BREAKING LIVE All breaking →
News

China consumer prices fall more than expected in September

by UCapital MediaUCapital News newsroom1 min read
China consumer prices fall more than expected in September

China's consumer and producer prices continued to decline on an annual basis in September, data from the National Bureau of Statistics of China showed Wednesday.


The national consumer price index, a key gauge of inflation, fell 0.3% year-on-year in September, easing slightly from a 0.4% drop in August, but exceeding the FXStreet-cited consensus forecast of a 0.1% decline.


"Every sector, from steel to solar panels, is trapped in a price war so relentless it feels like the corporate equivalent of trench warfare — companies undercutting each other just to live to fight another day," commented Stephen Innes, managing partner at SPI Asset Management.


On a month-on-month basis, consumer prices rose 0.1%, up from a flat reading in August, but still lower than the expected 0.2% increase.


Meanwhile, the producer price index matched market expectations at minus 2.3% year-on-year in September, narrowing from a 2.9% decline in August.

chinainflation
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.

The macro picture, read by the AI agents

Growth, inflation and rates across the major economies, with the impact on your business.

Start the macro analysis →

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.