After record hedge fund selling, Goldman Sachs recommends buying the dip: allocate to AI "selectively," but with limited confidence.

Title context: After record hedge fund selling, Goldman Sachs recommends buying the dip: allocate to AI "selectively," but with limited confidence. Concentrated hedge fund deleveraging and reduced crowding in the AI trade have created buying opportunities in some U.S. tech stocks, but Goldman Sachs remains cautious about a rebound. Its strategists recommend selectively allocating to AI assets, with the core rationale being that earnings expectations for leading companies have not yet been revised down; however, persistently swelling capital expenditures and still-unclear investment returns leave it uncertain whether this correction can become a good entry opportunity. This week, AI-related assets suffered a sharp selloff. Goldman Sachs' Hedge Fund VIP basket posted its worst single-day performance on Thursday since 2011, and Goldman's prime brokerage division also saw its largest tech stock selloff since September 2024. Meanwhile, the S&P 500 ex-AI constituents index outperformed the overall S&P 500 year-to-date for the first time, as the AI outperformance advantage that had persisted for nearly three years clearly weakened. Behind the selloff is the market beginning to re-examine the costs and returns of AI investment. Meta raised the lower bound of its 2026 capital expenditure guidance to $135 billion, free cash flow turned negative, and the stock fell 7% on Thursday. This shows that revenue beats are no longer enough to offset investors' concerns about continuously rising capital expenditures. Views within Goldman Sachs are also divided: position clearing may bring rebound opportunities, but whether AI demand growth can ultimately sufficient cash flow remains an unavoidable question. Hedge funds accelerate deleveraging, tech stocks see concentrated reductions Goldman Sachs Prime division data show that fundamental long/short fund managers' beta-adjusted net leverage fell 3.5 percentage points this week and has fallen 10.3 percentage points cumulatively since October, hitting a one-year low and sitting in the 14th percentile of the past three years' distribution. Over the past 8 trading days, hedge funds deleveraged on 7 days, with the pace of adjustment exceeding any period in all of 2025. Thursday's deleveraging ranked fifth for the year and was entirely driven by long selling. Fundamental long/short funds' net leverage ratio fell to 47%, in the 1st percentile of the three-year lookback range, indicating that funds are concentrating on cutting risk exposure. Goldman Sachs' Tony Pasquariello said in a Friday report that, based on client communications, this selloff has hit the market's core holdings, with Thursday's reduction in tech stock exposure setting the highest single-day record in the past two years. Fund flows also showed clear divergence. On Thursday, the Philadelphia Semiconductor Index fell 6.7%, while 337 S&P 500 constituents rose and the index as a whole fell only 1.2%. Meanwhile, the S&P 500 ex-AI constituents index rose 6.5% in October and is on track for its best monthly performance in nearly three years. AI stocks' advantage over the broader market is narrowing, and the market is no longer willing to pay a premium for AI-related assets indiscriminately. The AI trade has cooled sharply, but Goldman Sachs remains bullish on some leaders The retreat of the AI trade is particularly evident in the performance of Goldman Sachs' related portfolios. Goldman Sachs' AI and AI Risk Hedging portfolio (GSPUARTI) peaked at 583.45 points on June 22 and last quoted at 261.71 points, a drawdown of about 55% from the high. Goldman Sachs' High Beta Momentum portfolio (GSPRHIMO) has also given back all of its year-to-date gains, down about 43% from its June high and retesting its July low. The single-day performance of Goldman Sachs' AI Beneficiaries basket (GSTMTAIP) relative to the S&P 500 ex-AI constituents index (SPXXAI) fell to its worst level since the DeepSeek shock, and broad AI stocks have also fallen back to summer lows relative to the broader market. However, Goldman Sachs strategist Louis Miller believes that position adjustments and share price declines do not mean the AI investment thesis has become invalid. He offers three reasons for buying the dip: first, investors' momentum exposure has fallen to its lowest level since June 2025; second, the scale of this correction has been large, improving the risk-reward ratio of some assets; third, earnings expectations for AI leaders remain stable. Among these, earnings expectations are the most critical support. Goldman Sachs points out that earnings expectations for hyperscalers have not been revised down since July, and the data center equipment sector has shown no signs of slowing earnings growth. If earnings remain stable while share prices have already fallen sharply, some AI assets may have room to rebound. But position clearing does not mean risks have been fully released. Miller also acknowledges that although momentum exposure has declined, it remains in the 73rd percentile of the historical distribution, indicating that the related trades have not completely shed their crowded status. "Selectively" buying the dip, with focus on AI returns rather than computing power demand Goldman Sachs' recommendation is not broadly bullish on AI, but rather selective positioning while retaining hedges. On the one hand, the bank is bullish on rebound opportunities in some AI beneficiary stocks; on the other hand, it is wary that non-AI stocks have risen too quickly in the short term, and it is shifting investment focus to areas such as behind-the-meter power generation and self-built campus power generation, while avoiding cyclical and low-quality stocks. This strategy reflects Goldman Sachs' judgment that opportunities in the AI supply chain are diverging: position clearing and share price pullbacks have created entry opportunities, but that does not mean the entire sector has bottomed. Pasquariello admits he has "limited conviction" on market direction and notes that computing power demand (POWER) has a solution path, but the cost of capital (CAPITAL) problem still has no answer. As AI capital expenditures continue to expand, the market's focus is no longer just on whether cloud providers will continue to increase spending, but on whether these expenditures can sufficient revenue, profit, and free cash flow. Meta's stock reaction shows that even with strong business growth, valuations can still come under pressure if investment returns fail to keep pace with spending expansion. Historical performance also reminds investors to remain cautious. Goldman Sachs data show that after a 10% drawdown in momentum portfolios, the median return over the next three months is 16.4%; however, the rebound after the March 2026 low was ultimately fully given back, and after bottoming in July, the portfolio fell a further 15.4% over the following month. Next week, Microsoft and Alphabet will report earnings within hours of the Fed's rate decision, with Amazon following on Thursday. The results will further test the return capacity of AI investment. With the contradiction between capital expenditures and cash flow not yet alleviated, Goldman Sachs' strategy is to capture pullback opportunities in some AI assets rather than bet on a broad sector-wide rebound. This article is reprinted from "Wall Street See" (Wallstreetcn). GMTEight editor: Feng Qiuyi.