Quant was once considered to be the ‘Holy Grail’ in wholesale finance. Billions of dollars were poured into this exciting frontier for investing. Anything from hardware to mathematicians to programmers was hired to create what the financial industry thought to be their golden goose that will automatically continue to lay the proverbial ‘Golden Egg.’
During the last decade, rules-based traders made billions for their clients with their black-box trading algorithms and practical insights into human behavior. This altered the stock-trading game and retired the once envied stock-picking seers. However, there is a severe doubt plaguing the industry because black-box algorithms are misfiring. Competition is getting more challenging in everything from artificial intelligence and emerging tech to fees.
Added to that, Quants is on everyone’s radar now as the magic wand. So, anyone from Apple to Walmart and Disney is competing for the same talent that the financial industry had a monopoly over. Added to the mix is the fact that Quant is no longer expected only to provide technical solutions. Industries and verticals are now looking at the techniques with a much larger focus and are attempting to build a universal tool-set to solve original and novel problems. They are also trying to retain the built-in flexibility into models and integrate further into new businesses.
Quants are also expected to adapt their skillset to assimilate with the rest of the business talent to understand the bigger picture. With more varied applications and more significant impacts, there are no magic boxes anymore. The quants are now required to explain and defend their work as corporate quant awareness grows. The fluidity of markets indeed provides new ways of trading. With new financial products emerging, it is only with a thorough understanding of the whole ecosystem that a quant will stay ahead.
After the 2008 financial crisis, many more regulations and watchdog agencies have been introduced to keep the industry in check. The tightening of the money noose just became universal with the introduction of the 2021 AML Act. Regulation and oversight always add huge overheads and pressure to banks. The laws are particularly stringent for trading desks, and there are no signs that this will change anytime soon. In many ways, many agree that the new regulation is the natural result of quantitative finance’s continued maturation as an industry and practice.
On the other hand, those who feel that the regulations are far too onerous and, in the long term, will start damaging the bank’s profitability and operational overhead.
According to Jesper Andreasen, Head of Quantitative Research Department at Dankse Bank, on current regulation says, “There are elements that are useful, the whole credit risk in the system, for instance, but I’m not so sure everything is.”
Per a recent survey, the Chief Risk Officers of top-tier banks also have expressed their frustration. Another CRO stated, “The burden of regulation is now monumental and potentially having unintended adverse consequences. While much of the regulation was proven to be necessary due to poor industry self-regulation, the overload of new regulations and their specific implementation can impact the profitability of banks that is necessary for the strong, well-capitalized banking industry.”
As for the quant community’s assessment, some see the current changes in rules as a mere “patch” for valuation theory held accountable for effects and costs neglected before.
As it stands, the quant industry is going through a harsh winter. Over the past year, quant mutual funds have largely failed to beat the US stock market. According to Nomura research, only 15 percent of them could compete, a performance that lagged even behind traditional stock-picking funds that Quant was set to replace.
Many prognosticators have provided explanations for the lackluster performance. Some have blamed eroding popularity. Others have pointed at quant strategies’ effectiveness. But, the consensus remains that the problems are more than just a one-off event.
According to some analysts, the picture is much more complex than previously admitted. To some, the problem is that financial securities are becoming progressively difficult. The reasons mentioned include a change in structure, valuation, and hazard components. What’s more, the analysis of complex factors is a dubious work, but the fact remains that there is still a great deal of interest for experts who can chip away.
Simultaneously, the next big hope that the C-suite executives and academia have is emerging tech, particularly AI and Machine learning. According to Campbell Harvey, Professor of Finance at Duke University, “The pace of technological change and advances in machine learning and quantitative methods will result in a “shake out” in investment management.” “The future of finance will be much more quantitative than it is today. We are moving much more in that direction, whether it is systematic or discretionary trading. Machine learning is here to stay,” he added.
More assuringly, Alfred Spector, the Chief Technology Officer of Two Sigma Investments, put it this way:
“First, I think we’ll see quantitative approaches—meaning those grounded in advanced data science and systematic techniques—play a growing role in private market investing. We’ve all seen how these approaches can be effectively applied to investing in publicly traded securities. A next logical step is to extend this methodology to data-rich areas like private equity and venture capital.”
Cliff Asness, Managing and Founding Principal at AQR Capital Management, put the current crisis also in very favorable terms saying, “Many types of quantitative factor-based investing strategies, particularly the value factor, have had recent tough times, leading some to question if it is “broken.” A common critique argues that value no longer works as too darn many people know about it. But simple knowledge doesn’t kill a strategy. Real things, like becoming too expensive—yes, perhaps because of this widespread knowledge—can.”
The jury is still out on what caused the recent failures of quant funds, but one thing is crystal clear, both the academics and the D-suite folks are committed to the technology and the cost and benefit equation of the quant operations. The only question that remains now is how heavily they will invest in new technologies like AI and machine learning and how expensive competent talent will become.
This article was based on collected interviews by Bloomberg.