Use AdaptGrow, a GPU-accelerated matrix factorization algorithm, to turn rolling correlation and tail-dependence matrices into hard clusters, soft factor loadings, and structural-break signals at single-GPU and multi-node scale
Quant strategies routinely group instruments for portfolio construction, risk aggregation, statistical arbitrage, and trade surveillance. Incorrect groupings can make concentrated positions appear diversified, obscure risk shared across nominal boundaries, and select statistical-arbitrage pairs whose relationships fail under stress.
The practical difficulty is that the right groupings are neither directly observable nor stable. Factor exposures drift, instruments change classifications, and dependencies can change sharply during market stress. A clustering pipeline must therefore separate routine variation from structural change and be inexpensive enough to rerun as new returns arrive.
There are two common ways to group instruments from a dependence matrix. Hard clustering methods are computationally cheap but assign every instrument to exactly one group, which breaks down at sector boundaries and masks the graded exposures that matter for risk budgeting. Soft factorization methods like SymNMF handle boundary instruments and produce usable factor loadings, but their dense matrix objectives have historically limited practical use to moderate instrument counts rather than the scale at which this problem actually lives.
