How to Validate Financial Machine Learning Beyond Accuracy and Backtests
A trading AI can be accurate and still be economically useless. It can also produce an excellent backtest and still fail to provide convincing evidence that it has learned anything durable. These are not separate problems. They are different stages of the same validation problem.
The first asks whether a machine-learning prediction has economic value. The second asks whether the evidence for that value is robust and reproducible rather than accidental. For financial machine learning, passing only one of these tests is not enough.
Prediction Accuracy Is Not Trading Value
Machine-learning models are usually optimized for mathematical objectives: prediction error, classification accuracy, AUC, log loss, or similar metrics. Those metrics matter, but markets do not reward models for having good ML scores.
Markets expose decisions to transaction costs, volatility, asymmetric outcomes, timing constraints, and noise. Consider a model that predicts market direction correctly more often than another model....
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