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

Determining the number of factors in a forecast model by a random matrix test: cryptocurrencies