China Mobile

At the annual general meeting a dividend of HKD 5.27 per share for 2025 was approved, representing a total distribution of HKD 112.15 billion and an increase in the per-share payout of 3.54% as well as a 6.14% rise in the total distribution versus the prior year. The stock shows a 2025 dividend yield of 6.45% (prior year 6.64%) and is traded on the ex-dividend date with a dividend adjustment. FactSet analysts expect a dividend cut to CNY 4.85 for 2026, which their estimates would push the yield to 11.44%. » Mais em finanzen.net

China Mobile is expected to release results for the quarter ended 31.12.2025 on 27.03.2026. Two analysts see an average earnings per share of 1.93 CNY versus 1.29 CNY in the year-ago quarter; revenue is expected to be around 6% higher (one analyst: 264.26 billion CNY vs. 249.30 billion CNY). For the completed fiscal year, 11 analysts on average forecast 6.63 CNY per share (previously 6.45 CNY) and 12 analysts expect annual revenue of about 1,053.41 billion CNY (previously 1,040.76 billion CNY). » Mais em finanzen.net

The FCC is considering removing China Mobile as well as China Telecom and China Unicom from its robocall mitigation database. Authorities are demanding that the companies fix certification problems and are, in the extreme, weighing a ban on US carriers and voice service providers directly accepting calls from those providers. An exclusion would significantly restrict these telecom giants’ connectivity to US networks. » Mais em de.investing.com

For the quarter ended 30 September 2025, China Mobile reported earnings per share (EPS) of 1.44 CNY, unchanged from the prior year. Revenue increased by 2.53% year-on-year to 250.90 billion CNY (prior year: 244.71 billion CNY). » Mais em finanzen.net

0G Labs, in collaboration with China Mobile, has developed a decentralized AI training framework that enables large language models to be trained 10 times faster and at 95% lower cost compared to traditional centralized methods. The new approach, DiLoCoX, allows organizations to train massive models using standard internet connections and distributed infrastructure, reducing reliance on major cloud providers. This innovation could democratize AI development, lower barriers to entry, and reshape the business model for enterprise AI. » Mais em forbes.com