Every earnings season adds another piece to my growing post-earnings momentum database and trading research. After recording 60 real-world trades, I decided to compare one of the most common debates among active traders: Is it better to trade long or short? By measuring everything from win rates and maximum favorable excursions to profit factors and expectancy, the data revealed a far more nuanced answer than I expected.
Most traders and investors have a natural preference for either buying stocks or short selling them.
Many believe bullish trades are easier because markets tend to rise over time, while others argue that bearish momentum is often stronger and more decisive after disappointing earnings.
But is it actually better to trade long or short?
To answer that question, I analyzed 60 post-earnings momentum trades from my proprietary trading database, which currently contains 39 long setups and 21 short setups.
Every trade followed the same post-earnings momentum strategy, with all entries taken at the close of the first hourly earnings candle and performance tracked using standardized exit statistics.
I directly compared how bullish and bearish earnings momentum setups performed under two different exit strategies.
First, I measured the results of simply holding every trade until the close of the next trading day. Then, I compared those findings with an active risk management approach using a 9% profit target and 5% stop loss.
The results were far more nuanced than I expected.
While long and short trades generated nearly identical maximum profit opportunities, the best-performing direction ultimately depended on how the trades were managed after entry.
Below, we explore what my real-world trading data revealed, how both short and long setups performed across a range of important variables, as well as which side ultimately produced the strongest results.
But first…
Quick Answer: Is it better to trade long or short?
Based on my analysis of 60 real post-earnings momentum trades, neither direction was universally better. Long and short setups produced almost identical maximum profit opportunities, averaging about 12% MFE each. However, short trades significantly outperformed when held passively until the close of the next trading day, posting a 76.2% win rate and an average return of 5.52%, compared with 59.0% and 2.64% for long trades. When managed with a 9% profit target and 5% stop loss, the results reversed, with long trades generating higher expectancy and a better profit factor. Ultimately, this dataset suggests the best direction depends not only on the setup itself, but also on the exit strategy used.
Key Trading Statistics – Long Vs Short Post-Earnings Momentum
- 60 real post-earnings momentum setups analyzed, including 39 long trades and 21 short trades.
- Long and short trades generated nearly identical maximum profit opportunities, averaging 12.18% MFE for longs and 12.36% for shorts.
- Passive short trades produced a 76.2% win rate, compared with 59.0% for long trades.
- Passive short trades returned 5.52% on average, more than double the 2.64% average return for long trades.
- The median passive return was 7.32% for shorts versus 2.57% for longs, showing the advantage wasn’t driven by a handful of outlier trades.
- Passive short trades achieved a profit factor of 3.34, compared with 1.92 for long trades.
- 64.1% of long trades and 61.9% of short trades reached at least a 9% maximum favorable excursion (MFE).
- Short trades experienced slightly larger average pullbacks, with an average 6.52% MAE versus 5.82% for long trades.
- Using a 9% profit target and 5% stop loss reversed the results, with long trades producing a 3.28% average return compared with 2.16% for shorts.
- A 9% profit target and 5% stop loss reduced short trade expectancy by approximately 60.8% compared with simply holding until the close of the next trading day.
- Shorts didn’t produce larger peak moves—they were simply much better at preserving those gains into the next-day close.
Methodology – How the Long Vs Short Comparison Was Conducted
To compare long and short post-earnings momentum trades, I analyzed 60 real-world setups from my proprietary trading statistics database.
Every position followed the same entry methodology, with trades entered at the close of the first hourly earnings candle after an earnings announcement.
I then evaluated each trade using two different exit strategies.
- The first was a passive approach, where every position was held until the close of the next trading day.
- The second used active risk management, exiting trades with either a 9% profit target, a 5% stop loss, or the close of the next trading day if neither level was reached.
Finally, I compared the results using several standardized performance metrics, including win rate, average return, median return, maximum favorable excursion (MFE), maximum adverse excursion (MAE), expectancy, and profit factor, to determine whether either direction consistently outperformed the other.
Passive Exit Results: Do Long or Short Trades Perform Better?
Before testing active risk management, I wanted to see how each direction performed using the simplest possible exit strategy: holding the position until the close of the next trading day.
This passive approach removes the influence of profit targets and stop losses, allowing the underlying strength of each post-earnings momentum setup to be compared more directly.
Interestingly, the initial results strongly favored bearish earnings momentum.
