BREAKING LIVE All breaking →
News

Hang Seng ends lower before Hong Kong inflation data

by UCapital MediaUCapital News newsroom1 min read
Hang Seng ends lower before Hong Kong inflation data

The Hang Seng dropped 201 points or 0.8% to close at 26,344 on Monday, reversing a flat finish in the prior session as U.S. futures fell sharply ahead of Fed speeches and a key U.S. inflation report this week.


Caution also grew ahead of Hong Kong’s August inflation data due later today, while the city braced for one of its strongest super typhoons in years.


Hong Kong International Airport will suspend passenger flights for 36 hours from Tuesday evening, with the Airport Authority confirming preparations for the storm, named Ragasa. Losses were partly cushioned by the PBoC’s decision to keep key lending rates at record lows for a fourth month in September, in line with expectations.


Property stocks led declines, down over 1%, followed by financials, consumers, and tech. BYD Co. slipped 2.2% after filings showed Berkshire Hathaway had fully exited its 17-year stake.


Other notable losers included Citic (-6.3%), SITC Intl. (-5.9%), Techtronic Inds. (-4.0%), and Kuaishou Tech (-2.2%).

hongkonghangseng
DISCLOSURE

UCapital Asset Management LLP or group companies may have commercial relationships with the companies mentioned. This content is not financial advice.

✦ Ask the AI about this article

Sign up for free to ask the AI about this article.

Analyse the markets with UCapital’s AI agents

Multiples, financials, sentiment and risks in 60 seconds, with live data.

Open Markets →

AI use policy. UCapital AI supports the newsroom with news analysis, summaries, market reactions, translations and the generation of cards and charts. AI-generated or AI-assisted content is labelled in the byline (AI Desk, a named AI profile marked «AI», or joint byline) and is reviewed by editors before publication. News received via API feeds from third-party providers is attributed to the original source. AI can make mistakes: report any inaccuracies to the newsroom. Users’ personal data is not used to train the models.