Stock-Level Risk Parity

Overview

Traditional risk parity implementations operate at the asset class level due to computational constraints. This work extended risk parity to the stock level across large universes, incorporating multidimensional risk allocation constraints (sector, style, factor exposures).

Approach

Adapted a binormalization-based algorithm from signal processing to construct a scalable stock-level risk balancing framework. The system allocates risk across multiple dimensions simultaneously, preserves desired structural exposures, and maintains convergence stability in large universes.

Impact

Reduced portfolio construction runtime from hours to seconds, enabling materially expanded research and backtesting capability. Provided architectural foundation for later defensive equity and defensive growth implementations.