Quantocracy’s Daily Wrap for 08/23/2023

In the previous posts of this series, I detailed a methodology to perform stress tests on a correlation matrix by linearly shrinking a baseline correlation matrix toward an equicorrelation matrix or, more generally, toward the lower and upper bounds of its coefficients. This methodology allows to easily model known unknowns when designing stress testing scenarios, but falls short with unknown

Although the relationship is not immediately clear and obvious, the structural engineering has a lot in common with financial assets. In both cases we deal with the objects under stress over their entire lifetimes. There are two possible outcomes: something can break or perform well. The engineers run lots of analyses and tests before a given element is ready for its use and service. It is

Sector neutralization is a technique to hedge out sector bets and reduce the impact of sector-specific risks on the portfolio by ranking factors within sectors rather than across sectors. This post uses the debt-to-equity ratio to show why sector neutralization is important and how to perform it in Pipeline. This post is part of the fundamental factors series, which explores techniques for

Principal component analysis (PCA) is used widely in data science. Its a way to reduce the number of dimensions in a data set. Its also used in quant finance to find alpha. In a stock portfolio, a dimension might be a column of returns for one of the stocks. Once you get the model built, you could spend your time tweaking the code. Or, you can do it in an interactive dashboard to power your