CITIC SEC: The pullback in AI momentum is not a dividing line between bull and bear markets.

CITIC SEC released a research report stating that the pullback in the AI trend is not the beginning of a bear market, and non-AI sectors may instead face a more favorable environment. The core point is that the AI industry, in the short term, does not exert the broad pulling effect on other sectors of the economy that past industrial trends did; instead, it has generated considerable negative externalities for other economic sectors (surging materials and resource prices, soaring interest rates, and crowding out employment). This also means that the past experience of "the end of an industrial trend rally = transition from bull to bear" may no longer apply, and many non-AI sectors have long since entered a downtrend. At present, whether it is the second-derivative valuation correction of the AI sector or the valuation contraction of non-AI sectors, the main valuation contraction process is expected to be largely completed this year. Looking ahead, after deep adjustments, the probability of differentiation gradually converging and market breadth expanding is higher. The only issue is that the clearing of liquidity conditions takes time. At this stage, stable-return strategies represented by PB-ROE may be more advantageous. The main views of CITIC SEC are as follows: The pullback in the AI trend is not the beginning of a bear market; non-AI sectors may instead face a more favorable environment 1) Industrial trend pullback bull-to-bear transition. In the A-share market, there has been a fairly widespread notion that "industrial trend pullback = bull-to-bear transition," the core reason being that past industrial trends (industry and finance in 20062007, real estate and infrastructure in 2009, mobile internet in 20132015, energy transition in 20192021, etc.) brought broad pulling effects to other economic sectors. However, the progress of the AI industry in this round, at least in the short term, has produced considerable crowding-out effects on other economic sectors, including causing sharp increases in materials and resource prices, driving tight supply-demand conditions in the bond market and a sharp surge in long-term bond yields, crowding out employment to a certain extent, and producing an extremely unbalanced growth in wealth effects. The degree of market differentiation is historically rare, and there has even emerged a so-called opposition between the "silicon-based world" and the "carbon-based world." This also means that the past experience of "the end of an industrial trend rally = bull-to-bear transition" may not apply to this round of AI-hardware-led rally. Many non-AI sectors have long since entered a downtrend, and AI is merely the last to "catch down." 2) AI has generated negative externalities for other sectors in the short term; if AI momentum weakens, 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 livelihood materials such as electronic components and electricity, which raise production and living costs and suppress demand in other areas, typically consumer electronics. Second, the construction and investment in computing power infrastructure drives massive debt financing by the private sector, pushing up global risk-free interest rates and dealing a shock to all interest-rate-sensitive demand. 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 the 2225 age group in the occupation group with the highest share of automated AI use fell by 10.9% compared with November 2022, while the lowest group rose by 7.8% over the same period. These factors have triggered sharp differentiation 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 environmentfor example, risk-free interest rates may fall sharply. In fact, judging from current U.S. Treasury pricing, the direction of 10-year U.S. Treasury yields is highly correlated with the stock 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 of AI and the valuation contraction of non-AI are both expected to be basically completed this year. AI has completed the first wave of "second-derivative" correction. Similar to the new energy rally adjustment from the end of 2021 to the end of April 2022, this round of AI also saw valuations revised down in advance when industry prosperity was still present but 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, 2021April 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 depression. We used baskets of AI and non-AI leaders in the Chinese, U.S., Japanese, and Korean markets, measured PE (TTM) using the aggregate method, and conducted cross-market comparisons. As of October 22, A-shares are the only market where the AI valuation premium relative to non-AI remains above the historical median: the AI/non-AI PE ratio is 4.4x, at the 82nd percentile since 2016, while the same ratio in the U.S., Japan, and Korea has fallen to the 1st, 17th, and 24th percentiles, respectively. The PE of U.S. and Japanese non-AI non-financial leaders is 33.5x and 23.0x, respectively, at the 98th and 95th percentiles; A-share non-AI non-financial leaders are only at 15.6x, at the 21st percentile historically, with consumer, manufacturing, and pharmaceutical leaders at only the 12th, 1st, and 3rd percentiles, respectively. Of course, the low valuation stems from weak domestic demand and the market's potential concerns about external demand in non-AI areas (trade friction, high overseas interest rates, high oil prices, etc.), and is reasonable. But from another perspective, this pessimism that leads to low valuations largely stems from the huge disconnect between this year's reality and the market's expectations at the beginning 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 turn to a sharp negative growth, that the market would shift from expectations of Fed rate cuts to expectations of rate hikes, that 10-year U.S. Treasury yields would hit a new high since 2007, that China-EU trade friction would suddenly heat up, and that a U.S.-Iran war would break out and last for 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 macro negative factors. Whether it is overseas interest rates or domestic policy, any marginal change that exceeds expectations may help change the current A-share valuation system, which is more pessimistic than that of global markets. 2) K-shaped differentiation will gradually converge, and market breadth is expected to gradually expand. 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 future "first-derivative" inflection points. The siphon effect of AI on non-AI sectors is unlikely to recur. At the same time, considering that mainstream institutional funds remain overweight in the TMT sector, the process of position rebalancing during market rebounds will also constrain the elasticity of the AI sector. We have seen that during this week's process of the market stabilizing after declines, the first AI new technology to rebound was the Siasun Robot&Automation sector, where chips have been quite thoroughly cleared and institutional holdings are low, rather than sectors related to computing power infrastructure. For non-AI sectors, almost all current negative factors have been recognized by the market (even if perhaps not fully priced in). After a round of systemic adjustment, low expectations themselves are a favorable condition. Take the overseas expansion sector as an example: China-EU trade friction is already on the table, and annual rule revision negotiations and bargaining may become normalized in the future, but companies will ultimately adapt by relying on 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 U.S. midterm elections, the Middle East war situation may change positively, and the resulting oil price impact may weaken marginally; slower AI investment growth will bring a decline in overseas long-end risk-free interest rates, weakening the suppression of demand in 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. The clearing of liquidity conditions takes time, and stable-return strategies represented by PB-ROE may be more advantageous The process of rapid deterioration in liquidity conditions is likely nearing its end. Under the signal of a clear increase in ETF volume this week, active buying began to cautiously flow into the market, and the negative loop of deleveraging in margin financing was interrupted. As of October 21, the margin financing balance in the two markets had fallen to RMB 2.45 trillion, a cumulative decrease of RMB 560 billion from the intra-year high, a decline of 18.6%, and has returned 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 turnover of the two markets (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, below the level of about 9% before the "9.24" rally. The positions of active private funds have likewise returned to a low level. CITIC SEC channel surveys show that the positions of sampled active private funds have continued to decline since the week of August 7 and had fallen to 67.5% by October 16, the lowest since March 2024 and 8.8 percentage points below the historical median of 76.3%. Although liquidity indicators have fallen to relatively low levels, thorough clearing and the start of sustained improvement still require time. At 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 type is varieties with high dividend yields and steady profit growth, such as white goods, textiles and apparel, food seasoning, and thermal power. The other type 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 petrochemical segments. Risk factors Intensified friction between China and the U.S. in technology, trade, and finance; domestic policy intensity, implementation effects, or economic recovery falling short of expectations; unexpected tightening of macroeconomic liquidity at home and abroad; further escalation of conflicts in Russia-Ukraine, the Middle East, and other regions; and China's real estate inventory digestion falling short of expectations.