It seems that shrinking the covariance matrix is especially useful if the number of individual stocks is greater than the number of data points. However is there any special gain if you're not constrained by the data ?
Answer
When using the estimated covariance in the context of mean-variance optimization, then, yes, shrinking the covariance matrix is useful even when you have sufficient data.
A good reference is Golts and Jones, A Sharper Angle on Optimization, who discuss convariance shrinkage among other techniques and give two examples of the usefulness of shrunk covariance estimates in forming (unconstrained) optimal portfolios. The first is desensitizing the optimizer to small variations in alphas of highly correlated assets. The second is controlling leverage.
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