In my current post-earnings momentum dataset, 40.1% of setups experienced at least 5% MAE, 23.8% reached at least 10% MAE, and the three ugliest eventual winners all went more than 14% against the trade before finishing green. In this article, I break down those three setups, explain what maximum adverse excursion can reveal about stop-loss placement, and show why a stop that feels “safe” can sometimes sit directly inside the normal volatility of a winning trade.
Over the past several months, I have been building a database of post-earnings momentum setups to better understand what actually happens after a stock makes a significant move following an earnings report.
Post-earnings momentum trading is built around a relatively simple idea: when earnings cause a stock to make a significant initial move, that momentum can sometimes continue into the following trading session.
My strategy attempts to capture that continuation by entering in the direction of the initial earnings move after the first hourly candle closes. But identifying a setup is only half the problem.
I have also been experimenting extensively with position sizing, profit targets, stop losses, maximum adverse excursion (MAE), maximum favorable excursion (MFE), and passive end-of-day exits to determine how these trades behave after entry.
My current database contains 166 tracked post-earnings setups, with 147 currently containing complete next-day MAE and end-of-day outcome data.
One of the most important things this research has taught me is that a winning trade does not necessarily look like a winning trade the entire time you are holding it.
And that’s exactly why we’re here to study 3 of my ugliest winning trades!
All three eventual winners suffered more than 14% MAE — but remained inside a -15% catastrophic stop.
Quick Answer: What’s the Best Place to Put a Stop Loss?
There is no universal “best” stop loss—it should sit outside the normal volatility of the strategy you trade and represent a point where the setup has actually failed. In my post-earnings momentum dataset, average MAE was 5.60%, while 40.1% of setups experienced at least 5% MAE and 23.8% reached at least 10% MAE, making tight stops especially vulnerable to normal post-earnings volatility. For my specific strategy, I have settled on a -15% catastrophic stop paired with fixed $1,000 positions because it was the tightest stop in my testing that preserved every trade that eventually finished green. That does not make -15% appropriate for every trader—the stop must fit the strategy, and the position size must fit the stop.
Key Statistics – Ugliest Post-Earnings Momentum Setups 2026
- 166 total post-earnings setups tracked
- 147 setups with complete next-day MAE and EOD data
- 91 of 147 setups (61.9%) finished green in the direction of the original trade
- Average MAE magnitude: 5.60%
- Median MAE magnitude: 3.74%
- Maximum recorded MAE: 23.28%
- 40.1% of setups experienced at least 5% MAE
- 23.8% experienced at least 10% MAE
- 8.2% experienced at least 15% MAE
- Among eventual winners, average MAE was only 2.87%
- Among eventual winners, median MAE was just 1.77%
- 18 eventual winners experienced at least 5% MAE
- 5 eventual winners experienced at least 10% MAE
- 0 eventual winners experienced 15% or greater MAE
- The three ugliest winners recorded MAEs of -14.36%, -14.28%, and -14.07%
My Top 3 Ugliest Winners
When I say an “ugly winner,” I mean a trade that ultimately finished green but produced an unusually large adverse move, which I track as the maximum adverse excursion or MAE, before getting there.
These are not the trades traders normally want to screenshot.
It is much more satisfying to study an A+ setup that breaks out immediately, never goes red, hits a profit target, and makes the strategy look beautifully predictable.
But those trades only show us what happens when everything works perfectly, which often, is NOT what the day in, day out reality of post-earnings momentum trading looks like.
Ugly winners can teach us something arguably more important: how bad a trade can look without actually being dead.
If I am going to use a systematic stop loss, I need to understand not only how far my losing trades travel against me, but also how much adverse movement my winning trades sometimes require.
These three trades represent the extreme end of that spectrum.
U — MAE: -14.36%
Unity Software was the ugliest eventual winner in my entire current dataset.
