Robust Cross-Section Studies in the Presence of Outliers

Cross-sectional studies are particularly sensitive to the presence of outliers.

Even a small percentage of outliers can cause a very large percentage of wrong signals:
a) Buys that should be sells (false positives)
b) Sells that should be buys (false negatives)

In this experiment, we compare the performance of two regression methods:
i) Ordinary Least Squares (OLS): The standard regression method in the industry and in academia
ii) Random Sample Consensus (RANSAC): A machine learning method popular in computer vision

(c) 2019 by Marcos Lopez de Prado. All Rights Reserved.

machine learning,econometrics,statistics,RANSAC,robust,outliers,