End-of-month effect in Bitcoin
Harnessing predictable flow-effects
I have previously written about the end-of-month effect as it appears in a traditional financial market (SPY and TLT). In those cases, I observed systematic return patterns around month-end. Motivated by these findings, I extended the analysis to other asset classes to determine whether similar patterns could be detected elsewhere. In several cases, comparable effects were indeed present. In this post, I focus on my findings for a Bitcoin ETP.
What Is the End-of-Month Effect?
Before presenting the results, it is useful to briefly recap what is meant by the end-of-month effect. Many large institutional investors, such as wealth managers, pension funds, insurance companies and robo-advisors, operate under predefined portfolio allocation rules. These rules typically require portfolios to be periodically rebalanced to maintain target weights across asset classes.
The precise target allocation is often less important than the process itself. When assets within a portfolio diverge in performance, rebalancing forces investors to systematically sell assets that have performed well and buy those that have lagged, regardless of whether prices are attractive or unattractive. Crucially, this behavior is mechanical and rule-based, rather than driven by forecasts, valuations or new information.
Because this rebalancing is mandatory, it generates what can be described as price-insensitive trading. Large volumes of capital are moved simply to comply with allocation rules, not because investors believe prices should rise or fall. When many institutions rebalance simultaneously, most commonly at or near month-end, their collective actions can temporarily distort market prices. Investors who are able to anticipate these recurring flows may be able to identify short-term return patterns around the turn of the month.
The Hypothesis
Building on this framework, I formulated the following hypothesis:
For many institutional investors, direct ownership of Bitcoin or other cryptocurrencies is not permitted due to regulatory or internal constraints. As a result, ETPs are often the only viable vehicle through which these investors can gain exposure to Bitcoin. Compared to traditional equity or bond ETFs, Bitcoin ETPs are relatively small in size.
Because of this smaller market size, the trades of large institutional participants may have a disproportionately large impact on prices. If these investors rebalance their portfolios at month-end in a manner similar to traditional assets, the resulting flows could meaningfully influence short-term returns. Specifically, the hypothesis is that performance tends to be weaker toward the end of the month and stronger in the first few trading days of the new month.
Testing the Theory
To test this idea, I analyzed the Bitwise Physical Bitcoin ETP (BTCE – Yahoo Finance ticker: BTCE.DE), which is tradable in Germany. While the analysis is based on this specific product, the underlying mechanism should apply to other Bitcoin ETPs as well.
As an initial exploratory step, I grouped average returns by trading interval around the turn of the month. This provides a first indication of whether a systematic pattern may exist. Figure 1 presents the average returns across these intervals and offers a preliminary view of how performance differs between the end and the beginning of the month.
The chart clearly contradicts my initial hypothesis that BTCEs performance would be stronger during the first trading days of the month than at the end. Instead, the data show the opposite pattern. On average, the last trading days of the month are the strongest-performing period, while returns in the first days of the new month are positive but more modest in comparison. In contrast, the middle portion of the month tends to generate negative returns.
This finding suggests that, at least for BTCE, institutional rebalancing flows may not operate in the same way as observed in traditional equity and bond markets (if there are any). Rather than seeing buying pressure concentrated at the beginning of the month, price strength appears to build toward month-end.
It is also important to note the limitations of the dataset. Although BTCE was listed in 2020, I restricted the analysis to the period from 2021 to 2024 in order to focus on a more mature trading phase and avoid potential early listing effects. The drawback of this choice is an even shorter observation window, which reduces the robustness of the conclusions.
To further evaluate the effect, I compared the performance of the last five trading days of each month with that of the first fifteen trading days of the following month. The results of this comparison are presented in Figure 2.
Despite the relatively limited sample size, the data reveal a relationship between returns in the last five trading days of a month and those in the first fifteen trading days of the month. Rather than showing a reversal pattern, the results point to trend continuation. When the first fifteen trading days of a month generate positive returns, the following last five trading days also tend to be positive on average.
So, the next step is figuring out how to turn this into a trading strategy.
Trading Setup
Based on the observed return patterns, three distinct trading strategies are defined in order to compare their behavior and performance:
Buy & Hold
This strategy represents the baseline scenario. The portfolio is invested in the asset 100% of the time, regardless of market conditions or calendar effects. Buy & Hold serves as a benchmark against which the more active strategies can be evaluated.End-of-Month (EOM)
Under this approach, the portfolio is invested only during the last five trading days of each month and remains in cash for the rest of the time. This strategy is designed to test whether the end-of-month effect from figure 1 can be profitably captured.End-of-Month Trend Continuation (EOM Trend)
This strategy adds a simple trend filter to the EOM approach. The strategy is invested in the last five trading days of the month only if the return over the first fifteen trading days of that same month was positive. If performance during the first fifteen days was negative, no position is taken at month-end. The goal of this strategy is to participate only when it aligns with short-term momentum/trend.
Together, these three setups allow for a structured comparison between continuous exposure, calendar-based timing and calendar-based timing combined with a trend filter.
The Results
The next table presents the results of the different strategies for the observation period.
The Buy & Hold strategy delivers the highest CAGR at 34.58%, which is a direct consequence of maintaining full and continuous exposure to BTCE throughout the entire period. This approach fully participates in Bitcoin’s long-term upward trend but also absorbs every major drawdown and volatility spike along the way.
The End-of-Month (EOM) strategy achieves a slightly lower CAGR of 32.26%, which is a notable result given that the strategy is invested only during the final five trading days of each month. This indicates that a substantial portion of BTCE’s total return is concentrated in this narrow calendar window. In other words, the EOM strategy captures most of the upside while being exposed to the market for only a fraction of the time.
