South Korea's AI trading shock triggers a global style switch! Amid the capital outflow, "quantitative safe trading" returns to Wall Street.

With the risks of selling surrounding AI computing power infrastructure and popular semiconductor stocks sweeping the global financial markets, the overlooked quant safety trading is making a strong comeback. In recent weeks, investors in the stock market have been increasingly worried about AI computing power themed trading, leading them to seek refuge in a more conservative corner of the stock market: companies with long-term stable financial conditions. The theme trading surrounding AI seems to have transitioned from a unilateral momentum phase of "continuous capital expenditure increase + loose liquidity" to a phase of re-evaluating valuation, return rate, and interest rate paths. As a result, quantitative funds are withdrawing from crowded high-beta winners and shifting towards the "quality factor," which consists of strong balance sheets, stable profits, and relatively lagging valuations. It is noted that quality strategies have seen a 3.8% increase in the past two months, continuing to gain positive returns amid the sell-off of chip stocks and the decline of the Nasdaq 100. This does not reflect investors' overall bearish sentiment towards the US stock market, but rather a shift in style from "chasing gains" to "selecting profit quality" within the market. Momentum factors in global stock markets have experienced a rare and drastic reversal since the early 21st century, but the overall market remains resilient, closer to fund rotation rather than a systematic bear market. The so-called Quant Safety Trade that has recently gained traction is not simply buying utilities, gold, or low-volatility ETFs, but rather using rule-based models to long "high-quality" companies and, if necessary, short low-quality companies to extract quality factor returns that are independent of market fluctuations. Common screening indicators include higher return on equity, stable profit growth, lower debt and financial leverage, healthy cash flow, and sustainable business models; index compiling companies like MSCI also define the quality factor as companies with low leverage, stable profits, and high profitability, pointing out that they typically exhibit defensive attributes during economic contractions. It is worth noting that "safety" refers to companies' ability to withstand economic slowdowns, tightening financing environments, and decelerated capital expenditure, and does not imply that stock prices will not fall. Quant Safety trading is fundamentally different from the momentum stocks that are currently being sold off: Momentum strategies rely on continuing to buy winners based on price strength over the past 6-12 months, essentially believing in trend continuation, typically without requiring companies to be cheap, have low debt, or ample cash flow. MSCI defines it as a "persistence factor" of recent strong stocks continuing to outperform. When AI semiconductors, data centers, and highly leveraged tech stocks become common holdings, momentum strategies can easily form a "price rise--fund inflow--more crowded position" positive feedback loop. However, once AI capital return expectations, interest rates, or risk preferences change, this can quickly turn into synchronized stop-loss and rapid deleveraging. Quality strategies, on the other hand, focus on corporate financial resilience and their 90-day correlation with momentum has dropped to around -0.63, making them more like a factor hedge against crowded AI trades: not a denial of the long-term prospects of AI, but a preference for companies that can self-finance, generate sustained profits, and withstand valuation compression during a shift from narrative pricing to cash flow validation in the market. Data from Wall Street banks shows that in the past two months, buying quality stocks has been the top-performing investment strategy and style index, gaining 3.8%, according to Barclays Plc. The quality factor combination compiled by Barclays rose by 1.9% on Thursday, a day when chip manufacturers saw a sell-off, leading to a 1.6% decline in the Nasdaq 100 index. As momentum falters, cash flow accelerates: AI trading retreat brings forth "balance sheet safe haven" Last year, Wall Street institutional investors obsessed with AI theme trading largely abandoned fundamentally sound, undervalued companies with ample cash flow, resulting in a valuation metric near historical lows, according to data compiled by Barclays. Now, as concerns about overcrowded AI positions and worries about the Federal Reserve's interest rate path intensify, this investment style is showing signs of revival. Recently, the Korean stock market, often referred to as a "bellwether for AI computing power," has frequently experienced both upward and downward trading halts. The Philadelphia Semiconductor Index in the US has plummeted nearly 19% from its June high, edging closely to a technical bear market, coupled with extreme sell-offs in global AI computing power theme stocks and semiconductor sectors due to overly crowded and highly leveraged long positions, resulting in a market sentiment shift towards strong fundamental cyclicals and defensive stocks with robust balance sheets and cash flows, and whose gains this year have lagged behind those of popular tech stocks. Barclays Global Stock Tactical Strategy Director Alex Ortonman stated that momentum trades that investors have been focusing on have benefited from positive factors such as AI capital expenditures and loose monetary policies, but "many of these drivers are starting to fade." If the investment cycle weakens, these high-quality stocks based on "quantitative safety trading" could become a "safe haven in the storm." Despite being one of the strongest-performing sectors on Wall Street this year, the US semiconductor sector saw a sharp decline of 4.3% on Thursday, expanding its fall from the record high in June to 19%, with concerns about massive AI investments and uncertainties about investment returns potentially supporting high valuations. As shown in the above figure, as global funds flow into safe assets to sell the AI theme, quality factors outperform momentum factors. "Quality" is a term used on Wall Street