Fintech Firm Backed by Chinese Movie Stars Is Planning U.S. IPOBy
QuantGroup asks banks to pitch for role on U.S. share sale
Investors in Beijing-based firm include Fosun, Guosen
QuantGroup, a financial technology company backed by Chinese movie stars, is planning a U.S. initial public offering that could raise about $200 million, according to people with knowledge of the matter.
The Beijing-based firm has asked banks to pitch for a role on the potential offering, the people said, asking not to be identified because the information is private. The deal is at an early stage, and details are yet to be finalized, the people said.
QuantGroup joins other Chinese fintech companies in seeking funds in the U.S. equity market as consumers in the world’s most populous nation increasingly turn to non-traditional lenders. Fenqile, a Chinese online shopping mall that lets buyers pay in installments, picked banks to work on a planned IPO that could raise about $600 million, people with knowledge of the matter said in March.
SmartFinance, a Chinese internet loans business that judges borrowers on factors including how often they charge their phones, has also consulted banks about a possible U.S. listing that could happen as soon as this year. They would follow China Rapid Finance Ltd., which has risen 31 percent in New York trading since its $69 million IPO last month.
A representative for QuantGroup didn’t immediately respond to requests for comment.
Backers of QuantGroup include Star VC, the investment firm started by a group of Chinese celebrities. Li Bingbing, an actress who appeared in “Transformers: Age of Extinction,” and “Ip Man 2” actor Huang Xiaoming are among partners at Star VC, according to the website of fellow portfolio company Handu.com. Other QuantGroup investors include Fosun International Ltd. and Guosen Securities Co.
Any deal would add to the $1.9 billion in U.S. IPOs from Chinese companies in the past 12 months, according to data compiled by Bloomberg. QuantGroup operates xyqb.com, which generates and estimates credit ratings using user-provided information, internet and traditional data.