Overbond applies machine learning and AI analytics to assess bond market liquidity
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Overbond applies machine learning and AI analytics to assess bond market liquidity

The deadline looms for SEC-regulated investors to report on the liquidity of individual bond positions, but the more pressing question is the accuracy of fund valuations.


June brings the deadline for US asset managers to comply with SEC rules first proposed in 2017 – and amended in 2018 – to implement and disclose their liquidity risk-management programmes.

The aim is to reduce the danger that customers of mutual funds, who may have assumed they could cash out their investments in all circumstances, find their money trapped behind redemption gates if funds are unable to dispose of positions in a market panic.

The rule requires every fund to classify each individual portfolio investment into one of four liquidity buckets: highly liquid, moderately liquid, less liquid and illiquid investments.

These buckets should take into account relevant market-, trading-, and investment-specific considerations, as well as market depth and whether sales of an investment would notably change its market value.

Funds must review these liquidity classifications at least monthly, if not more frequently.

Tough challenge

The rules also impose on open-ended funds a cap of no more than 15% of portfolios to be in highly illiquid investments, and require disclosure of the proportion to be held in highly liquid assets and so, in theory, be easily convertible into cash to meet redemptions.

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