AI computing power trading volatility soared to 36%! Haitong Securities recommends jumping out of the crowded AI track and turning to "experience consumption, high-quality compound interest, and mergers and acquisitions alpha."

At a time when the global AI computing power infrastructure-related theme trading has seen severe volatility and a sharp pullback, sparking renewed interest in other sectors in the market, Wall Street financial giant Goldman Sachs Group, Inc. (Goldman Sachs) and its counterparts have adopted a similar new strategy - actively urging investors to look beyond technology stocks related to artificial intelligence. In recent days, the South Korean stock market, known as the "AI computing power indicator," has frequently experienced circuit breakers due to sharp rises and falls, with the Philadelphia Semiconductor Index in the US plummeting more than 20% from its June high, entering a technical bear market. This, coupled with extreme selling pressure in global AI computing power theme stocks and the semiconductor sector due to overcrowding and highly leveraged long positions, has led to a shift in global stock market investment sentiment towards high-quality fundamental cyclical sectors and defensive stocks with strong balance sheets and ample cash flow, which have not seen as much gains as popular technology stocks this year. A team led by Ben Snyder, senior stock strategist at Goldman Sachs Group, Inc., stated in a report released on Friday that the volatility of popular AI computing power infrastructure momentum trading has reached its highest historical level, except during economic recessions, even as the equal-weighted version of the S&P 500 index continues to set new highs. The Goldman Sachs Group, Inc. strategy team believes that the correlation among stocks within the market has dropped to historically low levels, keeping overall index volatility low, indicating significant internal market volatility. Global stock markets, and indeed broad-based financial markets globally, are facing a complex super stress test of "escalating tensions in the Middle East for GEO Group Inc.+return of energy inflation+A momentum deleveraging in the AI computing power theme": the Philadelphia Semiconductor Index has fallen over 20% from its June high, entering a technical bear market; the South Korean stock market has become the epicenter of the global AI computing power trading reversal due to excessive weight of memory chip stocks, daily rebalancing of single-stock leveraged ETFs, and concentration of retail financing positions. From Seoul's chip deleveraging frenzy to Wall Street's search for a "safe haven" through quantitative models, stocks that continue to have strong cash flow, a strong balance sheet, and have long trailed the AI theme are now deservedly taking the center stage in the stock market - such as consumer electronics giant Apple Inc. (AAPL.US), considered a "laggard in AI," skyrocketing in stock price amidst the AI computing power theme crash and reclaiming the title of "world's most valuable company." The escalating political risk in the Middle East for GEO Group Inc. no doubt makes this earnings stress test for tech giants even more complex. The re-escalation of US-Iran conflict has slowed down traffic in the Strait of Hormuz, with Brent crude oil briefly surpassing $90; the strait carries about 20% of global energy transport, and continued closure will reopen energy inflation, put pressure on corporate profit margins, and recalibrate the pricing path for the Fed to be higher and longer. The most reasonable market framework at present may not be simply judging whether the "AI super bull market is over" or blindly bottom-fishing after a crash, but entering a stage where the index appears volatile on the surface, internal dispersion is high, and cash flow regains pricing power. In the short term, it is advisable to reduce exposure to single-stock leveraged ETFs, short-term bullish options, and high momentum positions that rely solely on valuation expansion, and hedge against oil price and interest rate risks with high-quality balance sheets, defensive cash flow, energy, and some undervalued cyclical assets. AI momentum tremors, funds tacitly seeking the "second battlefield": Goldman Sachs Group, Inc. focuses on experiential consumer, high-quality compound interest stocks, and M&A targets. The firm says its long-short momentum factor has a high exposure to AI infrastructure companies, focusing on semiconductor and high-end technology hardware stocks; over the past three months, the factor has seen an annualized volatility of 36%. Goldman Sachs Group, Inc. expects this trading strategy to face significant challenges in the short term, as hedge fund leveraged positions remain high, and with North American tech giants unlikely to significantly raise capital expenditure guidance again this quarter, after a cumulative upward revision of approximately $100 billion in the previous quarter. In contrast, Goldman Sachs Group, Inc. emphasizes three themes with limited correlation to AI-related stocks and the potential to provide attractive investment opportunities with alpha: experience-driven consumer companies, high-quality "compound growth enterprises," and potential large M&A targets. Goldman Sachs Group, Inc.'s three alternative themes essentially shift the pricing anchor from "AI capital expenditure beta" to "real demand, cash flow compounding, and event-driven alpha," reducing factor correlation through experience-driven consumer spending, discounted high-quality stocks, and potential M&A targets. The truly scarce asset in the next phase will not be the one "related to AI," but rather those that can deliver profits and cash flow without continuous leverage. AI computing power infrastructure trading has transitioned from a one-sided momentum trend to high volatility, deleveraging, and capital return validation stage. Portfolios need to reduce their reliance on semiconductor and tech hardware as a single factor. The long-short momentum factor, highly exposed to the AI computing power value chain, has seen its