  This paper examines the applicability of Random Matrix Theory to portfolio
management in finance. Starting from a group of normally distributed stochastic
processes with given correlations we devise an algorithm for removing noise
from the estimator of correlations constructed from measured time series. We
then apply this algorithm to historical time series for the Standard and Poor's
500 index. We discuss to what extent the noise can be removed and whether the
resulting underlying correlations are sufficiently accurate for portfolio
management purposes.
