by Dhagash Mehta, Joshua Rosaler, Kharen Musaelian, Luca Candelori, Martin T. Wells, Ryan Samson, Stefano Pasquali, Vahagn Kirakosyan
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Suggested foundations for financial machine learning and rates and fixed income. Specialist prerequisites vary by paper.
Preserve time order and avoid future information in training.
Establish an interpretable baseline before a flexible model.
Understand discounting in a stochastic-rate model.
Translate yield movements into first-order price risk.
Account for dependence and overlapping observations.
Examine which information contributes to predictions.
Build a consistent discount curve.
Choose a reproducible strategy, specify the trading rule, and test what remains after costs and estimation uncertainty.