You've probably heard of a covariance matrix, but can you actually code it up? This is listed on deep-ml.com as problem 10, which is an "easy" problem, but in my opinion it's definitely a "medium" ...

Portfolio optimization project combining Hierarchical Risk Parity with Ledoit-Wolf covariance shrinkage. Markowitz is unstable out of sample because it inverts a noisy covariance matrix. HRP sidesteps ...

Introduction Now that you've gotten a high-level overview of the use cases for PCA and some general notes regarding the algorithm's implementation, its time to dive deeper into the theory behind PCA.

I put together a simple Python script to stress test a portfolio (equal-weighted here) by shocking one asset at a time instead of the whole covariance matrix. On the left, I vary each stock’s ...

Many ecological systems are subject critical transitions, which are abrupt changes to contrasting states triggered by small changes in some key component of the system. Temporal early warning signals ...

Given the drawbacks of implementing multivariate analysis for mapping multiple traits in genome-wide association study (GWAS), principal component analysis (PCA) has been widely used to generate ...