What makes a high-quality post-earnings momentum trade? To answer that question, I analyzed 59 real post-earnings momentum setups using my own trading database. The results revealed a 66.1% continuation rate, an average Maximum Favorable Excursion (MFE) of 11.99%, and a surprising statistical sweet spot: stocks moving 5% to 10% during the first hourly earnings candle consistently produced the best reward-to-risk ratios. This article breaks down the key findings and explains how they’re shaping my post-earnings momentum strategy going forward.
Most traders judge post-earnings momentum trades by a single question: did the stock keep moving in the same direction?
But that only tells part of the story.
To better understand what actually makes these setups tradable, I analyzed 59 completed post-earnings momentum setups from my own trading database.
I compared bullish and bearish reactions, first-hour move sizes, multi-timeframe breakouts, fundamental alignment, Maximum Favorable Excursion, Maximum Adverse Excursion, and next-day closing performance.
The goal was not to test one narrow theory. It was to identify the broader patterns shaping my recent trading research.
The data below has revealed several important findings, including a 66.1% overall continuation rate, stronger next-day persistence among bearish setups, improved reliability when daily breakouts occurred, and major differences in risk depending on the size of the initial earnings move.
Below, I break down the most useful lessons from the first 59 trades in my post-earnings momentum database.
Note: These results reflect my own trading methodology and dataset. They should not be interpreted as universal market statistics, but rather as observations from a consistent rules-based strategy.
Methodology
The findings in this article are based on a 59-trade post-earnings momentum database that I have been building throughout 2026. Every trade was measured using the same process:
- Entry: The close of the first hourly earnings candle.
- Direction: Long after bullish earnings momentum; short after bearish earnings momentum.
- Time horizon: Performance measured through the end of the following trading day.
- Maximum Favorable Excursion (MFE): The greatest move in the trade’s favor after entry.
- Maximum Adverse Excursion (MAE): The greatest move against the trade after entry.
- Continuation: Whether price continued moving in the direction of the initial hourly earnings reaction.
- Directional close: Whether the stock closed the following trading session in the same direction as the initial earnings move.
- Breakout analysis: Each trade was classified based on whether the hourly earnings candle broke key support or resistance on the hourly, 4-hour, and daily charts.
- Fundamental alignment: Earnings results, guidance, and company commentary were evaluated to determine whether they supported the direction of the technical breakout.
- Trade quality: Each setup was evaluated using the same rules-based post-earnings momentum strategy to ensure consistency across the dataset.
Although 59 trades is still a relatively small sample, every observation was collected using the same methodology, allowing meaningful comparisons between different types of post-earnings momentum setups.
Key Statistics – Overall Results & Findings From 59 Trade Setups
- 59 completed post-earnings momentum setups analyzed from my personal trading database.
- 66.1% of stocks continued moving in the direction of their first hourly earnings candle.
- The average Maximum Favorable Excursion (MFE) was 11.99%, with a median of 11.62%.
- The average Maximum Adverse Excursion (MAE) was 6.12%, while the median MAE was 3.53%.
- 62.7% of all setups produced at least a 9% Maximum Favorable Excursion after the hourly earnings candle closed.
- 61.0% of trades never moved 5% against the position, remaining within my preferred stop-loss range.
- 47.5% of setups generated at least a 9% MFE while keeping MAE below 5%, offering the ideal reward-to-risk profile based on my trading rules.
- The dataset consisted of 38 bullish and 21 bearish post-earnings momentum setups, allowing for meaningful comparisons between long and short trades.
Bull & Bears: Long Setups Vs Short Setups
Across my dataset, bearish trades produced cleaner directional follow-through than bullish trades. Approximately 71.4% of downside setups continued lower, compared with 63.2% of upside setups continuing higher.
The difference became even clearer at the following session’s close: 76.2% of short setups finished in the anticipated direction, versus 60.5% of long setups.
Shorts also averaged a 5.52% next-day directional close, compared with 2.77% for longs.
However, bearish setups did not produce larger average MFEs and experienced slightly greater adverse volatility, suggesting their main advantage may be more persistent follow-through rather than easier risk management.
