"Trading without Regret"
by Dr. Michael Kearns, Professor at the Computer and Information Science Department at the University of Pennsylvania
No-regret learning is a collection of tools designed to give provable performance
guarantees in the absence of any statistical or other assumptions on the data (!),
and thus stands in stark contrast to most classical modeling approaches.
With origins stretching back to the 1950s, the field has yielded a rich body of algorithms and analyses that covers problems ranging from forecasting
from expert advice to online convex optimization.
Dr. Kearns will survey the field, with special emphasis on applications to quantiative finance problems, including portfolio construction and inventory risk.
"Building Diversified Portfolios that Outperform Out-of-Sample"
by Dr. Marcos López de Prado, Senior Managing Director at Guggenheim Partners
Hierarchical Risk Parity (HRP) portfolios address three major concerns of quadratic optimizers in general and Markowitz’s CLA in particular: Instability, concentration and underperformance. HRP applies modern mathematics (graph theory and machine learning techniques) to build a diversified portfolio based on the information contained in the covariance matrix. However, unlike quadratic optimizers, HRP does not require the invertibility of the covariance matrix. In fact, HRP can compute a portfolio on an ill-degenerated or even a singular covariance matrix, an impossible feat for quadratic optimizers. Monte Carlo experiments show that HRP delivers lower out-of-sample variance than CLA, even though minimum-variance is CLA’s optimization objective. HRP also produces less risky portfolios out-of-sample compared to traditional risk parity methods.
Read the corresponding white paper here.