The stock initially moved +13.96% following earnings, creating a bullish post-earnings momentum setup. The first hourly candle broke out on both the hourly and four-hour charts, while the fundamental data and price reaction were aligned.
Then the trade became an absolute mess… From the close of the first hourly earnings candle, U eventually produced an MAE of -14.36%. On a $1,000 position, that amounts to a -$143.60 loss.
In other words, a trader entering long at my standard entry point would have watched the position move almost 15% against them.
On the other hand, its maximum favorable excursion (MFE) during the session was only +2.40%, and yet it eventually finished the next day just +0.10% above my entry.
Calling this a winner almost feels generous. But technically, it was.
And that technicality matters when studying stop-loss placement.
A -5% stop would have taken the loss. A -7% stop would have taken the loss. A -10% stop would have taken the loss. Even a -12% stop would have taken the loss.
And yet, a -15% catastrophic stop would have allowed the trade to survive.
U is therefore the most extreme example in the dataset of a trade that looked completely broken before technically managing to finish green.
ODD — MAE: -14.28%
ODD produced one of the most extreme initial earnings reactions in the dataset.
The stock fell -27.58% during its first hourly earnings candle, breaking support across the hourly, four-hour, and daily timeframes. The initial setup therefore pointed toward continued downside momentum.
The problem was what happened next.
After entering short, the trade moved as much as 14.28% against the position, which is an enormous adverse excursion by any means.
Despite that reversal, just like U, ODD eventually rolled back over into the green.
Its maximum favorable excursion reached +6.84%, and the stock ultimately finished another 3.56% lower than the hourly entry price, producing a positive result for the short trade.
This is actually in line with one of my other findings from a short vs long setup study, where I found that despite often ending with larger grains, short setups often produced larger adverse movement compared to long setups, before moving in the direction of the earnings reaction.
Again, virtually every conventional tight stop would have converted this eventual winner into a realized loss.
A -5% stop would have failed. A -10% stop would have failed. Even a wide -12% stop would have failed. Yet a -15% catastrophic stop would have survived by less than one percentage point.
ODD also demonstrates why large percentage moves following earnings can become so violent. The stock had already fallen almost 28% before my theoretical entry even occurred.
Expecting price to continue moving in a perfectly straight line after a move that large isn’t impossible, but it’s often approaching exhaustion levels.
BYRN — MAE: -14.07%
Byrna Technologies (BYRN) may be the most important example of the three because this was not merely a trade that survived long enough to finish barely green. It survived an enormous adverse move and then became a legitimately strong winner.
BYRN initially fell -26.78% following earnings, breaking down across the hourly, four-hour, and daily timeframes.
After my theoretical short entry, however, the stock reversed sharply and MAE eventually reached -14.07%. At that point, traditional tight stops would have declared the trade dead.
But the original downside momentum returned.
BYRN went on to produce a +14.75% maximum favorable excursion and ultimately finished 12.85% in the green for the short position by the next-day close.
Think about that path for a second.
The trade went approximately 14% against the position… and still ultimately produced almost a 13% gain by the end of the day.
That is precisely the type of trade that makes discretionary risk management so psychologically difficult.
If I were judging the setup entirely by how the position made me feel while I was holding it, there would have been every reason to panic. While holding a -14% unrealized loss, almost any trader or investor would have felt sick to their stomachs.
But under a predefined -15% catastrophic-stop system, nothing would have required me to do anything. The trade had not yet violated the rule and would have been allowed the room to breathe.
It simply looked terrible, felt terrible, but still turned into a 13% by EOD.
Additional Findings From This Study – Ugly Post-Earnings Momentum Winners 2026
The first thing that immediately jumps out is just how closely clustered these three trades were:
- U: -14.36% MAE
- ODD: -14.28% MAE
- BYRN: -14.07% MAE
All three crossed -14%, but none crossed -15%.
That does not magically prove that -15% will always be the perfect stop. A larger dataset could eventually produce a winning trade with -16%, -18%, or even greater MAE.
