Alibaba Shares Fall as Chip Control Worries Mix With Analyst Upgrades
· 5 sources
Alibaba dropped amid worries over chip controls and an Nvidia-Megaspeed scandal, while DZ BANK upgraded the stock and earnings are set for Nov. 19.
What happened
Alibaba shares came under pressure this week as concerns grew over tighter chip controls, according to several outlets. FXLeaders reports a 7.1% drop tied to a scandal involving Nvidia and Megaspeed, which it says threatens the supply of AI computing power in China.
Moomoo reports that Alibaba-W (09988) fell more than 4% in the Hong Kong afternoon session, while UBS Group suggested the related impacts may be overstated. Moomoo also notes that DZ BANK AG analyst Markus Leistner upgraded the stock to a buy rating and moved the target price from $135 to $140.
TIKR.com highlights that Alibaba has slumped for a year and says DBS thinks it can double. Investing.com reports that options pricing implies a possible move of 5.7% around the Nov. 19 earnings release.
Context
Alibaba is a major Chinese technology and cloud company, and access to advanced chips is an important input for AI computing infrastructure.
Why it matters
The mix of chip supply concerns, divided analyst views and an upcoming earnings report may keep attention on Alibaba and on Chinese AI computing.
Stocks in this story
Price changes are shown for the trading day of the story and are a coincidence in time, not proof that the news caused them. Figures are ATLAS data from our database.
$124.66Fair range: $69.04 to $222.84The price ($93.90) is 24.7% below that figure.
Chance of a rise in 1 month
49%Probability of closing higher than today within 21 sessions.Normal range: half of the scenarios end between $86.36 and $101.44; 9 in 10, between $74.88 and $116.99.
Fair value, probabilities and earnings for every stock. Free account, no card. Information, not advice.
Information, not advice. This content is informational and is not an investment recommendation or an offer to buy or sell securities. ATLAS estimates come from statistical models using public data, can be wrong, and past performance does not guarantee future results. Disclaimer
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