Date of Submission
Open Access Senior Thesis
Robert Day School Prize for Best Senior Thesis in Economics and Finance
Bachelor of Arts
© 2021 Coleman A Cornell
The limited span of useful data, coupled with increasingly expansive asset universes, cripples the traditional mean-variance problem. When optimizing in these environments, the pronounced effect of estimation error yields extremely unstable portfolios when evaluated out-of-sample. As a proposed solution to the "curse of dimensionality," Gillen (2016) presents subset optimization as a technique to reduce the impact of estimation error. Instead of optimizing jointly over the entire asset universe, subset optimization na\"ively aggregates over many "subset portfolios" that each optimize over a much smaller random sample of assets. Given the inefficiencies when using naive aggregation, converged subset optimization is presented as an extension to subset optimization. Simulation and backtest experiments illustrate the potential for outperformance when implementing this method of convergence.
Cornell, Coleman, "Converged Subset Portfolios: An Extension to Subset Optimization" (2021). CMC Senior Theses. 2726.