GTHT Strategy: Q1 Active Funds Boost Allocations to High-Growth Sectors Like AI Computing, Overseas Manufacturing, and Cyclicals, While Cutting Electronics, Nonferrous Metals, and Consumer Durables

Deep News04-24 18:51

Active equity funds reduced their exposure to A-shares and Hong Kong stocks in the first quarter of 2026, while increasing allocations to high-growth sectors such as AI computing, overseas manufacturing, and cyclical industries. Communications, power and new energy, and chemicals were consensus additions, while significant cuts were made to electronics, nonferrous metals, and consumer durables.

The primary characteristic of fund allocation in Q1 was a continuation of the景气投资 strategy, with AI computing, overseas manufacturing, and resources being the consensus areas for increased holdings. The stock allocation of active partial equity funds dropped quarter-on-quarter to 82.9%, and portfolio concentration declined noticeably, with the CR20 decreasing by 4.5 percentage points. In terms of market segments, active funds continued to increase their weighting in the ChiNext and STAR Market boards, while reducing exposure to the main board. Allocation to Hong Kong stocks fell by 1.7% quarter-on-quarter to 13.9%, continuing a downward trend. Stylistically, funds increased allocations to the CSI 1000 and CSI 500 indices while reducing exposure to large-cap blue chips like the CSI 300 and SSE 50. They added to cyclical and stable sectors while cutting growth, consumer, and financial stocks.

Significant adjustments were made to the individual stock holdings within active fund portfolios in Q1, with 7 changes among the top 20 largest holdings. Concentration in specific themes like communications, power and new energy, and nonferrous metals declined noticeably, with more allocations directed towards small and mid-cap stocks. Institutional investors maintained their focus on high-growth sectors, with AI computing, overseas manufacturing, and the petrochemical industry—all showing strong earnings growth—becoming the consensus choices for increased investment.

Regarding sector allocation, active funds primarily increased their exposure to midstream cyclical and manufacturing sectors while reducing consumer and financial holdings, with a tech focus on computing power. In Q1, the main increases were in communications/power and new energy/chemicals/petrochemicals/transportation, while the main decreases were in electronics/nonferrous metals/automobiles/home appliances/media. A detailed quarter-on-quarter analysis of actual allocations reveals: 1) Cyclical and Manufacturing: Midstream cyclical and manufacturing sectors saw significant overall increases. Secondary sectors such as batteries/power grids/automation equipment—which benefit from AI and overseas industrial trends—along with涨价周期 industries like precious metals/agricultural chemicals/shipping ports/refining/coal mining/chemical raw materials, saw the largest increases in allocation. Conversely, industrial metals within the upstream cyclical sector were substantially reduced. 2) A significant divergence emerged within the TMT sector: Core computing power产业链 (communications equipment/semiconductors) continued to see substantial additions, with several communications equipment (cable) stocks entering the top 20 holdings of active funds. However, consumer device and application-side industries saw major reductions, with components/consumer electronics/gaming/software development experiencing the largest cuts. 3) Consumer and financial sectors were reduced: Property consumption chain sectors (white goods/two-wheeled vehicles/baijiu) saw the largest reductions, potentially related to pressure on consumer sector景气度. Within the financial sector, non-bank financials (insurance/securities) were significantly reduced, possibly due to market concerns about the sustainability of their earnings growth in 2026.

Allocation to Hong Kong stocks continued to decline, with tech leaders being reduced. In Q1 2026, active funds further decreased their Hong Kong stock holdings, with the total market value of top holdings falling by RMB 32.88 billion to RMB 262.36 billion quarter-on-quarter. The allocation ratio dropped significantly by 1.7% to 13.9%. Sector-wise, active funds increased their holdings in petrochemical stocks and unique tech assets (communications, innovative drugs) in Q1, while reducing exposure to Hong Kong internet and electronics leaders.

A divergence is evident in the allocation to emerging industries, with potential for increased allocation to AI materials/infrastructure/application segments. In Q1 2026, active funds maintained high allocations to the two main themes of computing power and capital goods going overseas. However, the overweight ratios for material and application segments within the broader AI industry chain remained relatively low. This divergence within the AI chain, favoring computing power over materials and applications, has been apparent since Q4 2025. Drawing parallels with the new energy sector boom from 2019-2021, in the early stages of an industry's development, the main产业链 often sees earnings materialize first. Consequently, allocation increases and price rallies in the main sector typically outpace those in non-core segments like materials. However, in the middle and later stages of industry growth, as the景气 trend spreads throughout the chain, profitability in material segments can accelerate rapidly. Their allocation ratios may then rise in sync with the main产业链, and stock price gains could catch up. The current stage of the AI industry shows similarities to the initial breakout phase of the new energy industry around 2020. In terms of stock performance since 2024, computing power sectors, represented by communications equipment, have consistently led gains within the AI theme, while material segments have lagged. We believe that as the AI景气 wave spreads upstream and downstream, there is significant room for increasing allocations to the currently underweight AI materials, infrastructure, and application segments.

Risks include the limited guidance historical data provides for the future, potential biases in data statistical methodologies, and data estimation errors.

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