to refer to companies with strong balance sheets, high return on equity, and sustained profit growth. As momentum trading weakens, quality factors continue to rise, signaling a market reversal, while concerns surrounding AI are challenging existing investment strategies. In fact, according to a measure, the quality factor has become a form of anti-momentum trading. The 90-day inverse correlation between the quality factor and the momentum factor has reached around -0.63, one of the highest levels in Barclays' historical records. A reading of -1 indicates completely opposite trends between the two investment portfolios. Barclays' quality factor combination mainly includes stocks like Daikin Industries, Williams-Sonoma, Kohl's, Fastenal, Seagate Technology, Planet Fitness, Morgan Stanley, and Moderna. If historical trends are any indication, this strategy may have more room for growth in the future. Earlier this year, the "quality factor" theme suffered a setback, bringing its enterprise value to sales ratio to the lowest 11th percentile of all observed values in the past 20 years. According to a Barclays summary statistic, when this valuation multiple has dropped to such low levels in the past, there has been a 92% chance of a rise over the following six months. According to Ortonman from Barclays, "investors typically want to buy high-quality stocks, so this style has historically carried a valuation premiumbut that's not the case now. This situation is very rare." Historically, high-quality stocks tend to perform better when economic growth slows and financial conditions tighten. Barclays strategists believe that as Federal Reserve policymakers increasingly diverge in their future policy directions and the economy becomes more dependent on AI computing power infrastructure spending, the likelihood of this scenario is increasing. Ortonman points out that the ongoing rotation of funds into high-quality stocks does not necessarily mean investors are bearish on the US stock market. Since 2004, there have been over 45% instances of the quality factor rising alongside the S&P 500 index. "Quality factor cycles often indicate that investors are transitioning towards the later stages of the economic cycle, rather than signaling the end of a bull market." From the semiconductor deleveraging frenzy in Seoul to the Wall Street quant safety harbor, stocks with strong balance sheets are poised to take the deserving spotlight in the stock market The core of this current global stock market volatility is not the sudden collapse of AI industry fundamentals but rather the shift in market structure driven by high momentum, high concentration, and high leverage trading reversing course. South Korea has become the epicenter of this shift, not because it was the first to prove the failure of AI profitability logic, but because it was the first to expose the fragility of the market microstructure after price momentum and leveraged leverage outpaced profit realization speeds. This pressure is spreading through a chain starting from "Korean memory chips - Asian tech stocks - American semiconductor and high-beta momentum combinations." The path dependence of leveraged ETFs means that even if the target index first falls by 10% and then rises by 11.1% to return to the original level, triple leveraged products will incur a net loss of about 7% due to compounding erosion; hence, volatility itself acts as a passive deleveraging mechanism. South Korea's single-stock leveraged ETFs are still experiencing declines due to daily rebalancing, while margin buying is reaching extreme levels; JPMorgan believes that the largest-scale stock market globallyUS stock marketis undergoing a nearly identical journey to South Korea, indicating that the normalization of leveraged ETFs, retail call options, and margin accounts might take about three months of volatility before returning to the pre-April state. JPMorgan's calculation strategy shows that the scale of leveraged ETF assets for memory chips has shrunk by about 34% from its peak, while all stock leveraged ETFs have decreased by about 13%, but their leverage ratios relative to market capitalization have not yet returned to normal, potentially requiring about three months of high-volatility range trading to return to pre-April levels. Retail call option buying volume reached a cyclical extreme of nearly 14 million contracts, and margin account leverage remains near historical highs seen around mid-2018 and late 2021, indicating that marginal buying pressure in tech stocks is weakening, while the risk of forced liquidation has not completely disappeared. Barclays' mentioned "quant safety trading" resurgence is a mirror image of this structural reversal: funds are shifting from momentum factors that rely on price trends to high-profitability, low leverage, stable cash flow, and strong balance sheet quality factors. It is not simply about increasing holdings in utilities or essential consumer goods, but rather using a systematic long-short combination to go long on financial resilience and short on low-quality assets, to isolate market beta and capture quality premiums. The 90-day correlation between quality and momentum has dropped to around -0.63, indicating that the quality factor is becoming a natural hedge against crowded AI trades; however, this does not mean that the bull market is over, but rather that the market's pricing function is shifting from "who has had the fastest gains" to "who can withstand tightening financing, slowing capital expenditure, and continue to generate free cash flow." Michael Wilson, a top strategist from Morgan Stanley, also emphasizes that the AI cycle is not over, but the breadth and crowding of upward revisions to semiconductor profits are reaching extremes, and investors are now demanding continued acceleration of capital expenditure returns rather than being satisfied with strong absolute performance. In terms of investment strategy, it is advisable to temporarily reduce exposure to single-stock leveraged ETFs, short-term call options, and highly crowded AI hardware positions, and instead shift towards low leverage, high free cash flow, profit growth expectations, and sectors like finance, industry, healthcare, and consumer goods that have significantly lagged this year.