In this study, you’ll learn whether post-earnings momentum trading can actually be profitable, based on my dataset of 199 tracked setups. I’ll break down the strategy’s win rate, average return, profit factor, drawdown, and simulated profit, then show how results change when weaker setups are filtered out using breakout scores, multi-timeframe confirmation, fundamental alignment, and first-hour reaction quality.

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It’s true that every trader’s results will vary, even when applying a similar trading strategy to real-time price action. But at least based on my current dataset, yes, post-earnings momentum trading can be profitable.

I’ve now tracked 199 post-earnings setups using the same basic framework: enter at the close of the first hourly earnings reaction candle, trade in the direction of that move, use a $1,000 fixed position, apply a 15% catastrophic stop, and exit at the following trading day’s close.

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Across those 199 setups, the strategy produced approximately $3,838 in simulated profit. That works out to an average return of about +1.93% per trade, with a 55.8% win rate and a 1.66 profit factor.

The more interesting part is that the results get noticeably better when weaker setups are filtered out. And that’s exactly what I’ve been trying to do.

Below, we explore the profitability of my post-earnings momentum strategy, as well as what I’ve learned by tracking setups, digging into the data, and writing dozens of case studies about individual stock chart setups.

PTJ RESEARCH TOOL

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Quick Answer: Is post-earnings momentum trading a profitable trading strategy?

Yes. In my current dataset of 199 post-earnings momentum trades, a strategy using a fixed $1,000 position, a 15% stop, and a next-day EOD exit produced approximately $3,838 in simulated profit. The strategy averaged +1.93% per trade, won 55.8% of the time, and produced a 1.66 profit factor, which suggests there is a real positive expectancy in the sample so far.

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Stronger Breakout Scores Produced Better Results

One of the clearest findings in the dataset is that multi-timeframe breakout confirmation matters.

In my post-earnings momentum database, I score each setup from 0 to 3 based on whether the move is breaking out on the hourly, four-hour, and daily charts.

If there is no breakout on the hourly or any higher timeframe, that setup receives a 0 breakout score.

Meanwhile, if there is an hourly breakout, but no 4-hour or daily, the setup receives a 1. If there is an hourly and 4-hour break, the setup gets a 2 and if there’s a breakout on the hourly, 4-hour, and daily chart, the setup receives a 3 breakout score.

When I backtested my data and only included trades with a breakout score of 2 or higher, as opposed to trading every setup despite its breakout score, the strategy improved its overall expectancy.

That is a pretty big jump in quality. The strategy makes less total money because there are fewer trades, but the average trade gets better and the drawdown gets dramatically smaller.

That matters. A strategy that makes a little less money with much cleaner risk can actually be much easier to execute in the real world.

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Breakout Score 0 Looks Like the Weakest Bucket

My next finding is even more practical. It’s true that you don’t need to require a breakout score of 2 or higher to get a profitable trade. But the data is starting to make a pretty strong case that breakout score 0 setups should probably be avoided.

When I allowed scores of 1, 2, and 3, but skipped all score 0 trades, the strategy still improved.

That version produced:

That is a very interesting middle ground. I still get a decent number of opportunities, but by cutting out the setups with basically no higher-timeframe confirmation, I improve expectancy even more than requiring every trade to have a breakout score of at least 2.

Ultimately, the setups with a 0 score were responsible for a disproportionate amount of the ugliness in the dataset.

So the takeaway right now is not necessarily:

Only trade breakout score 2+.

It may be closer to:

Do not trade breakout score 0.

That is a much more flexible rule, and the data supports it surprisingly well.

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Recent Setups Were Objectively Ugly

The newest group of trades in the dataset was rough.

The most recent 27 setups produced just 7 winners and 20 losers, with an average next-day EOD return of -2.44% per trade. By comparison, the previous 172 setups averaged approximately +2.82% per trade.

And honestly, that weakness was noticeable while I was tracking the setups.

There simply wasn’t much clean momentum or follow-through in the first few trading days of September, despite there still being many eligible setups available for me to trade.

Part of that may have been the broader market environment. Stocks have recently been dealing with renewed inflation concerns, rising oil prices tied to the U.S.-Iran conflict, volatile Treasury yields, and uncertainty over an increasingly hawkish Federal Reserve.

Those forces helped produce a particularly messy start to September.

Hawkish comments from Fed Chair Kevin Warsh increased expectations for another rate hike, while subsequent comments from Governor Christopher Waller quickly pushed those expectations back in the opposite direction.

That kind of rapidly changing macro backdrop can make sustained individual-stock momentum harder to come by.

But I don’t think the macro environment tells the whole story. The setups themselves were also weaker.

Only 18.5% of the newest 27 setups had a breakout score of 2 or higher, compared with 43.0% across the previous 172 trades. In other words, far fewer recent earnings reactions were breaking through important levels across multiple timeframes.

There was also less overall confluence between the variables I track. Fewer setups combined strong breakout scores, supportive fundamentals, and a first-hour earnings move pointing in the same direction.

That distinction matters.

The recent losing stretch was not simply a collection of my highest-quality setups suddenly failing. The market was producing fewer high-quality setups in the first place.

That is actually one of the more useful findings from this dataset.

Post-earnings momentum appears to work better when the market provides genuine momentum to trade. When the macro backdrop gets messy and earnings reactions lack fundamental and technical confirmation, forcing marginal setups can quickly weaken the results.

