In finance, a covariance matrix can be a useful tool to estimate the cross correlations between different stocks. However, historical data contains random noise, which can alter the underlying information. Here we illustrate how Random Matrix Theory can be used to filter a diagonalisable matrix and how information about the market is carried inside its eigenvectors.

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