Firms Eye Machine Learning for Liquidity Risk Models

Many US mutual funds are expected to rely on vendor tools to comply with the SEC’s rule that they establish a formal liquidity risk management program that includes classifying the liquidity of their investments. These tools have the potential to improve the accuracy of firms’ models. But some see them as overly complex, and are seeking simpler approaches to compliance, reports Faye Kilburn.

However, some providers are taking advantage of recent advances in machine learning to develop these tools, and are now in the early stages of applying advanced statistical techniques to large, unstructured datasets that previously could not have been incorporated into the models.

Machine learning allows funds to account for the many factors that impact liquidity and account for very complex, non-linear relationships. Get it right, and the more sophisticated models promise value beyond the scop

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