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Assessing Z-Score Mean Reversion Signals from Stock Returns

Article Quant Q&A · Author: Alec Ric
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Summary

The document describes a single-stock strategy that standardizes log returns and uses z-scores as trading signals: enter long at a low extreme, exit near zero, enter short at a high extreme, and cover near zero. The author says the backtest results are good but questions the statistical rationale, particularly because stationary log returns do not by themselves establish a profitable mean-reversion effect.

The response offers a skeptical view: mean reversion needs empirical support, and the strategy may face competition from momentum and the positive long-run drift of equities. It suggests that any gains could be modest relative to trend following and notes exposure to broad market risk, recommending stop losses. The exchange supplies no backtest design, out-of-sample evidence, costs, or formal research citation, so its performance claims and conclusions cannot be assessed from the text alone.

Key ideas

  • Stationarity of log returns alone does not establish a tradable mean-reversion effect.
  • The strategy uses standardized returns to define extreme entry and central exit signals.
  • The response contrasts mean reversion with momentum and long-term equity drift.
  • The reported backtest has no details about costs, robustness, or out-of-sample performance.
  • The response highlights market exposure and proposes stop losses as a risk control.

Tags

EquitiesMean reversionTechnical indicatorsBacktesting
Full text
# Dumb question : under the assumption of the normal distribution and using log return stationarity


# Dumb question : under the assumption of the normal distribution and using log return stationarity












Under the assumption of the normal distribution, I'm trying to create a single stock mean reversion strategy. I took the log returns because they are stationary, I standardized them and used the zscore as trading signals(buy when zscore is -2, take gains when the zscore is 0, sell when zscore is +2 and take gains when zscore goes to 0. This strategy comes from Ernest chan's books and blogs.

I backtested it and the results are good. But I'm unsure about the statistical logic behind it.

Moreover I did not find any serious research paper about this strategy, and Ernest Chan's books don't really detail why he uses the Zscore.

Does this strategy makes sense or is it dumb, and why ?

## Answer by Ralph Winters (score 1)

https://quant.stackexchange.com/a/70684

I don't know of any statistical basis for the phenomenon of mean reversion. It really defies the rule of independence which is usually part of statistical methods. Other than that, it is also counter to momentum investing which go against precisely what you are intending to do. Countering that with the positive skew of long term stock price movement, I think that you might make some money from it, but would be much less that what you would make with trend following. Also you would be subject to the same risks as the general stock market. I would only trade it with stop losses.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.