Corporate technological patent and financing accessibility - An empirical analysis based on startups - XU, Tao (Institutional Knowledge at Singapore Management University, 2024)
Technological innovation is not only central to competition among firms but also a crucial driver of economic growth. The process of corporate technological innovation often demands substantial financial support. However, due to information asymmetry and capital constraints, tech startups encounter numerous obstacles in sustaining innovation. Venture capital is instrumental in fostering continuous corporate innovation, providing essential financial support for innovative activities, and playing a key role in achieving core technological breakthroughs. As a significant manifestation of corporate innovative technology, patents can mitigate the information asymmetry between startups and investors by creating a positive patent signal from diverse perspectives. Therefore, investigating the influence of different types of patent signals on the financing accessibility of tech startups holds substantial practical significance. This exploration not only has deep implications for enhancing the integration of technology and finance but also supports the development of small and medium-sized technology enterprises. This paper compiles a comprehensive database of financing events for tech startups specializing in advanced manufacturing and healthcare, integrating a complete sample of patent data for these firms. The study collects 28,610 financing events and 1,897,517 patent records from January 1992 to March 2024. It constructs patent signals based on quantity, quality, legal status, and business relevance from the perspectives of the company's independence and its associations. Subsequently, we develop regression models to examine the effects of these variables on tech startups’ financing accessibility. The moderating role of venture capital’s reputation and focus on the influence of patent signals is further analyzed. Additionally, this paper investigates the variations in the impact of patent signals on financing accessibility across companies with different technological attributes and financing stages. Finally, the paper employs a survival analysis model to address potential endogeneity issues by assessing the impact of patent signals on the likelihood of securing financing in subsequent rounds. The findings of this study are as follows: (1) From an independent perspective, the quantity, quality, and legal signals generally aid startups in securing larger financing volumes, with patent quantity and quality signals having a more pronounced impact on financing volume. In contrast, the business and legal signals of patents exert a comparatively smaller influence on the cumulative number of investment rounds than on financing volume. (2) From an associative perspective, the business signal significantly enhances the likelihood of startups obtaining financing, surpassing the effect of independent company-level patent signals. (3) The analysis of moderating mechanisms reveals that the reputation of venture capitals notably diminishes the positive impact of patent signals on financing accessibility, while limited attention from investors significantly bolsters the effect of technological similarity signals on financing accessibility. (4) Heterogeneity tests indicate that different technological attributes and financing stages of companies influence the impact of patent signals on financing accessibility, although the variations in impact magnitude are minimal. (5) The survival analysis in the robustness tests demonstrates that the influence of various patent signals on financing accessibility remains consistent even after addressing endogeneity concerns. The contributions of this paper are threefold: Firstly, at the theoretical level, it expands the study of the relationship between patent signals and financing accessibility across multiple dimensions such as quantity, quality, business, legal, and technological similarity, refining the application of signaling theory in the venture capital domain. Secondly, from a methodological standpoint, this paper employs advanced deep learning algorithms and textual analysis to construct a corporate-patent-technology topic graph. Utilizing patent text data, this paper innovatively develops a technological similarity index among startups and extends the measurement of technological association and similarity indicators into the Chinese startup financing market. Lastly, on the practical front, this paper provides strategic decision-making references for R&D investment and patent deployment for tech startups. It offers insights for government policy on patent information management and disclosure and promotes venture capital involvement in tech startups, suggesting avenues to accelerate the integration of technology and finance.