Advanced Alpha Models and Factor Timing

The multifactor model of Note 4 applies one set of linear weights to every stock at every time — three restrictive assumptions in one sentence. This note relaxes each: contextual modelling lets factor weights differ across stock groups (because factor efficacy demonstrably does), nonlinear modelling lets the return-score relationship curve (because for some factors it demonstrably is curved), and factor timing lets the weights move through time (because factor returns demonstrably cycle). Every relaxation buys realism and pays in parameters — and parameters bought with noisy data are model risk (model risk management). The note ends by observing that the modern machine-learning stack is all three relaxations performed jointly and automatically.

Learning objectives. By the end of this note you should be able to: