Against the backdrop of AI's rapid integration into financial markets, especially in enhancing information processing for sustainable finance, this paper explores a key question: "How do AI-driven intelligent investment research systems influence the reaction speed of stock price information?" Employing a literature-based analytical approach, we synthesize insights from studies on the impact of AI on information transmission, algorithmic processing, predictive accuracy, and stock price efficiency. The results show that AI accelerates the speed of reacting to stock price information by improving ESG data collection, enabling the analysis of complex ESG-return relationships, and boosting predictive precision. Theoretically, this study integrates fragmented research to clarify the role of AI in market efficiency; practically, it guides investors, regulators, and sustainable finance practitioners in leveraging AI for timely and value-aligned decisions.

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The core tension in the transnational expansion of social media platforms lies between scale efficiency and local legitimacy. Taking TikTok as its primary case, with RedNote (Xiaohongshu) and WeChat as comparisons, this paper decomposes overseas expansion strategy into four pillars: mode of entry, algorithmic distribution, the monetization loop, and governance architecture. It traces how ByteDance bought out the cold-start cost of network effects through the acquisition of Musical.ly, externalized content production to local creators so that the cultural discount never arose, extended an advertising–live-streaming–commerce loop at speeds that varied by market, and finally exchanged equity and governance rights for continued market access through the TikTok US data security joint venture formed in January 2026. The two comparative cases sharpen the argument from opposite directions: RedNote acquired scale by accident in the "TikTok refugees" episode of 2025 but could not consolidate it without institutional preparation, while WeChat, lacking a distribution mechanism independent of the social graph, has been unable to cross network barriers already occupied abroad. The paper argues that expansion follows two curves separating by stage. In the early phase algorithmic and product capability permit low-cost replication, the further expansion proceeds, the higher the legitimacy costs imposed by compliance, data governance and geopolitics, and the deeper institutional pressure reaches from operations into ownership itself. What ultimately sets the ceiling is not product capability but institutional adaptability. On this basis the paper draws out four strategic patterns and offers recommendations at two levels: for platform firms, and for policy and industry.

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The rapid expansion of ESG (Environment, Society and Governance) investment has placed ESG ratings at the core of capital market decisions. Nevertheless, the significant differences among various rating agencies have drawn widespread attention to the reliability of ESG information and its impact on market efficiency. To this end, this paper establishes a comprehensive analytical framework to assess the impact of ESG rating discrepancies on the information of the capital market, with stock price synchronicity serving as the measurement indicator. Based on a review of the relevant literature, it integrates studies on information asymmetry, limited investor attention, and analyst information intermediation. On this basis, it summarizes three main transmission paths through which ESG rating discrepancies impact information efficiency: heightened information asymmetry, scattered investor attention, and reduced analyst prediction quality. The results suggest that, taking into account the institutional features of China's capital market, including the dominance of retail investors, relatively weak information disclosure, and the strong policy-driven ESG, the aforementioned effects may be significantly amplified in the Chinese context.

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With the rapid growth of short-video live streaming, algorithmic recommendation has become the core driver of traffic distribution on Douyin. Many brands now use entertaining livestream content to reduce the stiff, promotional feel of marketing and attract viewers. This paper focuses on Douyin livestreaming, employing literature review and case analysis as its primary methods. It mainly explores how recommendation algorithms affect brand content dissemination and analyzes effective strategies for brand entertainment communication. The results show that algorithms enable accurate audience matching via user tags and behavioral data, while entertaining livestream content tends to sustain traffic advantages. Nevertheless, problems such as content homogenization still exist. Brands need to combine algorithm rules to innovate entertainment forms so as to achieve better communication and marketing effects. This research enriches the theoretical framework for algorithm-driven brand livestream communication in China's short-video ecosystem and offers actionable suggestions for physical-goods brands to implement long-term entertainment livestream marketing on Douyin.

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