The Turn of Month March → April in the DAX: 75% Win Rate
Few seasonal effects are as well documented as the turn-of-month effect — the observation that a large portion of monthly returns occur in the few days around the month-end transition. For the DAX, the transition from March to April shows particularly strong numbers.
We have statistically analyzed the last 20 years — and the effect is not only visible, it is statistically significant.
What Is the Turn-of-Month Effect?
The turn-of-month effect (TOM) describes a recurring pattern: returns in the last trading days of a month and the first trading days of the following month are systematically higher than in the rest of the month.
Reasons for this include:
- Salary and savings plan flows: At the beginning of the month, savings plans, occupational pension schemes, and ETF purchases flow into the market.
- Window dressing: Fund managers adjust their portfolios at quarter- and month-end.
- Psychology: The month transition creates a natural reset — new budgets, new targets.
The Numbers: DAX March → April (20 Years)
For our analysis we look at a window of t-3 to t+3 — the last 3 trading days in March and the first 3 trading days in April.
The results from SeasonAlpha speak for themselves:
| Metric | Value |
|---|---|
| Win Rate | 75.0% |
| Avg Return | +1.367% |
| Median | +1.820% |
| Max Gain | +3.98% |
| Max Loss | -3.20% |
| Standard Deviation | 2.261% |
| Sample | 20 years (15 winners / 5 losers) |
In 15 out of 20 years the turn of month March → April was positive. The median even exceeds the average — this indicates that the effect is not driven by individual outliers.
Is This Statistically Significant?
Yes. The significance test delivers a relevance score of 0.83 — a clearly green signal.
- t-statistic: 2.63 — the return deviates clearly from zero.
- p-value: 0.0163 — the probability that this result is pure chance is less than 2%.
- Cohen's d: The effect is medium-sized — not statistical noise, but a demonstrable pattern.
For context: a p-value below 0.05 is considered significant in statistics. With p = 0.0163, the DAX turn of month March → April is well below that threshold.
Why Specifically March → April?
The turn of month March → April has a special feature: it coincides with the end of Q1 → Q2. This amplifies the usual TOM effects:
- Quarter-end rebalancing: Institutional investors restructure portfolios.
- New quarterly budgets: Fresh capital flows into the market.
- Dividend season: Many European companies pay out in April — the expectation drives prices upward beforehand.
What Does This Mean for Traders?
The data shows a statistically robust pattern. Nevertheless: seasonality is a probability, not a guarantee. In 5 out of 20 years the turn of month was negative — sometimes with losses up to -3.20%.
Anyone wishing to use seasonal patterns can apply them as an additional filter:
- Is your setup already bullish? A positive TOM effect confirms the direction.
- Are you planning an entry? The days t-3 to t+3 historically offer a favorable time window.
- Use the significance test on SeasonAlpha to check which month transitions are truly robust — and which are just noise.
How to Find the Analysis on SeasonAlpha
- Open seasonalpha.ai
- Select Turn of Month in the sidebar
- Set the ticker to DAX (^GDAXI) and the time period to 20 years
- In the month transition selector, choose only March → April
- Scroll to the Statistical Significance section — there you will find the significance gauge
You can also compare other month transitions and experiment with window optimization.
Conclusion
The turn of month March → April is one of the strongest and most statistically significant TOM effects in the DAX. A 75% win rate, +1.37% average return, and a p-value of 0.016 speak a clear language.
Use this knowledge as a building block — not as the sole basis. Combine seasonal patterns with technical indicators and your own risk management. Try it yourself at seasonalpha.ai.
Frequently Asked Questions
What is the turn-of-month effect?
The turn-of-month effect describes the pattern that stocks achieve above-average returns around the monthly transition. Typically one looks at the last 2–3 and the first 2–3 trading days. The effect has been documented in financial research for decades.
Does the TOM effect work for every month transition?
Not all month transitions are equally strong. On SeasonAlpha you can analyze each of the 12 transitions individually and test them with a significance test. March → April is one of the strongest — others such as August → September are considerably weaker.
Can you trade the turn-of-month effect?
The statistics show a robust pattern, but no guarantee. Transaction costs, slippage, and extraordinary market events can overlay the effect in individual years. Seasonal patterns are best used as a supplement to existing strategies.
Why is the significance test important?
Without a significance test you do not know whether a pattern is genuine or pure chance. The p-value measures the probability that the result arose by chance. With p = 0.016, that probability is less than 2% — that is a strong signal.