But within the current dataset, -15% represents a remarkably clean dividing line.
Of the 91 trades that eventually finished green, 18 would have triggered a -5% stop. Five eventual winners would still have been stopped out using a -10% threshold.
At -15%, that number fell to zero.
That is an enormous difference.
There is another lesson here that is equally important: a wider stop only makes sense when position sizing gets smaller.
Giving a $50,000 position 15% of room would theoretically expose $7,500 of capital before accounting for slippage. For most retail traders, that type of unrealized loss is psychologically destructive.
But giving my standard $1,000 position the same amount of room creates only approximately $150 of predefined nominal risk. That’s not a small amount of money to risk.
But in comparison, unless you’re a millionaire, it’s a lot easier to handle a -$150 loss compared to a $7,500 loss.
The takeaway here is that a stop percentage cannot be evaluated independently from the amount of money being risked.
Finally, studying these trades has changed the way I think about the phrase “the trade isn’t working.” After all, a position being red does not automatically mean a setup has failed. A position being deeply red does not automatically mean a setup has failed.
The more useful question is whether the price movement has exceeded the amount of normal adverse volatility demonstrated by the strategy’s historical data.
That is exactly why tracking MAE, testing different variables, and studying the ugliest winners in your journal, matters.
Conclusion – My Ugliest Winning Trades
Tight stop losses can protect traders from large losses, but they can also repeatedly remove a trader from positions that eventually work.
In my current post-earnings momentum dataset, the three ugliest eventual winners endured -14.36%, -14.28%, and -14.07% MAEs before ultimately finishing the day in the green.
That does not make a 15% stop appropriate for every trader. It makes it appropriate for my current strategy, position sizing, historical volatility, and risk tolerance.
Whether you’re a new trader or an experienced investors, the goal is never just to find the tightest stop possible.
It is to find the stop that separates normal volatility from actual failure.
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.
More Trading Statistics…
Frequently Asked Questions About Stop Losses
What is the 7% rule for stop-loss?
The 7% rule generally refers to a risk-management principle popularized by Investor’s Business Daily and William O’Neil’s growth-investing methodology. Under that approach, an investor typically sells a stock once it falls approximately 7% to 8% below the purchase price rather than allowing a relatively small loss to become significantly larger.
That rule was developed for a specific style of growth-stock investing and should not automatically be applied to every trading strategy. A post-earnings momentum trade experiencing significantly greater short-term volatility may require completely different parameters.
What is the golden rule for stop-loss?
There is no universally accepted “golden” stop-loss percentage.
Investor’s Business Daily has referred to cutting a loss around 7% as a golden selling rule within its own growth-investing methodology. More broadly, however, a better universal principle is to define the maximum acceptable risk before entering a trade and size the position accordingly.
A stop should ideally represent a level where the trade thesis has meaningfully failed—not simply an arbitrary percentage selected because it sounds safe.
Why don’t stop losses work?
Stop losses do work, but they do not guarantee that a trader will exit at the exact stop price.
A conventional stop order generally becomes a market order once its trigger price is reached. During a fast-moving market, a gap, poor liquidity, or extreme volatility can therefore cause the actual fill to occur at a substantially worse price.
FINRA specifically warns that a stop price is not a guaranteed execution price and that short-lived price movements can trigger stops immediately before a stock rebounds.
There is also a strategic reason stop losses can appear not to work: the stop may simply be too tight for the volatility of the setup.
That is exactly what my MAE research is attempting to measure.
Do stop losses work outside RTH?
Not necessarily.
Regular trading hours for U.S. stocks are generally 9:30 a.m. to 4:00 p.m. Eastern Time, while pre-market and after-hours trading operate under different order rules.
Whether a stop order can trigger outside regular trading hours depends on the broker and trading platform. For example, Schwab states that traditional stop orders are not eligible for execution during its extended-hours sessions, while Fidelity says its extended-hours sessions accept limit orders rather than ordinary stop orders.
