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Avoiding Overfitting and Colinearity with Regularization
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Avoiding Overfitting and Colinearity with Regularization

Fighting 2 very important problems in quant finance

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Vertox
May 05, 2025
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Avoiding Overfitting and Colinearity with Regularization
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2 of the most common problems we face when building trading models are overfitting and colinearity. Regularization is a technique that we can use to combat both of those problems.

More broadly, regularization is a process that converts the answer of a problem to a simpler one.
This can be done in multiple different ways. Some explicit regularization techniques are penalties and constraints. Implicit regularization techniques are early stopping, robust loss functions, discarding outliers etc.

Bias-variance tradeoff tells us that as we increase regularization to learn broader patterns in our data our variance decreases but therefore our bias (inaccuracy) increases.

https://en.wikipedia.org/wiki/Bias%E2%80%93variance_tradeoff

Table of Content

  1. Norm Penalization and Constraints

  2. Lasso, Ridge and Elastic Net Regression

  3. Polynomial Regression

  4. Model Selection

  5. Robust Covariance Matrix

  6. Final Remarks

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