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In this famous paper, Bailey and De Prado discard Cross Validation as tool to check for Backtest overfitting, on the ground that it is just an holdout method:

... If we apply the holdout method enough times (say 20 times for a 95% confidence level), false positives are no longer unlikely: They are expected. The more times we apply holdout, the more likely an invalid strategy will pass the test, which will then be published as a single-trial outcome ...

But publishing the results as a single-trial outcome is a misuse of Cross Validation. One should publish the average OOS performance of the K trials. So Bailey and De Prado don't have a point there. Cross Validation does solve the problem of backtest overfitting.

Am I missing something?

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    $\begingroup$ CV is usually abused to become an equivalent of in sample diagnostics. It can help with overfitting when done properly but it doesn’t “solve “ the problem $\endgroup$ Jun 15 at 19:07
  • $\begingroup$ @Aksakal thank you, could you elaborate a bit more on "helps but does not solve"? By "solve" I meant that CV correctly estimates the OOS performance. What would be a more desirable outcome that correctly estimating the OOS performance? $\endgroup$
    – elemolotiv
    Jun 16 at 6:19
  • $\begingroup$ CV estimates cross validation performance when done properly. Can it “correctly” estimate it? In social sciences it is a big fat question. Unless you control regressors it is easy to overestimate oos performance with cv $\endgroup$ Jun 16 at 14:04
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If they publish information about all K trials, then you're right. But the author's point is that that's not typical practice. Typical practice is to not disclose that information, and it amounts to p-hacking where the statistical power of the test differs to what's being advertised.

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