by Austin Pollok, Kevin Robik
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Suggested foundations for financial machine learning and portfolio construction. Specialist prerequisites vary by paper.
Preserve time order and avoid future information in training.
Establish an interpretable baseline before a flexible model.
Understand the covariance structure of portfolio risk.
Read objectives, constraints and optimality conditions.
Account for dependence and overlapping observations.
Examine which information contributes to predictions.
Construct allocations and examine sensitivity to estimated inputs.
Choose a reproducible strategy, specify the trading rule, and test what remains after costs and estimation uncertainty.
Connect diversification, common risk factors, investor views, and the properties of a risk measure.