Exploring the factors that impact accelerator-based startup funding

For this study, we use signaling theory to explain how startups effectively signal to receive funding. Based on a sample of 1,554 startups collected from the websites of Y Combinator, Crunchbase, and LinkedIn we demonstrate that a startup signifies its potential risk level through cues, such as the industry risk level, founders’ educational background, startup location, and accelerator affiliation. We examine how investors perceive risk differently based on their risk-seeking or risk-averse tendencies. Specifically, we found that, with investors’ risk-seeking attitudes, there is a positive association between the investment risk level of the startup industry and the total funding amount received by a startup. Such positive association is stronger for startups that are located far from the accelerator. Conversely, due to investors’ risk-averse attitudes, there is a positive association between the founder’s education level (which theoretically mitigates perceived risk) and the total funding amount. This association is stronger for startups launched by accelerators other than Y Combinator. In summary, our findings suggest that investors’ interpretation of signals from startups varies based on whether they exhibit risk-seeking or risk-averse tendencies. This variance significantly influences the funding amounts secured by these startups. This paper, therefore, sheds light on the opaque process of startup funding and emphasizes risk interpretation by investors. It presents guidelines for startups to either highlight or mitigate different aspects based on the preferences of various investors. It also advises investors to be mindful of their own risk propensities when selecting startups for investment.