This is especially important for earnings traders because earnings announcements frequently occur when the regular market is closed.
Always verify exactly how your broker handles stops, stop-limit orders, overnight sessions, and extended-hours orders before relying on a stop for protection outside RTH.
Can a stop-loss guarantee my maximum loss?
No.
A stop determines when an order is triggered, but it does not necessarily guarantee the eventual execution price.
If a stock gaps through your stop or begins moving extremely quickly, a stop-market order can execute significantly beyond the intended price. FINRA notes that once a stop becomes a market order, the eventual execution price can differ substantially from the stop price during volatile conditions.
This is particularly relevant to earnings trading, where overnight gaps and sharp price movements are common.
What is the difference between a stop-loss and a stop-limit order?
A standard stop order typically converts into a market order once the stop price is reached.
A stop-limit order instead converts into a limit order. This gives the trader more control over the minimum or maximum acceptable execution price, but it introduces another risk: the order may never execute if price moves through the limit too quickly.
In other words, a traditional stop prioritizes getting out, while a stop-limit prioritizes price control.
Neither completely eliminates risk.
Is a 15% stop loss too wide?
For many trading strategies, absolutely.
A 15% stop on an oversized position can produce a devastating loss.
But stop distance cannot be separated from position sizing and historical volatility. In my own system, I use approximately $1,000 per setup, meaning a theoretical 15% catastrophic stop represents about $150 of nominal position risk before slippage.
The reason I am willing to give a trade that much room is because my current post-earnings dataset shows that a meaningful percentage of these setups experience large adverse excursions—and, so far, no eventual EOD winner has exceeded -15% MAE.
That conclusion applies to this specific dataset and trading system. It should not be interpreted as a recommendation that other traders use a 15% stop.
What is maximum adverse excursion (MAE)?
Maximum adverse excursion measures the furthest a trade moves against the position after entry.
For example, if a long trade is entered at $100, falls to $92, and later rallies to $115, its MAE was approximately -8%, even though the trade ultimately became profitable.
MAE is useful because it shows how much temporary pain winning and losing trades typically experience.
Studying MAE across a large sample can help traders determine whether their stop loss is protecting them from genuine failures or simply getting triggered by normal volatility.
Should a stop loss be based on a percentage or a technical level?
Either can work, but the important thing is that the method matches the trading system.
Some traders use fixed percentages. Others use support and resistance, moving averages, volatility measures such as ATR, previous highs or lows, or another technical level that would invalidate the setup.
The most important step comes afterward: once the stop distance has been determined, position size should be adjusted so that hitting the stop produces an acceptable dollar loss.
A wider logical stop should generally mean a smaller position—not simply more money at risk.
References
Charles Schwab. (n.d.). Extended hours trading. Schwab. https://international.schwab.com/investment-products/extended-hours-trading
Fidelity Investments. (n.d.). Trading FAQs: Placing orders. https://www.fidelity.com/trading/faqs-placing-orders
Financial Industry Regulatory Authority. (2025, March 26). Stop orders: Factors to consider during volatile markets. FINRA. https://www.finra.org/investors/insights/stop-orders-factors-consider-during-volatile-markets
Financial Industry Regulatory Authority. (n.d.). Order types. FINRA. https://www.finra.org/investors/investing/investment-products/stocks/order-types
Investor’s Business Daily. (n.d.). When to sell stocks to take profits and avoid big losses. https://www.investors.com/how-to-invest/when-to-sell-stocks/
The Paper Trading Journal. (n.d.). Post-earnings momentum database [Data set]. Retrieved August 26, 2026, from https://papertradingjournal.com/post-earnings-momentum-database/
The Paper Trading Journal. (n.d.). Stock chart setup case studies. https://papertradingjournal.com/stock-chart-setup-case-studies/
The Paper Trading Journal. (n.d.). Trading statistics. https://papertradingjournal.com/trading-statistics/
Leave a Reply