Passive short trades produced a 76.2% win rate, compared with 59.0% for long trades, while also generating an average return of 5.52% versus 2.64% for longs.
Even more importantly, the median return was 7.32% for shorts compared with 2.57% for longs, suggesting the advantage wasn’t driven by a small number of exceptional trades.
| Passive Exit Metric | Long Trades | Short Trades |
|---|---|---|
| Number of Trades | 39 | 21 |
| Winning Trades | 23 | 16 |
| Losing Trades | 16 | 5 |
| Win Rate | 59.0% | 76.2% |
| Average Return | 2.64% | 5.52% |
| Median Return | 2.57% | 7.32% |
| Average Winner | 9.33% | 10.35% |
| Average Loser | -7.46% | -9.93% |
| Profit Factor | 1.92 | 3.34 |
Passive short trades also achieved a profit factor of 3.34, nearly double the 1.92 recorded by long trades.
Interestingly, both directions produced almost identical average maximum favorable excursions (MFE), indicating that short trades didn’t necessarily offer larger profit opportunities—they simply did a better job of preserving those gains into the close of the next trading day.
At first glance, the data suggests that passive exits strongly favored bearish post-earnings momentum trades. However, as the next section demonstrates, that conclusion changed dramatically once a fixed 9% profit target and 5% stop loss were introduced.
Why Did Passive Short Trades Perform Better?
The clearest explanation is NOT that short trades produced larger moves.
In fact, the average maximum favorable excursion was almost identical for both directions: 12.36% for shorts and 12.18% for longs. The available profit opportunity was therefore nearly the same.
The difference was what happened after those favorable moves developed.
Short trades were more likely to continue lower or remain weak into the close of the next trading day, allowing them to preserve a larger portion of their gains.
Long trades, by comparison, were more likely to give back part of the move before the passive exit occurred.
Passive shorts produced an average return of 5.52% and a median return of 7.32%, compared with 2.64% and 2.57% for longs.
In other words, the bearish setups did not necessarily travel farther—they simply converted more of their available momentum into realized next-day returns.
That distinction may be the most important finding in the analysis.
Within this dataset, the apparent advantage of short trades came from stronger gain retention and more reliable next-day continuation, not from larger maximum price moves.
Did Long or Short Trades Produce Bigger Profit Opportunities?
Although passive short trades delivered stronger next-day returns, that does not mean they produced substantially larger price moves after entry.
To compare the maximum opportunity available on each side, I analyzed the maximum favorable excursion (MFE) recorded for all 60 setups.
The results were remarkably similar.
- Long trades generated an average MFE of 12.18%, while short trades averaged 12.36%.
- Median MFE was also close, at 10.48% for longs and 11.62% for shorts.
- Long trades were slightly more likely to reach at least a 9% favorable move, while short trades were more likely to cross the 10% threshold.
Overall, however, the differences were too small to suggest that either direction consistently produced larger maximum profit opportunities.
| MFE Performance Metric | Long Trades | Short Trades | Difference |
|---|---|---|---|
| Average MFE | 12.18% | 12.36% | 0.18 pts |
| Median MFE | 10.48% | 11.62% | 1.14 pts |
| Reached at Least 9% MFE | 64.1% | 61.9% | 2.2 pts |
| Reached at Least 10% MFE | 53.8% | 61.9% | 8.1 pts |
| Highest Recorded MFE | 39.13% | 34.22% | 4.91 pts |
Ultimately, there was virtually no meaningful difference in maximum profit opportunity.
As mentioned above, the stronger passive performance of short trades came from retaining more of their gains—not from producing substantially larger favorable moves.
Which Side Experienced Larger Pullbacks?
Maximum adverse excursion (MAE) measures the largest move against a trade before it reached its maximum favorable excursion or was ultimately exited. In other words, it helps quantify how much downside each position experienced after entry.
Despite producing stronger passive returns, short trades actually experienced slightly larger adverse excursions than long trades. Average MAE measured 6.52% for shorts compared with 5.82% for longs, while the median MAE was 4.35% versus 3.53%, respectively.
