Injecting artificial intelligence into the trading process could be just the thing to sooth equity investors' volatile nerves.
Concern over the impact of quantitative algorithms and high-frequency trading on equity market volatility — highlighted last week by comments from Treasury Secretary Steven Mnuchin — has led some sources to view AI methods like machine learning and robotic process automation as a possible counterweight to volatility. They cite the promise of AI as a way to automatically incorporate past behavior into current activity, potentially averting some major market swings.
"I expect the AI systems will likely 'smooth' the market, rather than aggravate it," said Roman Ginis, CEO of Imperative Execution Inc., New York, which launched the AI-assisted dark pool IntelligentCross earlier this year. Mr. Ginis said he expects AI to "reduce volatility in the same way as automatic flight control systems can keep the planes more stable during turbulence than when human pilots were involved."
Mr. Mnuchin in a Dec. 18 interview with Bloomberg News said he felt "market structure has led to a lot more volatility," citing high-frequency trading as a partial cause. HFT, using quantitative algorithms to enhance trading strategies, has been blamed for extreme market stress in instances including the Flash Crash of 2010, although volatility has been below its historical average in most of the years since the 2008-2009 financial crisis, according to the CBOE Volatility index.
And while sources contacted for this story disagreed with Mr. Mnuchin's assessment, they did say there is some promise that AI could reduce volatility.