This article offers an incisive look into the factor vetting process, crucial for developing effective stock selection models in finance. It highlights the importance of distinguishing robust factors from mere statistical noise, minimizing multicollinearity, and preventing overfitting to enhance model performance.
The ensemble approach, in a broad sense, refers to combining multiple units (which could be models, methods, or components) to work together to achieve more accurate and reliable outcomes than any individual unit could achieve alone. This is akin to making a decision based on the collective wisdom of a diverse group, rather than relying on a single opinion.
