Posts tagged 'model-validation'
Define timing, data splits, retraining, and cost rules for a fair comparison of logistic regression and baseline strategies, before testing actual performance.
Separate signal, execution, return, and label timing. Use synthetic unit tests to check information leakage and preprocessing boundaries.
Separate AI's research value from investment profitability and define evidence for usefulness through chronological testing, baselines, and reproducible records.
Examine noise, changing markets, repeated searches, leakage, costs, and selection bias, and define ways to challenge promising financial ML results.
Design a fair comparison of momentum, logistic regression, trees, and forests. Control model selection and distinguish classification from economic performance.
Design features, labels, time splits, and monthly retraining for a logistic-regression baseline, keeping predictive accuracy separate from investment returns.
Plan hypotheses, baselines, chronological splits, walk-forward validation, and protected test periods. Examine selection bias and the role of research records.
Forge Fate