Man vs machine: Who won on Brexit?

LONDON: As hedge fund manager Buford Scott sat at home, watching the TV in shock as it emerged Britain had voted to leave the European Union, his computer-based trading models were quietly boosting his business by 1.5%.

Scott’s algorithm-driven fund, and others like it, beat hedge funds run by humans on Brexit night, not because the computers had correctly predicted the results of the June 23 referendum, but because they followed trends already in place.

Computer-driven funds placed bets on safe-haven assets such as the yen and gold, which had performed well in the six months leading up to the vote, against riskier currencies such as the Mexican peso — a play yielding a more than a 7% return on June 24.

Hedge funds on average were down 0.18% on the day of June 24, while trend-following machine-based strategies gained 0.71%, according to data from the industry tracker Hedge Fund Research (HFR).

Some of the biggest hedge funds’ computer-run strategies — also known as systematic or quantitative — made huge gains in the aftermath of the Brexit vote.

UK-based Winton Capital, which manages $30 billion, gained 3.1% on June 24, while a $13.3-billion fund managed by Connecticut-based AQR returned 5.2% — about $700 million.

Polls had shown the Leave and Remain camps neck-and-neck before the referendum. But driven mainly by the view that undecided voters tend to opt for the status quo, bookmakers and betting markets had largely predicted Remain would win. Most players in the financial markets agreed.

“Almost no one was betting on the actual outcome of Brexit,” said GAM portfolio manager Anthony Lawler in London, a hedge fund investor whose firm recently bought the systematic investment manager Cantab Capital Partners, which made 4.1% in the year to June 27.

“Man chose to reduce risk given the unknown. Machines chose to be long in safe-haven assets because that trend was already in place. Machines won.”

Scott, the managing partner of Stelrox Capital, who was watching the referendum from his study in Wimbledon, said his models had put on a small short sterling position, which brought some profit — the pound tumbled as much as 12% on June 24 to a 31-year low.

But it was his short positions on emerging currencies such as the South African rand and Mexican peso, which also tanked as investors fled to safety, and his longs on the low-risk dollar and yen, that brought the biggest returns.

“The risk environment was obviously high. ... We hope we design our models so that these big, unexpected events are catered for, and we do that by diversifying across scores of currency pairs.”