This master thesis examines the effect of ex-ante uncertainty about the intrinsic value of US FinTech IPOs stock on the underpricing level. Baron (1982) Asymmetric information model and Beaty and Ritter (1986) the ex-ante uncertainty model were tested using regression analysis by selecting different proxies of ex-ante uncertainty (Firm Age and Market capitalization, IPO proceed and Number of uses of proceed, Venture backing and Underwriter reputation). The Data set consists of 62 US FinTech IPOs during the period from January, 2005 to July, 2017. The underpricing level of US FinTech IPOs was 20.46%. The market capitalization, venture backing and the number of uses of proceed were found to be significant in determining the level of underpricing. The thesis also concludes that Baron (1982) and Beaty and Ritter (1986) models don’t hold for the FinTech IPOs during the period from January, 2005 to July, 2017.