BREAKING LIVE All breaking →
News

Yuan hits 15-month high despite weak data

by UCapital MediaUCapital News newsroom1 min read
Yuan hits 15-month high despite weak data

The offshore yuan appreciated to around 7.05 per dollar, hitting its highest level since late September last year despite a raft of disappointing economic data.


November figures showed momentum cooling, with retail sales growth slowing sharply and falling well short of forecasts, underscoring persistent weakness in consumption.


Industrial output also eased slightly from the previous month and missed expectations, while fixed-asset investment contracted more than anticipated, marking its deepest downturn since the pandemic period.


The prolonged property slump remained a major drag, with real estate investment falling at a faster pace and home price declines intensifying across major cities.


However, economists said the deterioration in investment and housing conditions reinforced expectations for additional fiscal and monetary support early next year.


Elsewhere, the yuan found support from a weaker US dollar, after the Fed cut rates and signaled a less hawkish path than anticipated.

yuancny
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.