This paper examines through the use of multiple linear regression analysis which factors have significant impacts on the valuation of unicorn startups from the U.S. fintech industry. The dataset created for the purpose of this study is composed of 129 companies and cross-sectional data including both numerical variables as well as categorical. The correlation tests proved that there is a significant positive correlation between the valuation of the company and variables such as funding amount, above-average revenue, number of funding rounds, number of investors and number of employees. The linear regression model made it possible to examine the relationship between valuation and the aforementioned data. The study allowed us to draw meaningful conclusions and the results suggest that (i) funding amount is the most important factor affecting company valuation, (ii) other variables of high significance are the number of investors and activities in sub-sectors such as cryptocurrency and blockchain technologies, and (iii) the number of employees may have a moderate impact on the valuation. We conclude that as for the numerical variables the number of investors and acquired funding amount are the most important factors affecting the post-money valuation of unicorn startup companies from the fintech sector in the U.S. In addition, due to high significance of subsectors such as cryptocurrency and blockchain technologies it was possible to conclude that investors value the companies’ future potential and how involved in developing new technologies and solutions they are, more than the revenues generated by these companies.