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Reducing data dimensionality using PCA

One common problem when looking at financial data is the enormous number of dimensions we have to deal with. For instance, if we are looking at data from the S&P 500® index, we will have around 500 dimensions to work with! If we have enough computing power, we will be able to process so much data, but that will not always be the case. Sometimes, we need to reduce the dimensionality of the data.

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Alfredo Timermans