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Sounds like PCA is not the approach you're looking for. If you're looking to transform a risk vector in terms of securities V into a risk vector in terms of securities W, then the basic approach would be to perform a linear regression of V against W. The resulting regression coefficients will form a matrix B which will give a change of basis between V and W. ...


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If X contains several highly correlated indexes, the first PCA will be a linear combination of them and its weights will be similar because at the end they represent the same underlying phenomena. When you do a regression with the same variable in Y and X you will have perfect match of that specific regressor by construction. The real problem of colinearity ...


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I was just like you when I started out: I had learned a lot about machine learning (mainly neural networks and genetic algorithms/programming) and used it heavily. I also had learned about classic statistics but not nearly as much as about ML. The problem with ML is - as I see it today - that you are often taking a sledgehammer to crack a nut, meaning: ...


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The main reason to use traditional methods is interpretability. Specially when you are dealing with portfolios. Portfolios are nothing more than a linear combination of assets. Many Machine Learning methods are highly non-linear and therefore are hard to replicate with a real portfolio. For example if you want to minimize volatility of your emerging markets ...



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