At the heart of the software are 100+ pre-programmed metrics representing fairness and efficiency, including dozens of academically tested metrics for transactions costs and volatility.
Notwithstanding the wide variety for pre-programmed metrics, MQD also has a python language that allows users to develop and populate their own metrics.
Also included as standard in the software is a list of all major market design changes in each marketplace and a visual front end that allows one to look at the impact of such changes on metrics for fairness and efficiency making it an “academic–in-a-box” when it comes to assessing the implications of market design change for market quality.
Metrics are available at the security level, a group of securities level, index level, a decile of securities level or a market wide level.
While the system comes with publically available data as a standard option, data can also be incorporated from more private sources such as that typically available to an exchange or regulator.
The software is cloud based allowing users to burst up or down depending upon the number of markets they need metrics for in a particular timeframe.
The very latest use of the software is by data owners such as exchanges. CMCRC is currently placing MQD alongside the private data in the private cloud of one of the world’s top three North American exchanges with a view to enhancing the use of its data by third party data users.
Unique to the modus operandi of the CMCRC is the ability to supply smart human capital alongside its technology. In this sense it is worth emphasising that new technology often takes a lot longer than anticipated to benefit a new user because the third party user is already operating at 99% capacity and because it takes time to learn the full capability of a new technology. To mitigate these problems CMCRC trains smart human capital in how the technology works and puts both onsite for a three-month evaluation period. The total cost is US$50k setup and US$25k per element of human capital.
At the end of the trial the expectation is that the on-going cost of the technology and human capital will be met in full and be a small fraction of the total returns from the sale of better data.