by Manuel Noseda, Marco Paina, Nathan Soldati
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Suggested foundations for financial machine learning and text and language models in finance. Specialist prerequisites vary by paper.
Preserve time order and avoid future information in training.
Establish an interpretable baseline before a flexible model.
Understand how text is converted into numerical inputs.
Check whether labels measure the financial construct of interest.
Account for dependence and overlapping observations.
Examine which information contributes to predictions.
Test whether a text-derived signal survives an honest financial evaluation.
Choose a reproducible strategy, specify the trading rule, and test what remains after costs and estimation uncertainty.