What is sauce for the goose is sauce for the gander when it comes to implementing artificial intelligence in the capital markets industry insiders testified before the House Financial Services Committee’s Taskforce on Artificial Intelligence.
Taskforce chairman and ranking member Rep Bill Foster (IL-D) and Rep. Barry Loudermilk (GA-R) voiced bi-partisan concern that AI, and the big data which fuels it, could upend the capital markets eventually and lead to ever-consolidating markets.
Rep. Foster noted that consolidation and economies-of-scale are the natural byproducts of digital markets, and access to more considerable amounts of data provides fertile soil for market manipulation.
“For example, imagine what it would be worth if you had a 10-second early look at Trump’s Twitter feed,” he said. “Imagine how much money you could make off that.”
However, Nasdaq is using the same technology to detect such behavior within two seconds of it occurring, testified Martina Rejsjö, the head of Nasdaq Market Surveillance at Nasdaq.
“Our surveillance program uses algorithmic coding to detect unusual market behavior running more than 40 different algorithms in real-time utilizing more than 35,000 parameters,” she said.
Nasdaq also surveils approximately 150 post-trade patterns of potential misconduct as well.
The exchange operator uses AI to scour billions of transactions and quote data points, which it collects daily, for market-manipulation signals hidden amongst the ocean of data.
Rejsjö’s team also goes beyond merely looking for pre-defined expectations of how these signals would look.
“It can often limit the alert results depending on how the alert patterns are calibrated,” said Rejsjö. “Calibration also presents a continued challenge when determining the best balance between false positives and true alerts.”
As a result, Nasdaq Machine Intelligence Lab, Nasdaq Market Technology Business, and Nasdaq’s US surveillance team collaborated to enhance surveillance capabilities with the help of artificial intelligence to detect abnormal behavior patterns.
Meanwhile, fellow witness Marcos Lopez de Prado, professor of practice at Cornell University’s Engineering School and chief investment officer at True Positive Technologies, recommended that regulators turn to crowdsource and let the data-science community look for signs of market.
The practice would be similar to the Netflix Prize, in which the streaming site awarded $1 million to the team that provided the most significant improvement to the CineMatch recommendation algorithm, he said. “Regulators could anonymize transaction data and offer it to the worldwide community of data scientists who would be rewarded with a portion of the fines levied by regulators against the wrongdoers. The next time the financial markets experience something like the Flash Crash, this tournament approach could lead to faster identification of potential market manipulators.”