The Hidden Calendar Pattern in Bonds
The EOM-Effect
In my first post on the End-of-Month (EOM) Effect, I explored the phenomenon using a simple equity–bond reversal strategy built with SPY and TLT. The idea behind that analysis was to illustrate how market flows around the turn of the month, e.g. driven by institutional portfolio rebalancing, can create (short-term) recurring patterns in asset prices. By positioning in one asset while avoiding the other during these periods, the strategy aimed to capture those predictable shifts.
However, an interesting insight emerged from the original analysis: the EOM pattern was not only visible in the relationship between equities and bonds. In fact, the data already showed that TLT on its own exhibits a strong end-of-month tendency. This means that the seasonal behavior could potentially be exploited without relying on a cross-asset reversal between stocks and bonds.
In the original post, I examined the distribution of returns across individual trading days of the month for the period from 2003 to 2022. The goal of this analysis was to see whether returns tend to cluster around specific parts of the month, which is the key idea behind the EOM-Effect. The results revealed a clear pattern. The first few trading days of the month tended to produce slightly negative returns. In contrast, the middle portion of the month generated modestly positive returns. The most striking feature, however, appeared at the end of the month. The final trading days showed strong positive returns, highlighting a pronounced end-of-month effect during this long sample period.
Figure 1 presents the same type of analysis but for a more recent and shorter time window, from 2016 to 2025.
Comparing the two periods reveals that the structure of returns has changed in a meaningful way. While the positive performance at the very end of the month and the negative performance at the start of the month still persists, the middle of the monthly cycle looks quite different in the newer sample. The middle of the month, previously characterized by moderately positive returns, now shows distinctly negative performance. It has become the weakest part of the month in the updated dataset.
As a result, the return profile has become more concentrated. Instead of returns being distributed across the middle and the end of the month, the positive performance is now concentrated in the final trading days, while the rest of the month contributes negatively to overall returns on average.
So, the next step is figuring out how to turn this into a trading strategy.
Trading Setup
This is the setup for the long-only strategy. The execution of the strategy is refreshingly simple.
Long-only:
Go long TLT around the 15th trading day
Hold until month-end, then close the position.
Long-short:
1. At the start of the month go short TLT until the 15th trading day
2. Reverse the position ang go long TLT until the end of the month.
Precise timing isn’t necessary, nor is it possible. These flow effects occur unevenly over several days.
The Results
The next figure presents the annual returns of the long-only version.
Over the sample period, the strategy achieved a compound annual growth rate (CAGR) of 6.04%. Because the strategy is active for only five trading days each month its overall exposure is reduced, resulting in low volatility of 5.04%.
This contributes to a really good expected Sharpe ratio of 1.20, indicating an attractive risk-adjusted return profile. Keep in mind, this results are before costs.
Let’s look at the long-short version.
The next figure presents the annual returns of the long-short version.
Over the sample period, the strategy achieved a CAGR of 13.05%. The volatility is elevated to 14.92%, because this version is always invested.
This contributes to a solid expected Sharpe ratio of 0.87, indicating an attractive risk-adjusted return profile. Keep in mind, this results are before costs.
Both versions of the strategy appear sound when evaluated before costs. However, transaction costs are a highly individual factor and can vary significantly from one trader to another. Differences in brokerage fees, bid–ask spreads and borrowing costs for short positions all influence the total cost of implementing a strategy. Because of this variability, it is difficult to provide a universal answer to the question of whether a strategy will remain profitable after costs are considered. What works well for one trader may look very different for another.
For these reasons, it is essential that each trader runs their own cost assumptions and evaluates how the strategy performs under conditions that realistically reflect their personal setup. Estimating expected commissions, spreads and financing costs for short positions can provide a far more accurate picture of what the strategy might deliver in practice.
To illustrate this, the next figure presents the annual returns of the long-short version including the estimated costs for my trading setup.
After incorporating these cost estimates, the strategy’s performance naturally declines. Over the full sample period, the CAGR falls to 10.74%, which corresponds to a Sharpe ratio of 0.72. In relative terms, this represents a reduction in risk-adjusted performance of more than 17%.
Even after accounting for these costs, the strategy still produces double-digit annualized returns, which suggests that the underlying signal retains meaningful economic value. In other words, while costs reduce the edge, they do not eliminate it.
Another aspect worth highlighting is the potential diversification benefit. This strategy should be relatively uncorrelated to other stuff, e.g. equities.
Key Takeaways
The EOM Effect exists in bonds independently. TLT exhibits a strong end-of-month tendency on its own, meaning the seasonal pattern can be exploited without relying on a cross-asset reversal between stocks and bonds.
The return structure evolves over time.
Diversification potential adds another layer of appeal. Because the strategy is driven by month-end flow effects in bonds, it should be largely uncorrelated to equities, making it a potentially valuable addition to a broader portfolio.
Conclusion
The end-of-month effect in bonds is a real and exploitable phenomenon. It’s interesting how the return structure has actually changed in the more recent sample. The positive performance has become more concentrated at the end of the month, while the rest of the monthly cycle has deteriorated.
Both the long-only and long-short versions of the strategy present attractive risk-adjusted profiles before costs.
As always, no strategy is a free lunch. But the core message here is straightforward: the end of month bond effect is a durable, low-complexity pattern that deserves a place in any systematic trader’s toolkit, particularly given its potential to diversify away from other strategies, e.g. equities.
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.