The EOM Trend strategy produces a materially lower CAGR of 20.35%, which is expected because it is the most selective of the three approaches. By applying an additional trend filter, the strategy deliberately avoids market exposure during weaker months, resulting in fewer invested periods and, consequently, lower absolute returns.
Looking at risk, Buy & Hold exhibits extremely high annualized volatility of 63.11%, underscoring the severe price swings and deep drawdowns associated with uninterrupted Bitcoin exposure. The EOM strategy reduces volatility by more than half to 29.88%, demonstrating the powerful risk-reduction effect of concentrating exposure in a specific, historically favorable time window. The EOM Trend strategy lowers volatility even further to just 9.89%, resulting in a remarkably smooth return profile relative to Bitcoin’s typical behavior.
This steady decline in volatility clearly shows that introducing timing rules and trend filters can dramatically reduce overall portfolio risk. However, it is crucial to interpret this reduction correctly. The lower annualized volatility is largely a consequence of extended periods spent in cash. During the periods when the strategy is actually invested, price behavior remains nearly as volatile as under Buy & Hold (roughly 55% annualized volatility).
For this reason, position sizing becomes critical. These strategies should not be sized as if they were traditional low-volatility strategies. The exposure during invested periods must be adjusted to reflect the fact that intraday and short-term volatility remains very high.
Turning to risk-adjusted performance, Buy & Hold produces a Sharpe ratio of 0.55, which is modest given the extreme volatility involved. The EOM strategy improves the Sharpe ratio to 1.08, indicating that focusing exposure around month-end substantially enhances return per unit of risk. The EOM Trend strategy achieves a Sharpe ratio of 2.06, an exceptionally strong result that reflects highly efficient risk-adjusted performance over the full sample.
Despite its lower absolute returns, EOM Trend clearly dominates on a risk-adjusted basis. That said, these strategies should not be interpreted as conventional “Sharpe 1 or 2” strategies in the traditional sense. Their attractive Sharpe ratios are partly driven by the smoothing effect of long cash periods. The volatility experienced during actual market exposure remains fully “crypto-like,” and any real-world implementation must account for this reality through conservative sizing and robust risk management.
Key Takeaways
BTCE’s Returns Are Not Evenly Distributed Over Time
A significant portion of the returns occur during the last five trading days
Full-Time Exposure Maximizes Returns, Not Efficiency
Buy & Hold delivers the highest absolute returns, but it does so at the cost of extreme volatility
Selective Exposure Can Capture Most of the Upside
Much of BTCE’s upside may be concentrated in predictable periods
For those seeking better capital efficiency, smoother returns, or reduced drawdowns, calendar-based exposure offers a compelling alternative, provided positions are sized with full awareness of Bitcoin’s inherent volatility.
Conclusion
This analysis set out to investigate whether the end-of-month effect observed in traditional financial markets also exists in Bitcoin ETPs and whether it can be translated into a practical trading approach. While the original hypothesis expected stronger performance at the beginning of the month, the empirical results for BTCE clearly point in a different direction. Returns are not evenly distributed across the month; instead, price strength tends to build toward month-end, with the final trading days accounting for a disproportionately large share of total returns.
By translating these observations into systematic trading strategies, the analysis shows that calendar-based exposure can dramatically alter the risk–return profile. The End-of-Month strategy captures nearly the same long-term returns as Buy & Hold while spending far less time in the market, highlighting a striking inefficiency in constant exposure. Adding a simple trend filter further improves risk-adjusted performance, resulting in substantially lower drawdowns and a much smoother equity curve, albeit at the cost of lower absolute returns.
However, these findings must be interpreted with care. The dataset is limited in length and structural dynamics in crypto markets continue to evolve. Moreover, the attractive volatility and Sharpe statistics of the EOM-based strategies are largely driven by extended periods in cash. During active investment phases, volatility remains fully “crypto-like,” making prudent position sizing and robust risk management essential.
Overall, this analysis provides evidence that Bitcoin ETP returns are influenced by predictable calendar effects and short-term momentum dynamics. For investors and traders willing to move beyond continuous exposure, rule-based, calendar-driven strategies offer a compelling framework for improving capital efficiency and managing risk. While further validation across instruments and longer time horizons is warranted, the end-of-month effect in Bitcoin ETPs appears to be both real and exploitable when applied thoughtfully.
Based on these findings, I intend to harness the observed end-of-month effect, most likely through the EOM Trend strategy, as it offers the most attractive balance between stability and risk-adjusted performance. However, despite its high Sharpe ratio, I would not size this strategy in the same way I would a “conventional Sharpe 2” strategy in traditional asset classes.
I will explore practical considerations in more detail in a follow-up post in January.
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Disclaimer
The above article constitutes my or the authors’ personal views and is for entertainment purposes only. It is not to be construed as financial advice in any shape or form. Please do your own research and seek your own advice from a qualified financial advisor. I / The authors may from time to time hold positions in the aforementioned securities consistent with the views and opinions expressed in this article. The information provided in this article is not making promises, or guarantees regarding the accuracy of information supplied, nor that you guarantee for the completeness of the information here. The information in this article is opinion-based and that these opinions do not reflect the ideas, ideologies, or points of view of any organization the authors may be potentially affiliated with. The authors reserve the right to change the content of this blog or the above article. The performance represented is historical and that past performance is not a reliable indicator of future results and investors may not recover the full amount invested.