CITIC Securities: The AI Momentum Pullback Is Not the Dividing Line Between Bull and Bear Markets

1) A sector trend pullback does not equal a turn from bull to bear. In the A-share market, there has been a fairly widespread notion that "a sector trend pullback = a turn from bull to bear," the core of which is that past sector trends (industry and finance in 2006–2007, real estate and infrastructure in 2009, mobile internet in 2013–2015, the energy transition in 2019–2021, etc.) brought broad pull effects to other sectors of the economy. Yet the progress of the AI industry in this round has, at least in the short term, produced considerable crowding-out effects on other sectors of the economy, including driving a surge in materials and resource prices, pushing bond market supply and demand into tension and long-term bond yields sharply higher, crowding out employment to some extent, and producing an extremely unbalanced growth in the wealth effect, with a degree of market divergence rarely seen in history, and even the emergence of a so-called opposition between the "silicon-based world" and the "carbon-based world." This also means that the past experience that "the end of a sector trend rally = a turn from bull to bear" may not apply to this round of AI-hardware-led market; many non-AI sectors had long since entered a downtrend, and AI was merely the last to "catch down."

2) In the short term, AI has produced negative externalities for other sectors; if AI momentum fades, some non-AI sectors may instead see a turnaround. The negative externalities that have already become explicit include at least three types: first, sharp price increases in basic daily-life materials such as electronic components and electricity, which raise production and living costs and suppress demand in other areas, with consumer electronics a typical example; second, investment and construction in computing-power infrastructure driving huge debt financing by the private sector, pushing up global risk-free interest rates and hitting all demand that is sensitive to higher rates; third, crowding out employment, especially affecting young people's employment—for example, according to the latest September data from the Stanford and ADP employment databases, employment among 22–25-year-olds in the occupational group with the highest share of automated AI use fell 10.9% compared with November 2022, while the lowest group rose 7.8% over the same period. These factors have triggered sharp divergence between the real economy and the market, but conversely, if the pace of AI investment slows somewhat in the future, non-AI sectors may also not face such a negative macroeconomic environment—for example, risk-free rates may fall sharply. In fact, judging from current US Treasury pricing, the direction of 10-year US Treasury yields is highly correlated with the share-price trend of North American AI hardware stocks; if there is a sustained decline in the future, it may at least boost demand in overseas non-AI sectors.

The valuation contraction process has basically been completed this year, and market breadth is expected to gradually expand

1) The second-derivative correction in AI and the valuation contraction in non-AI are both expected to be basically completed this year. AI has completed its first wave of "second-derivative" correction. Similar to the new energy market adjustment from the end of 2021 to the end of April 2022, this round of AI likewise saw valuations revised down in advance while industry prosperity was still present and growth expectations were weakening. From the perspective of valuation contraction, the valuation compression in this round of AI hardware has been comparable to the first round of adjustment in new energy in 2022. According to Wind data, since July 1, the aggregate-method PE (TTM) of the electronics and communications sectors has compressed from 74.0x to 42.4x, a decline of 42.8%; during the first round of new energy adjustment in 2022 (December 17, 2021–April 26, 2022), the market-cap-weighted PE of the power equipment and new energy sector compressed from 51.2x to 27.8x, a decline of 45.7%. From a global comparison perspective, A-share non-AI sectors are in a global valuation trough. We used baskets of AI and non-AI leaders in the Chinese, US, Japanese, and South Korean markets, calculated PE (TTM) using the aggregate method, and made cross-market comparisons. As of October 22, A-shares were the only market where the AI valuation premium relative to non-AI remained above its historical median: the AI/non-AI PE multiple was 4.4x, at the 82nd percentile since 2016, while the same multiple in the US, Japan, and South Korea had fallen to the 1st, 17th, and 24th percentiles, respectively. The PE of US and Japanese non-AI non-financial leaders was 33.5x and 23.0x, respectively, at the 98th and 95th percentiles; A-share non-AI non-financial leaders were only 15.6x, at the 21st percentile of history, with consumer, manufacturing, and pharmaceutical leaders at only the 12th, 1st, and 3rd percentiles, respectively. Of course, the low valuations stem from weak domestic demand and the market's potential concerns about external demand in non-AI areas (trade frictions, high overseas interest rates, high oil prices, etc.), and there is reasonableness to that. But from another angle, the pessimism that has led to these low valuations largely stems from the huge disconnect between this year's reality and the market's expectations at the start of the year. Investors could hardly have anticipated that in the first year of the "15th Five-Year Plan" the broad fiscal deficit would unexpectedly swing to a sharp negative growth, that the market would shift from expecting Fed rate cuts to expecting rate hikes, that 10-year US Treasury yields would hit a new high since 2007, that China-EU trade frictions would suddenly heat up, and that a US-Iran war would break out and last nearly a year. This gap is the reason why many non-AI sectors saw sharp valuation contraction this year despite impressive earnings growth. At present, at least the market has fully recognized these negative macro factors; whether in overseas interest rates or domestic policy, any marginal change that exceeds expectations may help alter the currently more pessimistic valuation system of A-shares relative to global markets.

