CSOP Hang Seng HK-US Tech ETF (03442.HK) Rises 1.13% Intraday as Nvidia's Huang Expects Chip Sales to Double Next Year

NewTimeSpace News: As of 14:02 on September 22, CSOP Hang Seng HK-US Tech ETF (03442.HK) rose 1.13% to a latest price of HK$8.045, with an opening price of HK$8.035, a high of HK$8.115, a low of HK$8.035, and an intraday amplitude of 1.01%. The ETF traded 264,500 shares with a turnover of HK$2.1366 million, an average price of HK$8.078, a premium rate of -0.61%, and a turnover rate of 0.30%. Its current net asset value per unit is HK$8.004, and its assets under management stand at HK$696.4 million.(Note on scope: the net asset value per unit, assets under management and premium rate are as of September 21, 2026.)

On the news front, Nvidia chief executive Jensen Huang said at an artificial intelligence event in the United Kingdom on September 17 that he expects the company to sell twice as many chips next year as this year, noting that the bottleneck is not demand but Nvidia's ability to produce the chips. Nvidia is one of the eight US constituents of the Hang Seng HK-US Tech Index.

CSOP Hang Seng HK-US Tech ETF (03442.HK) closely tracks the Hang Seng HK-US Tech Index (net total return version). The index reflects the performance of top technology companies listed in Hong Kong or the United States; its constituents comprise the Hang Seng TECH Index constituents eligible under Southbound Stock Connect, plus eight companies listed on NASDAQ — Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, SpaceX and Tesla. Hong Kong-listed constituents are freefloat-adjusted market-capitalisation weighted while the eight US constituents are equally weighted, with weight caps of 60% and 40% for SEHK-listed and NASDAQ-listed securities respectively, and a minimum of 30 constituents.

CMB International believes that AI has become an important revenue and earnings growth driver for leading US technology companies; under the AI-driven cycle, capital expenditure is no longer used merely to ease short-term supply constraints but is being deployed ahead of demand for the computing power, data centres and network infrastructure required for AI training and inference.

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