During the dotcom bubble, he kept updating a working paper – Bubble Logic: Or, How to Stop Worrying and Love the Bull – and went on to publish research papers with attitude, such as Stock Options and the Lying Liars Who Don’t Want to Expose Them. AQR’s strong performance makes his cases authoritative, but before Asness’ arguments stoked academic debate and gained the financial media’s attention, his contrarian streak emerged at The University of Chicago, where he undertook a PhD in finance in 1988. At this time, finance professors at Chicago were establishing their school as a stronghold of value investment thinking. When Asness arrived, a draft of Eugene Fama and Kenneth French’s momentous yet unassumingly titled paper, The Cross Section of Expected Stock Returns, began circulating throughout the campus. It found that, over time, value stocks persistently outperformed growth stocks more often than the efficientmarket hypothesis expected. They reached this conclusion after deep and rigorous testing of market data, and Asness observed this disciplined methodology while working as Fama’s research assistant.
“They are real believers in sweat equity: building great datasets that no-one else has, looking at data in different ways – even if someone’s already looked at it – and coming up with interesting observations,” he says. “They’re great theorists, too, but are certainly believers in proving your assertions through data.” This instilled a “tremendous, tremendous respect for data” in the finance PhD student. It was strong enough to overcome any peer pressure to write a characteristic Chicago dissertation championing value investing, since Asness decided to focus on stock price momentum as a profitable investment strategy. He was essentially saying that Wall Street firms, which didn’t pay enough attention to value strategies, were onto something. “It was a pretty large departure from University of Chicago orthodoxy, so just by choosing that topic I guess I was already going in a different direction. “I was very nervous going up to Gene Fama. I told him what I wanted to write, and all he said was: ‘If you have the data, write the paper’.”
Asness finished the paper in the year after he left Chicago, writing at night after working by day on the Goldman Sachs Asset Management (GSAM) fixedincome desk. During this time, his plan to pursue dual careers in academia and investment consulting were sidelined after he sized up the money-making opportunities in funds management. For this, Asness is thankful: even if academia offered more than half the money that funds management does, and he returned to the campus, it would have been a mistake, he says. Running quantitative funds enables him to test ideas and build models – like academics do – but also find out if they work, and engaging institutional clients is a proxy for teaching. He can, obviously, still write papers too. Before these days at GSAM, Asness did not know he was schooling up to become a quantitative funds manager. “I didn’t even know the word ‘quant’ until I went to Wall Street,” he says. Studies of whether markets are efficient, and the relation between risk and return, were part of financial economics.