Deeply Implementing the "Artificial Intelligence+" Initiative; Hwabao WP China Science And Technology Innovation Board Artificial Intelligence ETF (589520) Rises 3.45% in Morning Trading
NewTimeSpace (newtimespace.com) News: As of 10:06 AM on June 30, 2026, the Hwabao WP China Science And Technology Innovation Board Artificial Intelligence ETF (589520) rose by 3.45%, with its latest price quoting at RMB 0.78. Looking at a longer time horizon, as of June 29, 2026, theHwabao WP China Science And Technology Innovation Board Artificial Intelligence ETF has achieved a cumulative increase of 4.14% over the past week.
Data shows that the fund shares of theHwabao WP China Science And Technology Innovation Board Artificial Intelligence ETF grew by 417 million units over the past year, achieving significant growth. As of June 29, the net asset value (NAV) of the Hwabao WP China Science And Technology Innovation Board Artificial Intelligence ETF has risen by 31.77% over the past three months.
On the news front, on June 29, 2026, the executive meeting of the State Council pointed out that it is necessary to deeply implement the "Artificial Intelligence+" initiative, leverage China's advantages such as a complete industrial system and rich application scenarios, and accelerate the large-scale commercial application of intelligent products and services.
Kaiyuan Securities stated that the performance of domestic models is further advancing toward, or even surpassing, the world's top levels. The virtuous cycle of "large-scale financing + model performance leaps + high token/ARR growth + valuation surges" among leading model vendors will continue. While generating high revenue growth for themselves, it may continue to drive robust demand in upstream and downstream computing power hardware, vertical applications, and computing power leasing. In addition, as the dense release window for semi-annual earnings previews approaches, potential high-growth directions such as large models and computing power leasing—which may benefit from high token usage growth—are worthy of attention. It is recommended to align with semi-annual earnings expectations and position around core AI directions.
Hong Kong-listed Large Model Concept Stocks:
KNOWLEDGE ATLAS (02513.HK)
Knowledge Atlas Technology Joint Stock Company Limited (02513.HK; commercially known as Zhipu AI) is a leading player in China's independent large language model (LLM) sector (recognized for its capabilities and market position by Frost & Sullivan). The company has released its next-generation flagship model, GLM-5, achieving open-source State-of-the-Art (SOTA) performance in coding and agentic capabilities. It has also open-sourced the multimodal image generation model GLM-Image in collaboration with Huawei. Focusing on novel model architecture design, generalized reinforcement learning paradigms, and autonomous model evolution, its business layout closely aligns with the trend of enterprise-level AI productivity transformation.
MINIMAX-W (00100.HK)
A top-tier player dedicated to omni-modal Artificial General Intelligence (AGI). The company continuously iterates its video generation model matrix and has partnered with mainstream domestic and overseas chip manufacturers and inference platforms to complete underlying adaptations. The total monthly active users (MAU) of its B2C and B2B LLM applications, along with multimodal API call volumes, maintain rapid growth, further broadening its computing power ecological layout.
MININGLAMP-W (02718.HK)
As an LLM concept stock, the company fully underpins its Agentic AI positioning with its self-developed edge models—Mano, Cito, and Mano-P—alongside the Cider inference framework. Its business architecture utilizes the DeepMiner LLM as the underlying engine and Octo as the collaborative hub, uniformly delivering various AI products and industry solutions in the form of Agentic Services. Mininglamp pursues a differentiated technological route, bypassing the parameter expansion of general models in favor of a "Scaling Out" approach that coordinates multiple specialized small models. Its core moats do not rely on parameter scale, but rather on granular scenario data, specialized models, and continuous learning. This enables it to achieve a level of precision in vertical scenarios that general models cannot match, as it remains committed to building an open-source, privately deployable, and white-box auditable Private AI infrastructure.
Risk Warning: The above content is a compilation of recent public research reports and market information and does not constitute any investment advice. The industrial implementation of large models is affected by multiple factors such as technological evolution, market competition, and policy changes, and contains uncertainties. Investment involves risks, and caution should be exercised when entering the market.
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