Jack Farley

The HF game used to be simple. Find the nugget the sell-side mismodeled, put on the trade, wait for the revisions and the long-onlys. You knew how being right fundametnally got you paid. Then came alt data, and the game moved up a level. You didn’t have to wait for the company to report. You could watch spending, shipments and foot traffic to see the quarter taking shape. For a while, having the dataset first was a very good business. But over time, the advantage became harder to hold onto. More data, better tools, more people digging into smaller pieces of the market. Funds understood companies better than ever. And somehow it got harder to make money from that. I think that comes back to a first principle of making money in the market: someone has to follow your trade. And if you look around the table, the players have changed: - ETFs grew. More money buying themes, styles and characteristics rather than company fundamentals. In cap-weighted funds, yesterday’s winners became a bigger part of what the next dollar bought. - Pods multiplied. More teams with similar data, tools and risk constraints, getting better at finding the same nugget and putting on the same trade. - Long-onlys rotated. Capital rotated from value into growth, creating a split: in sectors losing capital, even strong quarterly results could struggle to bring those buyers back. In sectors getting the flows, being fundamentally right came with an extra tailwind. - Retail grew. Some through ETFs, some through individual names, bringing another pool of money responding to stories and price action rather than the line item you had figured out. I saw it firsthand in financials in the mid-2010s. Energy had a similar problem. Long-only capital was leaving. Pods were still doing the work. You could catch the revision and still be waiting for a buyer who had left. Those names became “hedge fund hotels”: everyone knew the company and management well, had similar models, and obsessed over the same one or two line items where they thought the alpha was. Everyone was playing the same quarterly catalyst, but often, even when you were fundamentally right, there just wasn’t anyone who cared enough to follow. Then the same dynamic hit sectors with a lot of alt data. The apparent alpha attracted more people, while providers increasingly sold both the data and the analysis. Everyone had roughly the same read on the quarter, and it got into positioning earlier. Being early used to mean at least the other HFs would follow your trade. Now they were already in, and the long-onlys weren’t playing the quarterly game. Nailing the print didn’t necessarily mean a big payday. But missing it when everyone was already on the same side could mean a big loss. Through a lot of that period, tech and healthcare generally worked better. Tech had fundamental winners with growth allocations and ETF flows behind them. Healthcare, especially biotech, had events that could force a reprice. Being right could still pull in new capital. But the same thing that made tech work so well also made the trade more crowded than ever, especially as the AI trade took hold. Money was coming in from almost every direction: retail, ETFs, long-onlys, trend followers and fundamental hedge funds. This time, the hotel wasn’t just full of HFs waiting on the same quarter. Pretty much everyone had checked in. You can see the pattern in the table below. Different teams, different sectors, all that company-level work. Yet when momentum moved sharply in the selected months, a lot of the pod shops’ P&L moved with it. (Citadel does seem to hold up better on the downside.) So why do pods often move first when the trade turns? Their business model runs on leverage. Tight drawdown limits mean they can’t sit through the same losses (-2% is a lot!). Those earlier sector unwinds were mostly hurting other pods. But this time, the same names sit in far more portfolios. If pod deleveraging pushes prices down enough, it can trigger selling from single-manager funds, quants, (levered) retail and other holders too. How far it spreads depends on each group’s risk model and how much pain it can take. It’s a different game now. The hard part is spotting the break when, most of the time, following the crowd pays. Can AI help, or does it just make everyone better at finding the same trade? I have a few thoughts, but this post is already long enough, so more next time…