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

## TL;DR

This paper introduces a random matrix test to objectively determine the number of significant factors in a cryptocurrency forecast model, reducing subjectivity and computational cost compared to traditional methods.

## Contribution

The study applies a random matrices test to identify significant factors in a Reduced Rank Regression model for cryptocurrencies, offering an efficient alternative to cross-validation.

## Key findings

- The method aligns with visual inspection results.
- It reduces subjective bias in factor determination.
- Computational efficiency surpasses cross-validation.

## Abstract

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.