Marcos López de Prado writes the following in his book Advances in Financial Machine Learning:
In general, we need at least
\frac{1}{2} N (N+1)
independent and identically distributed (IID) observations in order to estimate a covariance matrix of size N that is not singular. For example, estimating an invertible covariance matrix of size 50 requires, at the very least, 5 years of daily IID data.
What is the reasoning for that number of observations? Where can I find some sources related to this that I can cite?