Still, the idea that downside setups often provide cleaner directional follow-through is supported by parts of the data, although not by every metric.
What does all of this mean?
Ultimately, short setups did not produce meaningfully larger maximum moves. In fact, they provided a slightly smaller average Maximum Favorable Excursion compared to long setups.
On the other hand, where the shorts clearly outperformed was in directional persistence:
- A short setup was 71.4% likely to be classified as continued, versus 63.2% for a long.
- A short setup was 76.2% likely to close the following session lower, versus 60.5% of longs closing higher.
- The average short finished 5.52% farther in the correct direction by EOD, nearly double the 2.77% average for longs.
Average Maximum Favorable vs Adverse Excursion
The average Maximum Favorable Excursion (11.99%) was nearly 2× larger than the average Maximum Adverse Excursion (6.12%), suggesting that qualifying post-earnings momentum setups generally offered favorable reward-to-risk characteristics.
So the best interpretation is not necessarily that shorts move farther intraday. It is that they have so far been more likely to retain their directional move into the next close.
That is a genuinely useful distinction, and something that I plan to implement into the way I look at and think about post-earnings momentum setups in the future.
The Sweet Spot: Stocks Moving 5% to 10% Produced the Best Risk-to-Reward
Perhaps the most important finding in my entire dataset wasn’t related to bullish versus bearish setups or multi-timeframe breakouts.
Instead, it was the importance of the size of the first hourly earnings candle.
After grouping all 59 completed trades by the percentage move during the first hour after earnings, I found that stocks moving between 5% and 10% consistently produced the strongest combination of continuation, manageable pullbacks, and favorable reward-to-risk ratios.
Due to limited sample size, the data for stocks moving less than 5% was too small and unreliable to draw meaningful conclusions. After all, it only contained less than two trade setups.
The 5% to 10% group, however, stood out across almost every metric.
Nearly nine out of ten trades (88.2%) continued moving in the direction of their initial earnings reaction, while an impressive 94.1% still closed the following session in that same direction.
Just as importantly, these trades experienced an average Maximum Adverse Excursion (MAE) of only 2.72%, which lives comfortably within many trader’s 3% to 5% stop-loss range.
Perhaps the most telling statistic was that 70.6% of these setups generated at least a 9% Maximum Favorable Excursion (MFE) without first moving more than 5% against the position.
In other words, they not only worked more often—they were considerably easier to manage from a risk perspective.
By comparison, the largest earnings movers produced much less attractive statistics.
Stocks moving 20% or more during the first hourly earnings candle continued only 50% of the time, averaged just 9.00% MFE, and experienced a much larger 10.66% average MAE.
Only 25% of these trades produced a 9% favorable move while keeping adverse movement below 5%, which reinforces one of the biggest lessons I’ve learned from building this database:
The biggest earnings moves are not necessarily the best trading opportunities.
A stock moving 25% or 30% after earnings may attract the most attention, but it also tends to be much more volatile and far more likely to experience a deep pullback before continuing—or reversing altogether.
Meanwhile, stocks moving 5% to 10% appear to strike an ideal balance.
The move is large enough to confirm that institutions are aggressively repricing the company, but not so extreme that the trade has already become overextended.
Based on my dataset, this range currently represents the statistical sweet spot for post-earnings momentum entries. It provides the best combination of follow-through, manageable risk, and overall reward-to-risk ratio.
Do Multi-Timeframe Breakouts Improve Post-Earnings Momentum?
I also tracked whether each setup broke a key level on the hourly, 4-hour, and daily charts to see if multi-timeframe confirmation improved performance.
- 70.0% continuation rate
- 70.0% next-day directional close rate
The strongest directional results came from daily breakouts.
These setups continued 70.0% of the time and closed the next day in the earnings direction 70.0% of the time, compared to 62.8% and 65.1% for hourly breakouts.
Interestingly, daily confirmation did not produce larger moves. Average MFE was 10.80%, versus 11.56% for hourly breakouts and 11.94% for 4-hour breakouts.
