In this paper we use the data from “paipaidai”, an online peer-to-peer lending platform in China, to testify whether mood affects the efficiency of credit approval by individual. Refering to the studies in Psychology and Financial Economics, we employ season, temperature and weather as mood proxies, and crotrol the variables related to the quality of loan to study the credit approval behavior under different mood condition. The results suggest that the efficiency of credit approval is significantly correlated with mood—positive mood would improve the efficiency, while negative mood would reduce it. Specifically, loan examined under better mood condition (e.g. spring, comfortable temperature, and sunny days) has significantly higher probability of approval, but lower probability to default if approved; and that examined under lower mood condition shows lower probability of approval and higher probability to default if approved. This effect of mood is even stronger when a loan application to judge is more complex, atypical, or unusual. Moreover, investor sentiment, denoted by closed-end fund premiums, has the same effect on credit approval as well.