2) K-shaped divergence is gradually converging, and market breadth is expected to broaden gradually. On the AI side, anxiety about the growth of commercialization space will continue to constrain the absolute annual scale of AI investment, while deepening the market's vigilance toward the future "first derivative" inflection point. The siphoning effect of AI on non-AI sectors is unlikely to recur. At the same time, given that mainstream institutional funds remain overweight in the TMT sector, the process of position rebalancing during a market rebound will also constrain the elasticity of the AI sector. We have observed that during this week's process of the market stabilizing after declines, the first AI new technology to rebound was the robotics sector, where chips have been quite thoroughly cleared out and institutional holdings are low, rather than sectors related to computing infrastructure. For non-AI sectors, almost all current negative factors have been recognized by the market (even if they may not be fully priced in). After a round of systematic adjustment, low expectations themselves are a favorable condition. Taking the overseas expansion sector as an example, China-EU trade frictions are already on the table, and annual rule-revision negotiations and bargaining may become routine in the future; companies will ultimately adapt by virtue of their strong competitive advantages. As long as the pace of RMB appreciation slows somewhat next year, exchange losses will decrease year on year, which will bring a recovery in net profit margins. If the Democratic Party controls Congress after the US midterm elections, the Middle East war situation may change positively, and the resulting impact on oil prices may weaken at the margin. Slower growth in AI investment will bring a decline in long-term risk-free rates overseas, weakening the demand-suppressing effect on non-AI industries. Since these negative factors have been systematically recognized by investors this year, any subsequent marginal improvement will also boost non-AI sectors themselves.

Clearing on the funding side takes time; a stable-return strategy represented by PB-ROE may be more advantageous

The process of rapid deterioration on the funding side is likely nearing its end. Under the signal of significantly increased ETF volume this week, active buying has begun to flow cautiously into the market, and the negative loop of deleveraging in margin financing has been interrupted. As of October 21, the margin financing balance in the two markets had fallen to 2.45 trillion yuan, a cumulative decrease of 560 billion yuan from the year's high, a drop of 18.6%, returning to the level of late November last year. The cooling at the trading level is more evident: the proportion of margin financing purchases in the two markets' turnover (MA5, same below) averaged 9.86% in June, fell to 8.99% in July, 8.66% in September, and further fell to 8.01% since October; the reading on October 16 was 7.84%, the lowest since September 2024 and below the roughly 9% level before the "9.24" market rally. The positions of active private funds have likewise returned to low levels. A survey of CITIC Securities channels shows that the positions of sampled active private funds have continued to decline since the week of August 7, falling to 67.5% on October 16, the lowest since March 2024 and 8.8 percentage points below the historical median of 76.3%. Although funding-side indicators have fallen to relatively low levels, thorough clearing and the start of sustained improvement still require time. During this stage, momentum and prosperity strategies will remain weak, market rotation will accelerate, and low-valuation quality strategies represented by PB-ROE will be more advantageous. At present, there are two types of industries that will satisfy such a framework next year: one is varieties with high dividend yields and steady profit growth, such as white goods, textiles and apparel, food and condiments, and thermal power; the other is varieties with the logic of supply-clearing potential, such as oil shipping, express logistics, leading real estate developers, some lithium battery materials, and midstream segments of petrochemicals.

Source: CITIC Securities Research Risk Disclosure and Disclaimer

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