The stock market plummeted in late October, causing both the Nasdaq and benchmark S&P 500 to experience corrections at the end of the month. This marks the 36th U.S. market correction since 1980.
While investors and market experts differ in their opinions regarding what contributed to such a volatile October, some say quantitative (“quant”) fund managers and machines are to blame, citing rising bond yields followed by a stock market selloff.
According to a quote from David Lafferty, chief market strategist at Natixis Investment Managers, published in a recent Reuters article:
‘When there’s $1 trillion in mitigating the downside, there’s a good argument to be made that it actually creates more downside because now they’re all selling at the same time. When everyone does it, they create the problem they’re trying to avoid.’
The October ‘flash crash’ is hardly the first-time mechanical models and algorithms have been blamed for financial downturn. We’ve seen before what can happen when machines are left to make trading decisions alone, and how an unexpected event can trigger a chain reaction.
In August 2007, just months before the global financial crisis, some of the largest firms in the hedge fund industry suffered massive losses – known today as the Quant Quake. And more recently over the past few years, there have been several flash crashes.
Despite these warning signs about relying on “black-box technology” and algorithms to manage money, assets being managed by quant hedge funds continue to grow – surpassing the $1 trillion mark this year. As the rapid adoption of artificial intelligence (AI) and machine-learning technologies continues to permeate across industries, it’s clear data-driven investment strategies have a place in the future of finance.
In the meantime, it’s important to learn from the events of the past, and develop the tools and strategies to mitigate this risk. In the wake of the latest crash, the most prudent investors are transitioning to less aggressive sectors to mitigate risk. By applying a similar, thoughtful and measured approach to algorithmic decision-making and quantitative strategies, funds will benefit from a strategy that combines the best of both worlds: human and machine.
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