提升A股配置 基金“专业买手”布局跨年行情

Group 1 - The core viewpoint is that despite increased volatility in the A-share market in November, fund advisors are increasing their allocation to A-share assets, indicating a preparation for the year-end market rally [1] - In November, 131 fund advisor portfolios adjusted their allocations, with A-share assets receiving the largest increase compared to other asset types, as many fund managers begin to position for the year-end market trend [1] - The "year-end market rally" typically occurs from November to March, driven by positive expectations for policies, economy, and earnings for the following year, with historical data suggesting a high probability of such rallies [1] Group 2 - The Yingmi Fund's strategy for December indicates that the market is preparing for a spring rally in 2026, with a neutral short-term allocation value for domestic stocks but a potential for medium to long-term recovery [2] - Industry consensus suggests that despite short-term volatility in A-shares, declines present better buying opportunities, with optimism regarding overseas risk appetite and domestic liquidity expectations [2] Group 3 - Fund advisors are accelerating their adjustment pace, with cyclical industries such as coal, electric equipment, basic chemicals, and food and beverage receiving increased allocations in November, while some technology assets saw reduced allocations [3] - In December, fund advisors are refocusing on technology growth assets, anticipating early market speculation on improving economic conditions for the coming year [3] - Specific fund portfolios, such as Wanji Fund's "Wanji Extraordinary New Quality Drive," increased their mixed fund allocation from 54.6% to 64.1%, optimizing holdings in AI computing and innovative pharmaceuticals [3] Group 4 - Recent positive signals in the broader technology sector include improvements in lithium battery supply and sustained high demand in the optical module industry, which are areas of mid-term focus [4] - However, short-term trading in popular areas like AI computing chips and energy storage remains crowded, requiring time for market digestion, while lower-crowded areas like AI applications and robotics lack incremental funding and fundamental catalysts [4]