We introduce "factors on demand", a modular, multi-asset-class return decomposition framework that extends beyond the standard systematic-plus-idiosyncratic approach. This framework, which rests on the conditional link between flexible bottom-up estimation factor models and flexible top-down attribution factor models, attains higher explanatory power, empirical accuracy and theoretical consistency than standard approaches.

We explore applications stemming from factors on demand:
- The joint use of a statistical model with non-idiosyncratic residual for return estimation and a cross-sectional model for return attribution
- The optimal hedge of a portfolio of options, even when the investment horizon is close to the expiry and thus the securities are heavily non-linear
- The "on demand" feature of FoD to extract a parsimonious set of few dominant attribution factors/hedges that change dynamically with time
- Accommodating in the same platform global and regional models that give rise to the same, consistent risk numbers
- Point-in-time style analysis, as opposed to the standard trailing regression
- Risk attribution to select target portfolios to track the effect of incremental alpha signals on the allocation process

Fully commented code supporting the above case studies is available at MATLAB Central File Exchange under the author's page.