Short trades were also more likely to experience pullbacks of at least 5%, although the percentage exceeding 10% was nearly identical between the two groups.
| MAE Performance Metric | Long Trades | Short Trades |
|---|---|---|
| Average MAE | -5.82% | -6.52% |
| Median MAE | -3.53% | -4.35% |
| Trades Exceeding -5% MAE | 35.9% | 42.9% |
| Trades Exceeding -10% MAE | 28.2% | 28.6% |
| Worst Recorded MAE | -23.28% | -21.80% |
These findings suggest that bearish momentum trades often required traders to withstand larger temporary reversals before the trend resumed.
This helps explain why passive exits favored short trades while the 9% profit target and 5% stop loss strategy did not. A fixed 5% stop loss was more likely to remove traders from otherwise successful bearish setups before they had an opportunity to continue lower.
How Did A 9% Profit Target and 5% Stop Loss Affect Long Vs Short Trades?
Up to this point, my data appeared to strongly favor bearish earnings momentum. Passive short trades produced a substantially higher win rate, average return, median return, and profit factor than long trades.
However, I also wanted to know whether those results would hold up under a more active approach to risk management.
To answer that question, I applied a 9% profit target and 5% stop loss to every trade in the dataset. If neither level was reached, the position was exited at the close of the next trading day.
This created a standardized comparison to determine whether active trade management changed the outcome.
Surprisingly, it did.
Once a fixed profit target and stop loss were introduced, the results completely reversed.
Long trades outperformed short trades across several important performance metrics, suggesting that trade management may have a greater impact on overall profitability than simply choosing between long and short setups.
| Active Strategy Metric (9% PT / 5% SL) | Long Trades | Short Trades |
|---|---|---|
| Winning Trades | 23 | 11 |
| Losing Trades | 16 | 10 |
| Win Rate | 59.0% | 52.4% |
| Average Return | 3.28% | 2.16% |
| Median Return | 9.00% | 5.39% |
| Profit Factor | 2.79 | 1.91 |
| Average Winner | 8.67% | 8.67% |
| Average Loser | -4.47% | -5.00% |
| Expectancy (Average Return) | 3.28% | 2.16% |
Why Did Active Risk Management Reverse the Results?
At first glance, the passive results seemed to suggest that short trades were clearly superior.
However, once every trade was managed using a 9% profit target and 5% stop loss, the advantage shifted to long trades.
Although short trades ultimately generated stronger next-day returns, they also experienced slightly larger adverse excursions after entry. Many bearish earnings momentum trades appeared to undergo sharp but temporary rallies before continuing lower.
- On average, short trades recorded a 6.52% maximum adverse excursion (MAE) compared with 5.82% for long trades.
- The median MAE told a similar story, measuring 4.35% for shorts versus 3.53% for longs.
- 42.9% of short trades experienced pullbacks of at least 5%, compared with 35.9% of long trades.
Those larger temporary reversals made bearish momentum trades much more likely to trigger the 5% stop loss before the primary trend resumed.
In contrast, long trades generally experienced smaller retracements, allowing more positions to remain open long enough to reach the 9% profit target.
This is reflected in the active risk management strategy results, where longs produced a 3.28% average return and 2.79 profit factor, compared with 2.16% and 1.91 for shorts.
Ultimately, this suggests that the difference wasn’t the quality of the setups themselves—it was how they behaved after entry. Within this dataset, bearish earnings momentum often required traders to withstand deeper temporary pullbacks before continuing lower.
A fixed 5% stop loss may therefore have been too restrictive for many short trades, while it appeared to fit the natural behavior of bullish momentum setups much better.
Passive Exit Vs Active Exit
In the end, our long-versus-short comparison changed substantially depending on how each trade was exited.
Holding every position until the next-day close strongly favored short trades, while applying a fixed 9% profit target and 5% stop loss shifted the advantage to long trades.
This suggests that trade direction alone did not determine performance.
Instead, the exit strategy had a major influence on which setups produced the strongest results.
Passive exits were better for bearish momentum, giving them more time to recover from temporary rallies, while the tighter active risk management approach appeared better suited for long setups because they experience smaller average pullbacks.
Hold Until Next-Day Close
Passive exits allowed short trades to preserve more of their bearish momentum into the close of the next trading day.
9% Target / 5% Stop
The fixed stop and target better matched the smaller adverse excursions recorded by long trades.
Statistical Limitations
While the findings in this study are compelling, they should be interpreted within the context of the dataset.
The analysis is based on 60 real post-earnings momentum trades, including 39 long setups and 21 short setups. Although this is a meaningful sample for identifying early trends, it is still relatively small from a statistical perspective, particularly on the short side.
