ETFs – Sparse Bond Portfolio Optimization and Market Liquidity

Download this research report to find out more about machine learning-enhanced portfolio construction and market liquidity data integration approaches that can be used in the active management of fixed income ETFs.

Aimed at investment managers and risk specialists, this new study provides a quantitative comparison between sparse portfolios built with traditional optimization techniques, and with unsupervised machine-learning enhancements focused on projected market liquidity volumes. The different cases presented in this white paper leverage the SS&C Algorithmics simulation-based methodology.