The former head of equity strategy at Brevan Howard on how to survive as an analyst
Hedge funds have long been an escape route for sell-side analysts, economists and strategists looking for a way out of shrinking investment banks’ research desks, but even here they’re becoming endangered.
Hedge funds are embracing artificial intelligence, they’re hiring big data specialists and training algorithms to think like the best human traders. If they want to survive in hedge funds, human analysts have to change, says the former lead equity strategist at Brevan Howard Asset Management.
“Working in a hedge fund is naturally premised on an egotistical belief that you have a better insight than other actors in the market – that you are the ‘smart’ money,” says Amit Khanna, who worked for nearly ten years as lead equity strategist at Brevan Howard Asset Management. “However, it would be naïve to not also realise the world has become a lot more complex.”
Khanna says analysts' future success will be about using the ‘quantimental’ approach of blending discretionary judgement with insights from statistics. Understanding the “idiosyncratic narrative” of a stock together with the macro backdrop is important if you want to endure, he says. If artificial intelligence is really going to gain any traction in asset management, it needs to be combined with traditional expertise.
“I think the aspect of domain expertise is under-appreciated,” he says. “A robust process for selecting, analysing and presenting data and then converting to actionable trades is paramount. It requires experienced market practitioners to design the algorithms alongside the PhD data scientists.”
Khanna has just left Brevan Howard to join Quant Insight, the start-up that uses AI for securities analysis. It was founded in 2014 by former Brevan partner and macro portfolio manager Krishnan Sadasivam and Mahmood Noorani, who worked at UBS, Bluecrest Capital Management and Millennium Capital Partners, but has just kicked off its product this year.
Hedge funds are still working out the best ways to handle the huge new datasets they’re increasingly buying in from third parties. Having, say, satellite crop images or credit card receipts is one thing, but turning that data into actionable trades that generate alpha is the main challenge.
“Funds are hastily investing in data scientists, expanding their quant capabilities as they are forced to adapt and show their clients they are adapting, but it remains a learning curve for most," says Khanna. “Identifying the right ingredients to then concoct the best recipe requires domain expertise with world class machine learning or AI expertise.”
Quant Insight says that it’s aiming to bring AI expertise to the masses – beyond the confines of the top computer-driven hedge funds who employ legions of PhDs. But the firm is still placing traditional financial services expertise at the heart of its business. Its quants and programmers are based in India, and it’s tapped astrophysicists at Cambridge University for machine learning expertise. Most of its London-based staff come from top hedge funds and investment banks, because it believes that this expertise helps make sense of the numbers.
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