by Eva Lütkebohmert, Julian Sester
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Suggested foundations for financial machine learning and monte carlo and numerical simulation. Specialist prerequisites vary by paper.
Preserve time order and avoid future information in training.
Establish an interpretable baseline before a flexible model.
Understand convergence, sampling error and confidence intervals.
Generate reproducible samples and distinguish randomness from model assumptions.
Account for dependence and overlapping observations.
Examine which information contributes to predictions.
Turn a discounted payoff into an estimator.
Choose a reproducible strategy, specify the trading rule, and test what remains after costs and estimation uncertainty.
Start in discrete time, move to Brownian motion, then read the original pricing argument and simulate it.