forward-looking statements. Grayscale assumes no obligation to update any forward-looking statements contained herein and you should not place undue reliance on such statements, which speak only as of the date hereof. Although Grayscale has taken reasonable care to ensure that the information contained herein is accurate, no representation or warranty (including liability towards third parties), expressed or implied, is made by Grayscale as to its accuracy, reliability, or completeness. You should not make any investment decisions based on these estimates and forward-looking statements.
There is no guarantee that the market conditions during the past period will be present in the future. Rather, it is most likely that the future market conditions will differ significantly from those of this past period, which could have a materially adverse impact on future returns. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN. PAST PERFORMANCE IS NOT INDICATIVE OF FUTURE RESULTS. We selected the timeframe for our analysis because we believe it broadly constitutes the most complete historical dataset for the digital assets that we have chosen to analyze.
HYPOTHETICAL SIMULATED PERFORMANCE RESULTS HAVE CERTAIN INHERENT LIMITATIONS. There is no guarantee that the market conditions during the past period will be present in the future. Rather, it is most likely that the future market conditions will differ significantly from those of this past period, which could have a materially adverse impact on future returns. Unlike an actual performance record, simulated results do not represent actual trading or the costs of managing the portfolio. Also, since the trades have not actually been executed, the results may have under or over compensated for the impact, if any, of certain market factors, such as lack of liquidity. Simulated trading programs in general are also subject to the fact that they are designed with the benefit of hindsight.
If actual trading activity was executed at levels that differed significantly from the general market data used in the hypothetical simulated performance, the actual returns achieved would have varied considerably from the results of the hypothetical simulated performances and could have been substantially lower and could result in significant losses.