Does Past Performance Predict Future Returns: A look at designer models

[AlgoMan]

@ScifoSpace

Instead of just adding more and more features when performance drops, a better approach in high-dimensional settings is often to use techniques like PCA (as Marcos López de Prado has discussed) or just use ortogonality matrices. This can help reduce redundancy and multicollinearity among features without simply increasing model complexity. In many cases, this can be more effective than continuously expanding the feature set.

For those who has not experimented with PCA, try it out, it's quite interesting.

PCA Formula Exporter: Automatically turn Principal Components into Ranking Systems

@Scifospace came with a brilliant idea to break down the result from a Principal Component Analysis (PCA) Components to new factors for P123. While PCA is excellent for uncovering the "true" underlying drivers of stock returns by reducing multicollinearity and distilling correlated factors into independent components, translating the math into Portfolio123 formulas is incredibly tedious. To make this effortless, I built a small desktop application: PCA Formula Exporter. What it does: It takes…