Rather than viewing these results as definitive proof that one direction is always superior, they should be considered evidence of how this particular post-earnings momentum strategy has performed so far.
Continuing to collect standardized trading data will make future analyses increasingly reliable and help determine whether these patterns persist over hundreds of trades rather than dozens.
Final Verdict: Is It Better to Trade Long or Short?
Based on my analysis of 60 real post-earnings momentum trade setups, neither long nor short setups proved to be universally superior.
Instead, the results showed that the answer depends largely on how the trade is managed after entry.
Perhaps most importantly, both directions generated nearly identical maximum profit opportunities, suggesting that the quality of the setup mattered more than whether it was bullish or bearish.
Ultimately, the best trading decisions aren’t based on opinions—they’re based on actual trading research and scientific evidence.
As my post-earnings dataset grows, so too will my understanding of what truly creates a profitable edge in post-earnings momentum trading.
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
Is it better to trade long or short?
Based on my analysis of 60 post-earnings momentum trades, neither direction was universally better. Passive exits favored short trades, while an active strategy using a 9% profit target and 5% stop loss produced better results for long trades.
Why did short trades outperform with passive exits?
Short trades didn’t generate significantly larger profit opportunities—they simply retained more of those gains into the close of the next trading day. Passive shorts produced a 76.2% win rate and an average return of 5.52%, compared with 59.0% and 2.64% for long trades.
Why did long trades perform better with a 9% profit target and 5% stop loss?
Long trades generally experienced smaller pullbacks after entry, making them less likely to trigger the 5% stop loss before reaching the 9% profit target. Short trades often experienced deeper temporary rallies before continuing lower, reducing their performance under active risk management.
What is maximum favorable excursion (MFE)?
Maximum favorable excursion (MFE) measures the largest unrealized profit available after entering a trade before it is closed. It represents the maximum opportunity the market offered, regardless of where the trade was ultimately exited.
What is maximum adverse excursion (MAE)?
Maximum adverse excursion (MAE) measures the largest move against a position after entry. It helps traders understand how much a trade typically retraces and whether their stop-loss placement is appropriate for a particular strategy.
Is short selling riskier than buying stocks?
Short selling generally carries additional risks, including short squeezes, borrowing costs, and theoretically unlimited losses if risk is not managed properly. In this dataset, short trades also experienced slightly larger average pullbacks than long trades, despite producing stronger passive returns.
Do these findings apply outside of earnings season?
Not necessarily. Every trade in this study followed the same post-earnings momentum strategy, with entries taken at the close of the first hourly earnings candle. Different trading strategies and market conditions may produce very different results.
Will these results change as more trades are added?
Possibly. This analysis is based on 60 real-world trades, and the database continues to grow every earnings season. As more data is collected, the conclusions may become stronger, weaker, or change altogether. That’s why I continuously update my research using standardized trading statistics.
Can beginners short stocks?
While beginners can learn to short stocks, it is generally more complex than buying shares. Short selling involves additional risks, margin requirements, and the potential for short squeezes. New traders should fully understand these risks before incorporating short positions into their trading strategy.
Which exit strategy worked best overall?
Neither exit strategy was universally superior. Holding trades until the close of the next trading day produced the best results for short trades, while a 9% profit target and 5% stop loss improved the performance of long trades. Within this dataset, trade management had a greater impact on profitability than simply choosing between long and short setups.
APA References
Corporate Finance Institute. (n.d.). Maximum adverse excursion (MAE). https://corporatefinanceinstitute.com/resources/career-map/sell-side/capital-markets/maximum-adverse-excursion-mae/
Corporate Finance Institute. (n.d.). Maximum favorable excursion (MFE). https://corporatefinanceinstitute.com/resources/career-map/sell-side/capital-markets/maximum-favorable-excursion-mfe/
Laforest, J. (2026). Post-earnings momentum trading statistics database (Version current through 60 trades) [Unpublished proprietary trading dataset]. Paper Trading Journal. https://papertradingjournal.com/2026/07/10/post-earnings-momentum-analysis/
Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance.
Schwager, J. D. (2012). Market sense and nonsense: How the markets really work (and how they don’t). John Wiley & Sons.
Van K. Tharp. (2007). Trade your way to financial freedom (2nd ed.). McGraw-Hill Education.
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