annualized volatility rise to 36% over the past three months, reaching rare levels outside of recession periods; hedge fund exposure remains high, and with the massive tech companies having cumulatively raised capital expenditure guidance by approximately $1 trillion last quarter, the likelihood of a similar scale increase again this earnings season is limited. Stock correlations are at extremely low levels, allowing the equal-weighted S&P 500 index to continue setting new highs, indicating that the current phase is closer to an internal clearing of AI crowded trades than a collapse in overall U.S. stock market demand. The micro mechanisms behind this adjustment have been revealed in the South Korean and American semiconductor markets: the Philadelphia Semiconductor Index has fallen over 20% from its June peak, entering a technical bear market; the South Korean KOSPI index also fell over 20% from its record high, with Samsung Electronics and SK Hynix's high index weightings combined with single-stock leveraged ETFs, margin financing, and trend fund concentration, leading to rapid conversion of fundamental profit doubts into mechanical rebalancing, stop-loss, and forced selling. As a result, Korean regulatory authorities have paused the listing of new products and raised the minimum margin for retail participation in single-stock leveraged ETFs to 30 million South Korean won. This shows that global funds are not completely denying the demand for AI computing power, but rather reducing the risk exposure to high leverage, high momentum, high valuation, and continuous acceleration of capital expenditure reliance. Goldman Sachs Group, Inc.'s three alternative themes - experiential consumer, high-quality compound interest, and M&A alpha - essentially shift the pricing anchor from "AI capital expenditure beta" to "real demand, cash flow compounding, and event-driven alpha," reducing factor correlation through experiential consumer spending, discounted high-quality stocks, and potential M&A targets. The truly scarce asset in the next phase will not be "AI-related," but rather those that can deliver profits and cash flow without continuous leverage. Barclays PLC Sponsored ADR's long-term tracking of eight style indices shows that buying quality stocks as an investment strategy has performed best in the past two months. As concerns mount among investors in the stock market about theme trading around AI computing power and a wide range of popular semiconductor stocks, neglected quantitatively secure trading strategies are making a strong comeback. Last year, Wall Street institutional investors obsessed with AI-themed trading essentially abandoned undervalued stocks with strong fundamentals, leading to a valuation indicator plunging to near historically low levels. Now, with concerns about overcrowded AI positions and the Fed's interest rate path intensifying, this investment style is regaining momentum. AI computing power infrastructure trading has transitioned from a one-sided momentum trend to high volatility, deleveraging, and capital return validation stage, leading portfolios to reduce their reliance on semiconductor and tech hardware as a single factor. The long-short momentum factor, highly exposed to the AI computing power value chain, has seen an annualized volatility of 36% over the past three months, reaching rare levels outside of recession periods; hedge fund exposure remains high, and with North American tech giants having cumulatively raised capital expenditure guidance by approximately $1 trillion last quarter, the likelihood of a similar scale increase again this earnings season is limited. Stock correlations are at extremely low levels, allowing the equal-weighted S&P 500 index to continue setting new highs, indicating that the current phase is closer to an internal clearing of AI crowded trades and market breadth expansion, rather than a collapse in overall demand in the U.S. stock market. Goldman Sachs Group, Inc.'s three alternative themes - experiential consumer, high-quality compound interest, and M&A alpha - essentially shift the pricing anchor from "AI capital expenditure beta" to "real demand, cash flow compounding, and event-driven alpha," reducing factor correlation through experiential consumer spending, discounted high-quality stocks, and potential M&A targets - the next truly scarce asset will not be "AI-related," but rather those that can deliver profits and cash flow without continuous leverage. The firm says its long-short momentum factor has a high exposure to AI infrastructure companies, focusing on semiconductor and high-end technology hardware stocks; over the past three months, the factor has seen an annualized volatility of 36%. Goldman Sachs Group, Inc. expects this trading strategy to face significant challenges in the short term, as hedge fund leveraged positions remain high, and with North American tech giants unlikely to significantly raise capital expenditure guidance again this quarter, after a cumulative upward revision of approximately $1 billion last quarter. In contrast, Goldman Sachs Group, Inc. emphasizes three themes with limited correlation to AI-related stocks and the potential to provide attractive investment opportunities with alpha: experience-driven consumer companies, high-quality "compound growth enterprises," and potential large M&A targets. Goldman Sachs Group, Inc.'s three alternative themes essentially shift the pricing anchor from "AI capital expenditure beta" to "real demand, cash flow compounding, and event-driven alpha," reducing factor correlation through experience-driven consumer spending, discounted high-quality stocks, and potential M&A targets. The truly scarce asset in the next phase will not be "AI-related," but rather those that can deliver profits and cash flow without continuous leverage. The firm says its long-short momentum factor has a high exposure to AI infrastructure companies, focusing on semiconductor and high-end technology hardware stocks; over the past three months, the factor has seen an annualized volatility of 36%. Goldman Sachs Group, Inc. expects