It’s a good time to be a quantitative analyst, or a ‘quant,’ right now. The hunger for combined expertise in math, finance, and computer programming is voracious, because it’s being equated with better and more certain money returns.
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Wall Street definitely thinks so: According to the Wall Street Journal, 27.1% of all stock trading by investors today is done by quant funds.
The most-desired human of the future, the quant, is taking over—whether in the front office of an investment bank or a fund, where he or she supplies traders with trading and pricing models and trading strategies, or in the back office, where they analyze and validate existing models and strategies and develop new ones.
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And, they command starting salaries of a quarter of a million dollars …
The quant trend began as part of a much wider automation drive across industries. We are relying on software for an ever-growing number of things in our everyday lives, so why should investment firms be any different?
Algorithmic trading and quantitative analysis are closely interconnected and interdependent and they have shaped the new face of trading. It’s a highly-automated face that relies on algorithms to sift through massive amounts of data and detect patterns and trends, and, ultimately, enable investment and trading decisions.
Today’s software can even anticipate what human traders will do and beat them to it. In fact, artificial intelligence, developed by quants, has been beating human traders pretty consistently over the last few years.
So, the good bit is this: quants and the programs they develop enable better decisions, considerably reducing risk with their predictive models, and thus ensuring attractive returns and profits.
Computers can even be programmed to mimic the trading and investing behavior of the stars of the industry, theoretically becoming capable of emulating their success. Basically, quants can theoretically develop software than can trade like George Soros or invest like Warren Buffet.
Hello Quant Fund
With such possibilities, the rise of the ‘quant fund’ is another phenomenon to be expected. Yet not all of these funds are created equal, and things have come to a point where quantitative models need to be used to sort the genuine quant funds from firms that use Excel for their predictive modeling.
The investment banking/fund management landscape is becoming increasingly complex and reliant on technology. Just look at these figures: between January and March 2017, investors poured $4.6 billion into quantitative strategies globally. During the same period, hedge funds saw outflows of $5.5 billion.
That’s all good for quants looking for a job but it’s not so good for the ‘regular’ human traders who are getting replaced by software.
But it’s not just the potential of losing your job that should have you concerned with the rise of the quants.
According to Bloomberg analysis, the growing popularity of quantitative analysis among traders and investors is actually undermining its strength.
What happens to every new strategy when too many traders start using it? Well, it stops working. And it’s happening with quantitative analysis, only faster than usual.
Bank of America Merrill Lynch says that its quant clients now use three times as many factors (chunks of predictive code) as they did 20 years ago. Normal as this is, it is eroding their returns and this, too, is happening very quickly.
A Credit Suisse report on over 5,000 managed futures funds has revealed that average annual returns over the past decade have fallen by more than half to 3.1%. What’s more telling, perhaps, is that last year was the worst one since 1995, with a decline of 6.8%.
For all the lower risk, more stable and higher returns, and overall appeal, quantitative analysis was behind two recent crises.
In 2007, the quant crash or ‘quant meltdown’ saw a lot of hedge funds suffer simultaneous drawdowns of between 30% and 50%–and some even more. Massive liquidations of securities led to hefty losses, in some cases double-digit, and all this was blamed on the superfast trades made possible by new software, developed by quants. Sometimes, even quants cannot predict events, the crash showed.
The Flash Crash of 2010 was also linked to quants and high-frequency trading. The flash crash highlighted what some may consider a non-too-comfortable truth: Algorithms have become an inseparable part of financial markets, with all sorts of implications, not all of them positive.
Bloomberg recently reported on concern in the financial industry that the currently low liquidity on the equity market could trigger bad decision-making among quants in a bid to boost dwindling returns. Such bad decisions could cause another crash, skeptics believe.
Wolf Richter also warned that the growing influence of quants could lead to unforeseeable and potentially disastrous events.
Here are the questions he asks: “But what happens to the markets when a few machines rather than millions of humans make more and more trading decisions? When too many of them use the same inputs and formulas by the same PhDs from the same schools?”
The answer, for now, is “Nobody knows.” quants might be the new rocket science of trading, but it could also be a power overload.
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