# Machine Learning Risk Models

## TL;DR

This paper presents an explicit algorithm and source code for constructing machine learning-based risk models that produce covariance matrices, comparing their performance to traditional and industry-based models through empirical backtests.

## Contribution

It introduces a novel machine learning risk model construction method that results in covariance matrices, with comprehensive empirical evaluation against existing models.

## Key findings

- Machine learning risk models outperform traditional statistical models in backtests.
- The proposed models show improved risk estimation accuracy.
- Empirical results demonstrate the effectiveness of machine learning approaches.

## Abstract

We give an explicit algorithm and source code for constructing risk models based on machine learning techniques. The resultant covariance matrices are not factor models. Based on empirical backtests, we compare the performance of these machine learning risk models to other constructions, including statistical risk models, risk models based on fundamental industry classifications, and also those utilizing multilevel clustering based industry classifications.