this trading strategy to face significant challenges in the short term, as hedge fund leveraged positions remain high, and with North American tech giants unlikely to significantly raise capital expenditure guidance again this quarter, after a cumulative upward revision of approximately $1 trillion last quarter. In contrast, Goldman Sachs Group, Inc. emphasizes three themes with limited correlation to AI-related stocks and the potential to provide attractive investment opportunities with alpha: experience-driven consumer companies, high-quality "compound growth enterprises," and potential large M&A targets. Goldman Sachs Group, Inc.'s three alternative themes essentially shift the pricing anchor from "AI capital expenditure beta" to "real demand, cash flow compounding, and event-driven alpha," reducing factor correlation through experience-driven consumer spending, discounted high-quality stocks, and potential M&A targets. The truly scarce asset in the next phase will not be "AI-related," but rather those that can deliver profits and cash flow without continuous leverage. The firm says its long-short momentum factor has a high exposure to AI infrastructure companies, focusing on semiconductor and high-end technology hardware stocks; over the past three months, the factor has seen an annualized volatility of 36%. Goldman Sachs Group, Inc. expects this trading strategy to face significant challenges in the short term, as hedge fund leveraged positions remain high, and with North American tech giants unlikely to significantly raise capital expenditure guidance again this quarter, after a cumulative upward revision of approximately $1 trillion last quarter. In contrast, Goldman Sachs Group, Inc. emphasizes three themes with limited correlation to AI-related stocks and the potential to provide attractive investment opportunities with alpha: experience-driven consumer companies, high-quality "compound growth enterprises," and potential large M&A targets. Goldman Sachs Group, Inc.'s three alternative themes essentially shift the pricing anchor from "AI capital expenditure beta" to "real demand, cash flow compounding, and event-driven alpha," reducing factor correlation through experience-driven consumer spending, discounted high-quality stocks, and potential M&A targets. The truly scarce asset in the next phase will not be "AI-related," but rather those that can deliver profits and cash flow without continuous leverage. The firm says its long-short momentum factor has a high exposure to AI infrastructure companies, focusing on semiconductor and high-end technology hardware stocks; over the past three months, the factor has seen an annualized volatility of 36%. Goldman Sachs Group, Inc. expects this trading strategy to face significant challenges in the short term, as hedge fund leveraged positions remain high, and with North American tech giants unlikely to significantly raise capital expenditure guidance again this quarter, after a cumulative upward revision of approximately $1 trillion last quarter. In contrast, Goldman Sachs Group, Inc. emphasizes three themes with limited correlation to AI-related stocks and the potential to provide attractive investment opportunities with alpha: experience-driven consumer companies, high-quality "compound growth enterprises," and potential large M&A targets. Goldman Sachs Group, Inc.'s three alternative themes essentially shift the pricing anchor from "AI capital expenditure beta" to "real demand, cash flow compounding, and event-driven alpha," reducing factor correlation through experience-driven consumer spending, discounted high-quality stocks, and potential M&A targets. The truly scarce asset in the next phase will not be "AI-related," but rather those that can deliver profits and cash flow without continuous leverage. Barclays PLC Sponsored ADR's long-term tracking of eight style indices shows that buying quality stocks as an investment strategy has performed best in the past two months. As concerns mount among investors in the stock market about theme trading around AI computing power and a wide range of popular semiconductor stocks, neglected quantitatively secure trading strategies are making a strong comeback. Last year, Wall Street institutional investors obsessed with AI-themed trading essentially abandoned undervalued stocks with strong fundamentals, leading to a valuation indicator plummeting to near historically low levels. Now, with concerns about overcrowded AI positions and the Fed's interest rate path intensifying, this investment style is regaining momentum. AI computing power infrastructure trading has transitioned from a one-sided momentum trend to high volatility, deleveraging, and capital return validation stage, leading portfolios to reduce their reliance on semiconductor and tech hardware as a single factor. The long-short momentum factor, highly exposed to the AI computing power value chain, has seen an annualized volatility of 36% over the past three months, reaching rare levels outside of recession periods; hedge fund exposure remains high, and with North American tech giants having cumulatively raised capital expenditure guidance by approximately $1 trillion last quarter, the likelihood of a similar scale increase again this earnings season is limited. Stock correlations are at extremely low levels, allowing the equal-weighted S&P 500 index to continue setting new highs, indicating that the current phase is closer to an internal clearing of AI crowded trades and market breadth expansion, rather than a collapse in overall demand in the U.S. stock market. Goldman Sachs Group, Inc.'s three alternative themes - experiential consumer, high-quality compound interest, and M&A alpha - essentially shift the pricing anchor from "AI capital expenditure beta" to "real demand, cash flow compounding, and event-driven alpha," reducing factor correlation through experiential consumer spending, discounted high-quality stocks, and potential M&A targets - the next truly scarce asset will not be "AI-related," but rather those that can deliver profits and cash flow without continuous leverage.