With the development of financial technology (referred to as fintech), the risks faced by fintech companies have received increasing attention. This paper uses the Sentence Latent Dirichlet Allocation (Sent-LDA) topic model to comprehensively identify risk factors in the fintech industry based on textual risk factors disclosed in Form 10-K. Furthermore, this paper analyzes the importance of risk factors and the similarities of the risk factors for the whole fintech industry and different fintech sub-sectors from the perspectives of risk factor types and risk factor contents. In the empirical analysis, 53,452 risk factor headings of 34 fintech companies included in the KBW Nasdaq Financial Technology Index (KFTX) over the period 2015–2019 are collected. The empirical results show that 20 risk factors of the fintech industry are identified. However, the important risk factors vary differently among different fintech sub-sectors. For the analysis of risk factor similarity, mean values of similarity of risk factor types and the similarity of risk factor contents both increased from 2015 to 2019, which indicates that the risks faced by fintech companies are becoming increasingly similar. The mean value of similarity of risk factor contents is 42.13%, while the mean value of similarity of risk factor types is 80.93%. Thus, although the types of risk factors faced by different fintech companies are similar, the contents of risk factors disclosed by different companies are still quite different. The comprehensive identification of fintech risk factors lays an important foundation for the further measurement and management of risks in the fintech industry. In the feature, we will further make effective risk estimations of the fintech industry based on the identified fintech risk factors.

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In order to study the impact of company name on investor recognition and company value, this article constructs a set of evaluation system of the company name according to the Chinese way about thinking and Chinese characters from the terseness, smooth, moral and recognition, then I grade the listed companies. This article makes the comprehensive evaluation on behalf of the company name, the number of shareholders, the average number of shares hold by an owner and the institutional investors holding on behalf of the investor recognition, then makes the tobin’s Q and price-to-book on behalf of the company value. Afterwards, this article sets up a fixed effect panel regression model. The empirical research shows that: if the company’s name is concise, easy to pronounce, easy to remember and has a good moral, the company will have the higher investor recognition, and higher valuations.