by Howard Su, Huan-Hsin Tseng
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Suggested foundations for financial machine learning and stochastic processes and pricing. Specialist prerequisites vary by paper.
Preserve time order and avoid future information in training.
Establish an interpretable baseline before a flexible model.
Understand what it means to condition on current information.
Follow Brownian dynamics and the second-order term in Itô calculus.
Account for dependence and overlapping observations.
Examine which information contributes to predictions.
Connect stochastic dynamics to no-arbitrage prices.
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.