Applying random matrix theory to extract principal components of intra-day stock price correlations

We propose to apply the random matrix theory to extract principal components from a large number of time series with high-level of complexity and randomness, such as intra-day stock prices. We show that the corresponding eigenvector components of signals reflect the actual trends of the real markets.