  We determine the number of statistically significant factors in a forecast
model using a random matrices test. The applied forecast model is of the type
of Reduced Rank Regression (RRR), in particular, we chose a flavor which can be
seen as the Canonical Correlation Analysis (CCA). As empirical data, we use
cryptocurrencies at hour frequency, where the variable selection was made by a
criterion from information theory. The results are consistent with the usual
visual inspection, with the advantage that the subjective element is avoided.
Furthermore, the computational cost is minimal compared to the cross-validation
approach.
