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AI & Markets

Where machine learning and AI genuinely help in markets — and where they do not.

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Frameworks for this subject

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Walk-Forward Validation

Standard practice in quantitative research

Intermediate

Tests a model only on data from after the period it was fitted on, which is the minimum bar for any market model.

What it asks you to do

  • Fit on an in-sample window, then test on the period immediately following it.
  • Roll both windows forward and repeat across the full history.
  • Report only out-of-sample results.

Where it struggles

Prevents look-ahead bias but not survivorship bias or overfitting through repeated retesting. Every extra variant you try on the same data quietly weakens the result.

Source: Time-series cross-validation methodology

Articles

No AI & Markets articles published yet — the frameworks above are the reference for now.