This course explores portfolio construction as a learning problem, incorporating complex risk aversions (e.g., VaR)
and exposure constraints. Students will learn standard and alternative-data predictors, including intraday signals
and neural-network insights into liquidity dynamics. An end-to-end perspective connects prediction, execution, and
performance attribution, highlighting cross-asset interactions such as earnings season effects.