The 4-hour breakout category produced the lowest average MAE (5.33%), suggesting it may help reduce downside risk, although the difference from hourly (5.92%) and daily (5.83%) breakouts was modest.
Overall, these results suggest that daily breakouts improve directional reliability, while 4-hour breakouts may slightly improve risk management.
However, I wouldn’t use breakout confirmation by itself.
In my experience, it works best alongside earnings move size, candle structure, fundamentals, and volume rather than as a standalone filter.
Do Fundamentals and Technicals Need to Agree?
One of the more surprising findings in my dataset was that setups where the fundamentals and technicals did not agree actually produced slightly better statistics.
At first glance, this data appears to suggest that fundamental alignment isn’t important.
But I don’t think that’s the correct conclusion.
The “No” category contains only 14 trades, including several technically-driven momentum moves such as WIX, ZS, INTU, DOMO, and ACN.
Meanwhile, the “Yes” group contains 38 trades, including several extremely extended earnings reactions that later reversed, such as HPE, MDB, CNXC, QUBT, and NAVN.
In other words, fundamental alignment didn’t cause weaker performance.
Rather, many of the strongest fundamental setups also experienced the largest initial earnings moves, making them more vulnerable to profit-taking and pullbacks.
My takeaway is that fundamental alignment helps establish a logical trade thesis, but it does not eliminate the risk of chasing an overextended move.
As my earlier analysis showed, the size of the first hourly earnings candle appears to have a much larger impact on risk-to-reward than whether the earnings report and technicals perfectly agree.
Key Takeaways From My Post-Earnings Momentum Research
After analyzing 59 post-earnings momentum setups, several patterns have become increasingly difficult to ignore.
While the dataset is still growing, these are the conclusions that currently have the strongest statistical support.
1. Stocks moving 5% to 10% after earnings produced the best overall setups.
This group generated the highest continuation rates, the smallest average pullbacks, and the strongest overall reward-to-risk profile. Nearly 71% produced at least a 9% MFE while keeping MAE below 5%, making them the easiest trades to manage under my current risk rules.
2. Bigger earnings moves were not better.
Contrary to what many traders assume, stocks moving 20% or more during the first hourly earnings candle produced the weakest statistics. They continued only 50% of the time, experienced the largest average MAE (10.66%), and offered the poorest reward-to-risk characteristics in the dataset.
3. Bearish earnings momentum deserves more attention.
Short setups produced slightly smaller average MFEs than longs, but they demonstrated stronger directional persistence, with 71.4% continuing in the earnings direction and 76.2% still closing the following day in that direction. The data increasingly suggests that downside post-earnings momentum often produces cleaner follow-through.
4. Daily breakout confirmation improved directional reliability.
Trades that simultaneously broke key daily support or resistance levels continued in the earnings direction 70% of the time. While this didn’t increase average MFE, it did improve the probability that the original trading thesis remained intact.
5. Entry quality may matter just as much as setup quality.
Case studies such as PENG and BYRN demonstrate that even excellent post-earnings momentum setups can produce poor initial entries if price has already become extended. Waiting for an EMA pullback often improves the average entry price, reduces MAE, and makes it easier to achieve a favorable reward-to-risk ratio.
Conclusion – My Post-Earnings Momentum Hypothesis
While my post-earnings momentum database is still growing, the first 59 trades have already revealed several repeatable patterns.
The data suggests that moderate first-hour earnings moves (5% to 10%), multi-timeframe technical confirmation, and disciplined entries consistently produce the best reward-to-risk opportunities.
Just as importantly, the research reinforces that successful trading isn’t about chasing the biggest moves—it’s about finding the setups where the probabilities are most in your favor.
As I continue expanding this dataset, I’ll keep refining these findings and sharing new insights as they emerge.
If you want to go deeper:
- Explore the Trading Statistics Hub to understand how different sectors behave across market cycles
- Study real setups inside the Trade Reviews section
- Learn the framework behind high-probability setups in the Post-Earnings Momentum Strategy
This is how you turn raw market data into repeatable trading edge.
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