Despite this ugly stretch, the full 199-trade sample remains profitable.

If anything, these 27 trades are helping clarify an increasingly important part of the strategy: knowing when a post-earnings move is strong enough to trade may be just as important as knowing how to trade it.

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Setup Quality Matters More Than Just the Earnings Move

The data is starting to show that not every earnings reaction deserves a trade.

The strongest setups in my dataset tend to have several variables lining up at the same time. The clearest signal so far is multi-timeframe breakout confirmation.

Even more interesting, simply eliminating breakout score 0 setups improved the strategy. That tells me I probably want to see at least one legitimate breakout before considering a trade, with two or more confirmations being preferable.

Fundamental alignment appears to strengthen the setup further.

Among trades with a breakout score of 2+ where the fundamentals also clearly agreed with the hourly earnings move, the strategy produced roughly a 59% win rate, +3.50% average return, and 2.64 profit factor in the current sample.

The size of the initial reaction matters too. My current data continues to favor more controlled moves over extreme ones.

The 5% to 10% first-hour reaction bucket has remained considerably healthier than the 20%+ bucket, while 20%+ initial moves have produced negative average EOD returns across the current sample.

So, based on the data so far, my stronger setups generally look something like this:

The setups I increasingly want to avoid are almost the mirror image.

A breakout score of 0 is becoming a major red flag. I also want to be cautious when the stock makes a huge first-hour move without higher-timeframe confirmation, when the fundamentals contradict the move, or when several variables are giving mixed signals.

The biggest lesson is becoming pretty simple:

The earnings move gets my attention. Confluence determines whether it deserves a trade.

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PTJ RESEARCH TOOL

Historical Setup Explorer

Search a stock ticker to browse every chart and image from its PTJ case study without leaving this page.

Enter a stock ticker

$

Search Setups

Try: ANF DKS RDDT PXS

Conclusion – Post-Earnings Momentum Trading Profitability

So, can you make money with post-earnings momentum trading? Based on my current dataset, yes.

Across 199 setups, the strategy produced approximately $3,838 in simulated profit using a fixed $1,000 position, a 15% catastrophic stop, and a next-day EOD exit.

The strategy averaged +1.93% per trade, won 55.8% of the time, and produced a 1.66 profit factor.

But the more important finding is that the quality of the setup matters a lot and there are many variables at play. I’ve learned that even simply eliminating breakout score 0 setups improved the strategy’s expectancy.

That is where I think the research is getting more interesting. The edge may not be:

earnings happened, stock moved, trade it.

It may be:

earnings happened, the stock moved, the fundamentals agreed, and multiple timeframes confirmed the move.

That is a much better setup.

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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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FAQ: Post-Earnings Momentum Trading Profitability

Is post-earnings momentum trading profitable?

Based on my current dataset, yes. Across 199 tracked setups, my strategy produced approximately $3,838 in simulated profit using a fixed $1,000 position, a 15% catastrophic stop, and a next-day EOD exit. The strategy averaged +1.93% per trade, had a 55.8% win rate, and produced a 1.66 profit factor.

What win rate does my post-earnings momentum strategy have?

Across the full 199-trade dataset, the strategy won 55.8% of the time, with 111 winners, 84 losers, and 4 breakeven trades. The results suggest a strategy does not need an extremely high win rate to produce positive expectancy.

How much does the strategy make per trade?

Using my current rules, the strategy averaged approximately +1.93% per trade across all 199 setups. With a fixed $1,000 position, that works out to roughly $19.30 of simulated profit per trade on average.

Does breakout confirmation improve post-earnings momentum results?

So far, yes. Setups with a breakout score of 2 or higher produced a 58.2% win rate, +2.94% average return, and 2.38 profit factor, compared with a 55.8% win rate, +1.93% average return, and 1.66 profit factor across the full dataset.

Should I avoid post-earnings setups with a breakout score of 0?

My current data increasingly suggests that breakout score 0 setups are weaker. When I eliminated score 0 trades but still allowed breakout scores of 1, 2, and 3, the strategy produced a 57.3% win rate, +2.44% average return, 1.97 profit factor, and approximately $466 max drawdown.

What makes a strong post-earnings momentum setup?

The stronger setups in my dataset tend to combine multi-timeframe breakout confirmation, supportive fundamentals, a meaningful but not excessively stretched first-hour reaction, cleaner price action, and sufficient volume. I currently prefer at least a breakout score of 1, with scores of 2 or 3 providing stronger confirmation.

Does the size of the first-hour earnings move matter?

Yes. My current data suggests that more controlled first-hour reactions have generally performed better than extreme moves. The 5% to 10% first-hour bucket has remained stronger, while the 20%+ bucket has produced negative average EOD returns in the current sample.

Can post-earnings momentum stop working during weak market conditions?

Performance can weaken significantly during poor momentum environments. The most recent 27 setups produced only 7 winners and 20 losers, averaging -2.44% per trade, while the previous 172 setups averaged approximately +2.82% per trade. Those recent trades also had much weaker breakout confluence, suggesting that market conditions and setup quality both matter.

Does this prove post-earnings momentum trading will always be profitable?

No. The dataset shows positive historical expectancy, not guaranteed future returns. Market conditions change, and the article itself shows that recent performance can deteriorate sharply when momentum, breakout confirmation, and variable confluence weaken. The more useful takeaway is that selectivity appears to matter just as much as the earnings reaction itself.