by Iason Kynigakis, John Cotter, Thomas Conlon
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Suggested foundations for financial machine learning and asset pricing and factor research. Specialist prerequisites vary by paper.
Preserve time order and avoid future information in training.
Interpret exposures, residuals and uncertainty in fitted coefficients.
Work with returns and recognise their empirical limitations.
Account for dependence and overlapping observations.
Examine which information contributes to predictions.
Connect factor definitions to portfolio construction.
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.