Goldman Sachs: Equity Long-Short and Quant Funds Both Fell Over 3% in October, a First on Record
nashnova research
Goldman data show fundamental long/short and systematic funds both lost over 3% in October — the first simultaneous drawdown of that scale since records began in 2016; the MSCI World index fell just 0.77%, exposing a collective strategy failure that is now forcing capital reallocation.
The index barely moved — why did professional funds get hit so hard?
The MSCI World index fell just 0.77% in October; the S&P 500 dropped only 0.87% — the broad market was nearly flat.
Yet fundamental long/short funds — managers picking individual stocks to buy and sell — lost 3.49%. Systematic long/short funds — algorithm-driven strategies — lost 3.60%.
This means → the losses did not come from a falling market. They came from the funds' own stock picks and model signals — alpha itself broke down.
Where exactly did the quant strategies blow up?
Quant funds posted a pure-alpha loss of -5.06% in October — the worst single-month drawdown in six years.
The core culprit: a violent momentum reversal. Goldman's flagship momentum basket swung from +27% in September to -18.6% in October.
In plain terms = the stocks that had rallied hardest turned into the biggest losers. The more aggressively the algorithms chased the trend, the worse the reversal hurt.
Profitability and growth factors provided partial offsets but fell far short. Year-to-date, quant strategies still hold a +16.2% total return.
Why did the biggest funds lose the most?
Asset-weighted returns came in at -3.49%, far worse than the equal-weighted average of -0.98% and the median of -1.69%.
This means → the largest funds bore the heaviest losses. They were most concentrated in AI longs and "AI-loser" shorts — when the trade reversed, the stampede was fiercest among the biggest books.
TMT-focused funds — heavy in tech, media, and telecom — lost 12.6% in a single month, wiping out three-quarters of the gains built up over Q3.
Roughly 35%–40% of long/short funds are now flat or underwater year-to-date. The hedge-fund industry overall has cut AI-related exposure to a net underweight for the first time since early 2023.
After the losses, how are funds repositioning?
October saw the largest global net selling since March, with a long-sell to short-cover ratio of roughly 4:1 — funds were overwhelmingly cutting long positions.
Information technology, industrials, and materials were the most sold sectors globally; consumer discretionary, real estate, and healthcare saw net buying.
At the same time, funds were net buyers of macro products — index futures and ETFs combined — at a 1.6:1 long-buy to short-sell ratio.
In plain terms = the playbook is clear: dump single stocks, buy the index. Funds slashed the individual longs that had blown up while adding index exposure to keep overall net-long levels from collapsing too fast.
What does this data set signal for markets ahead?
Two strategy types failing in unison + a crowded stampede among the largest funds + the worst quant-momentum drawdown in six years — all three appearing in the same month is unprecedented.
This reflects a market where positioning has become dangerously homogeneous: when a critical mass of funds bet the same direction (AI longs), a reversal delivers damage far beyond what the index alone would suggest.
October's data becomes a key reference point for tracking where capital flows next — whether funds continue to cut AI exposure and rotate into defensives, or reload after the pullback, will define the market's next dominant theme.
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