CAT, also known as Rule 613, was introduced by the Securities and Exchange Commission (SEC) in 2015 to create a comprehensive database that tracks equity and option securities that are trading through the US capital markets as part of the national market systems (NMS). The data will be used by regulators in order to effectively survey the market and allow them to focus their reconstruction efforts most effectively.
The task of reporting is obviously a mammoth one. When asked what types of specific information that it is critical to include when reporting this data, senior policy advisor at the SEC Mark Donohue said that it is a question with practical elements, cost elements and use elements.
From the SEC perspective, he said, one of the major uses if market construction and understanding what happened during times of most stress by recreating the data.
“If we think about the data elements that are required to be normalised so that a routine surveillance can be run, that is not necessarily the exact same data elements are necessary to be effectively recreated for projection,” he said. “Rejection messages are very helpful to understand market reconstruction, but may not be as advantageous from a surveillance standpoint.
He added: “We need to keep in mind the market reconstruction component and policy components as well.”
Donohue continued to say that he can conceive a future where data elements get added to CAT so that policy decisions can be made on a more informed basis. Additional elements can point you in different directions, he concluded.
The technology itself
From the perspective of the overall architecture of the database, one of the issues around using machine learning or pattern is that machines are very good at picking up patters, but struggle to make distinctions of what is actually relevant, an area where humans still maintain an edge.
Gregg Berman, director of market analytics and regulatory structure at Citadel, who moderated the panel, asked what types of tools and technologies the CAT will be able to provide to the industry to ensure that that the database is correct, and ensure that if you are running machine in order to look for a pattern it is at least possible to know that it is coming up with analysis based on accurate data.
“There is actually a fair amount of tech built into the system itself to ensure the authenticity of the data that we are seeing,” said Shane Swanson chief compliance officer of Thesys CAT – the plan processor of the project, who is overseeing the build and operation of the system itself.
"There is a fair amount of tech built into the system itself to ensure the authenticity of the data"
The authenticity process of knowing that you are getting data from who you think you are getting it from is a very important aspect.
“We store all the data in as raw a recorded format as possible, then we do the internal transformation and make it into an internal binary that can actually be recorded in an efficient manner,” he said.
If there is any ever question from a CAT reporter during the reporting phase when a firm raises an issue with the data, then we can go back to the actual raw level data that was actually received, do a comparison and make sure that it marries with the actual data that was submitted, he continued.
“There is always that capability. Obviously in terms of error corrections and reporting that data is also presented back to the firm so that they can make all those changes if there is a legitimate error.”
From the compliance perspective, Joanna Fields chief executive officer and founder of Aplomb Strategies suggested that spends most of her time dealing with data integrity issues at clients that are trying to deal with CAT.
“My suggestions to firms are pretty simple,” she said. “Hire a new trendy data office, someone that can go in and make sure that if you do a million trades that they all feed into your surveillance systems properly.”
“If you trade a million shares of IBM, then you should have that number of shares in your surveillance system and you should be able to deal with things like duplication and making sure that your data is actually complete,” she added.
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