An earnings question came up inside our Owl Bundle User Group (OBUG) while we were forward testing Dr. Ken Long’s Critical States system. One of our members experienced a large earnings-related gap loss on AMZN — a tail event consistent with earnings-driven volatility. Naturally, the question followed: Should we avoid trading through earnings?
Rather than debate it philosophically, we decided to test it properly.
The Objective
Evaluate whether avoiding trades before or during earnings improves the risk-adjusted performance of the Critical States system within our curated stock portfolio.
Specifically:
Does earnings exposure degrade expectancy?
Does it increase drawdown fragility?
Does it harm P&L or Monte Carlo stability?
Is an earnings filter statistically required?
We ran EdgeRater backtesting & Monte-Carlo analysis across:
6-year window
11-year window
16-year window
This was not anecdotal analysis. It was systematic, data-driven testing.
Case 1: Avoid Trades ± X Days Around Earnings
If earnings were structurally harmful, removing them should materially improve performance.
It didn’t.
Across 11 and 16 years:
Expectancy remained stable
FE25 ( Final Equity at the 25th percentile - Monte Carlo analysis) showed no structural degradation
Drawdowns remained comparable
No durable long-term advantage emerged
Conclusion: Earnings exposure does not degrade expectancy or compromise long-term system integrity.
Case 2: Avoid Only Pre-Earnings Trades
We isolated entries occurring X days before earnings, while allowing trades after earnings.
What the data showed:
Over 11- and 16-year windows, expectancy was flat to slightly lower than baseline.
FE25 and drawdown metrics remained statistically similar.
Trade count decreased without a compensating robustness gain.
Interpretation:
Blocking only pre-earnings trades does not enhance structural integrity.
It slightly alters trade distribution, but does not improve risk-adjusted performance.
In longer horizons, it marginally reduces edge density.
In short, it may change how the ride feels — but not the structural edge.
Case 3: Enter Only Before Earnings
This is where the data became interesting. When isolating entries 7–9 days before earnings: Across multiple timeframes:
Expectancy improved vs baseline
Win rate increased
FE5 improved
Drawdowns remained comparable
Sample sizes were meaningful
Interpretation: There appears to be a volatility regime 1–2 weeks before earnings that slightly favors the Critical States profile.
Structural Insight
Critical States is a short-term volatility-aware system. Earnings events can create bigger price swings and occasional sharp gaps, but over 16 years of testing, there is no evidence that earnings exposure damages the system’s long-term performance.
Across 6-, 11-, and 16-year backtests of the Critical States template within our curated portfolio, we find no statistical reason to add an earnings avoidance filter. Expectancy, Monte Carlo robustness (FE25), and drawdown behavior remain stable with earnings included.
Important Execution Distinction
The conclusions above assume:
Mechanical execution
Taking every valid signal
Consistent position sizing
Expectancy math only holds when the full distribution is sampled.
If a trader:
Skips signals intermittently
Sizes differently around earnings
Avoids trades emotionally
Then the tested expectancy no longer strictly applies. In that case, avoiding earnings may be psychologically prudent — but that becomes a discretionary overlay, not a system requirement. This distinction matters.
Also, these conclusions apply to diversified portfolio implementation. Symbol-level earnings sensitivity may vary, and concentrated single-symbol execution warrants separate evaluation. Future research may segment results by market regime to assess earnings sensitivity during high-stress periods
Join us inside OBUG
Most traders adjust systems based on recent experience or isolated outcomes. We take a different approach. Inside OBUG, every idea is treated as a hypothesis to be tested — not assumed.
We focus on building a structured, evidence-based process: Systematic backtesting across multiple market cycles, Monte Carlo analysis to understand distribution and drawdown behavior, and forward testing to validate real-time execution If you’re interested in developing trading decisions grounded in data rather than reaction, you’re welcome to join us.. Join us at OBUG:
This material is provided for educational and research purposes only. Results are based on historical backtesting and do not represent actual trading performance. Past performance is not indicative of future results. This is not investment advice or a recommendation to buy or sell any security.