How to measure “market quality” as trading patterns change

11 October 2011

A working definition of “market quality” might be “the prospects for a market participant to successfully match his/her order at a competitive price on a given trading venue”. This means that detailed measurements and analysis of liquidity become central to market quality, and by implication, market attractiveness to investors.

Cinnober has published a white paper on how to measure market liquidity and understand the true impact of algorithmic trading on the market. The required functionality is supplied by the market surveillance system, Scila Surveillance, going beyond market surveillance in its traditional sense.

Today’s equities markets are increasingly fragmented, and the competition for liquidity among trading venues is fierce. To maximize available liquidity, venues routinely fine-tune the parameters that define their market models, such as the level of market transparency, counterparty information, tick-sizes, trading method, tariffs, etc.

Moreover, algorithmic trading contributes to a growing part of trading volumes and marketplaces’ earnings, both in equities and other markets, and is a large determinant of liquidity.

The potential negative effects of certain types of algorithmic trading are widely discussed in media, among market participants, regulators and other stakeholders. Some algorithmic trading is beneficial and adds to liquidity, but some harmful variants can reduce liquidity. Does it increase market volatility, amplify short-term market reactions and in the long run cause traditional investors to pull out from these markets?

As trading patterns change and trading techniques become increasingly sophisticated, our tools for measuring market quality also need to be upgraded.

One of the key functional areas of Scila Surveillance is the detailed analysis of market quality. To get a more accurate picture of liquidity, simple measurements such as best bid/ask spread can be complemented by more sophisticated measurements, such as: liquidity measures over time, replenishment times after liquidity events, average order lifetime, etc.

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