(TND) — Artificial intelligence is fast becoming a part of our lives, and so are stock traders on Wall Street.
As with other AI applications, there are potential benefits and potential risks.
The more traders rely on computers to make their decisions, the more they all buy or sell based on the same analysis and the same underlying programs, one expert said.
Too much uniformity within the market is dangerous, said Pawan Jain, an assistant professor of finance at West Virginia University.
“If they are all on the buy side and no one is trying to sell, the market will collapse,” Jain said.
Jain has been involved with algorithmic trading for years and said a transition to a purely computer-based form of trading is “inevitable”.
That’s because these technological tools, including AI, are useful and profitable for stock traders, he said.
AI can deprive the market of analytics diversity, but that’s not the only danger, Jain said.
Traders used technology to execute trades at lightning speed.
Trading is now done in nanoseconds.
“This speed definitely increases the magnitude of casualties,” he said.
AI-powered tools create volatility in the market, and a more volatile market means a higher risk of a crash, Jain said.
He claims investors are missing out on real prices because computerized trading is so fast that stock prices can’t keep up.
The potential impact of AI on financial markets is enormous: assets worth over a trillion dollars change hands every day.
Institutional investors began using computer programs in the early 1980s to complete large deals quickly and efficiently.
“Program trading” became more sophisticated and popular before leading to the 1987 Black Monday stock market crash, Jain said.
According to Jain, regulators have responded with measures to limit the use of program trading, including circuit breakers stopping trading when there are significant market volatility.
But program trading was becoming increasingly popular. Then high frequency trading, which started in the early 2000s, became the next big technological development.
Jain said that got us to a point where stock traders could use AI algorithms to analyze big data in ways humans couldn’t.
Traders using these tools typically buy and sell assets at prices very close to the market price, which means they don’t charge investors hefty fees, Jain said.
That’s one of the advantages.
And it helps ensure there are always buyers and sellers in the market, Jain said.
The technology can reduce the impact of market inefficiencies, he said.
However, Jain cautioned that these AI-powered tools can react so quickly to market signals, even small ones, that they can cause sudden spikes or falls in asset prices.
He said the 2010 “flash crash” was a cautionary tale. Approximately $1 trillion in market value was erased and restored in minutes.
Herd mentality in stock trading can be dangerous, and ChatGPT-powered trading algorithms and similar programs have the potential “to make it worse,” according to Jain.
In general, there are concerns about bias and factual errors in AI systems. Financial markets are not immune, Jain said.
A computer retailer might even have an incentive to try and clog a competitor’s system with inaccurate information or fake orders to gain a small speed advantage, Jain said.
And public companies trying to cater to high-frequency traders have tricks to posting bad news, making it harder for the AI-powered computers to read, he said.
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